Development of AHPDST Vulnerability Indexing ...

Pure Appl. Geophys. 2017 Springer International Publishing DOI 10.1007/s00024-017-1499-9

Pure and Applied Geophysics

Development of AHPDST Vulnerability Indexing Model for Groundwater Vulnerability Assessment Using Hydrogeophysical Derived Parameters and GIS Application K. A. MOGAJI1 Abstract—Producing a bias-free vulnerability assessment map model is significantly needed for planning a scheme of groundwater quality protection. This study developed a GIS-based AHPDST vulnerability index model for producing groundwater vulnerability model map in the hard rock terrain, Nigeria by exploiting the potentials of analytic hierarchy process (AHP) and Dempster–Shafer theory (DST) data mining models. The acquired borehole and geophysical data in the study area were processed to derive five groundwater vulnerability conditioning factors (GVCFs), namely recharge rate, aquifer transmissivity, hydraulic conductivity, transverse resistance and longitudinal conductance. The produced GVCFs’ thematic maps were multi-criterially analyzed by employing the mechanisms of AHP and DST models to determine the normalized weight (W) parameter for the GVCFs and mass function factors (MFFs) parameter for the GVCFs’ thematic maps’ class boundaries, respectively. Based on the application of the weighted linear average technique, the determined W and MFFs parameters were synthesized to develop groundwater vulnerability potential index (GVPI)-based AHPDST model algorithm. The developed model was applied to establish four GVPI mass/belief function indices. The estimates based on the applied GVPI belief function indices were processed in GIS environment to create prospective groundwater vulnerability potential index maps. The most representative of the resulting vulnerability maps (the GVPIBel map) was considered for producing the groundwater vulnerability potential zones (GVPZ) map for the area. The produced GVPZ map established 48 and 52% of the areal extent to be covered by the lows/moderate and highs vulnerable zones, respectively. The success and the prediction rates of the produced GVPZ map were determined using the relative operating characteristics technique to give 82.3 and 77.7%, respectively. The analyzed results reveal that the developed GVPI-based AHPDST model algorithm is capable of producing efficient groundwater vulnerability potential zones prediction map and characterizing the predicted zones uncertainty via the DST mechanism processes in the area. The produced GVPZ map in this study can be used by decision makers to formulate appropriate groundwater management strategies and the approach may be well opted in other hard rock regions of the world, especially in economically poor nations.

1 Department of Applied Geophysics, Federal University of Technology, P.M.B. 704, Akure, Nigeria. E-mail: [email protected]

Key words: Geoelectric, analytic hierarchy process (AHP), dempster–shafer theory (DST), groundwater vulnerability potential index (GVPI), ROC, GIS.

1. Introduction In environmental studies, outputs from numerous geophysical investigation fields, such as mineralization potential mapping, geotechnical evaluation, archeological prospecting, and hydrogeophysical mapping, have played vital roles in decision-making process (Adeoti et al. 2016; Kayode et al. 2016, Olawuyi et al. 2016; Mogaji and Lim 2016). Precisely, in the field of hydrogeophysics, geophysical methods had effectively aid in determine the soil/rock characteristics, characterization of aquifer properties and assessing boundaries parameters for the process of monitoring the easy with which water flows in the earth’s subsurface lithology (Adeoye-Oladapo et al. 2015). Thus, for the sustainability and optimal exploitation of groundwater resources, the potentials of geophysical techniques have gainfully been employed with appealed results (Mohamed et al. 2013; Mogaji 2016; Kayode et al. 2016). The typical of such relevant geophysical methods widely explored in the field of hydrogeophysics include electromagnetic, magnetic resonance sounding, seismic refraction, ground penetrating radar, seismic reflection, electrical resistivity method (ERM), etc. (France´s et al. 2014; Sundararajan et al. 2004; Gruba and Rieger 2003; Sultan and Santos 2009; Sharma and Barawal 2005; Meju et al. 2002; Ehinola et al. 2006; Jupp and Vozoff 1975; Koefoed 1979). Of all the aforementioned non-invasive geophysical techniques, the ERM is most widely explored in

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Pure Appl. Geophys.

solving hydrogeologic problems viz: the groundwater potentiality zones mapping (Adeoye-Oladapo et al. 2015; Mogaji 2016; Kayode et al. 2016), the groundwater contamination studies (Helmy et al. 2016; Bahkaly et al. 2015), the saline water incursion studies (Chandrasekhar et al. 2014; Zogala et al. 2009), and geothermal explorations (Kumar et al. 2011). This perhaps is due to the unique ERM’s ability to demarcate different geological layers such as top soil, weathered, fractured and bedrock zone that easy the evaluation of groundwater flow mechanism, the groundwater occurrences and its accumulation in the subsurface (Robert et al. 2011; Sharma and Barawal 2005). Besides, the quantitative means of evaluating the nature of those diverse geological layers’ composition and their fluid content using estimated geoelectrical parameters (resistivity and thickness) that are commonly obtained from the ERM’s data analysis is a good attribute (Fitterman et al. 2012; Hinnell et al. 2010). Moreover, the easiness access to quality data and the cost effectiveness of ERM compared to all other aforementioned methods could be other reasons (Oyedele and Ekpoette 2011; Anomohanran 2015). The further analysis of the common geoelectrical parameters (resistivity and thickness) using relevant hydrologic equations to produce groundwater vulnerability indices usable in the monitoring of groundwater quality status as alternative to the conventional DRASTIC vulnerability modeling technique is the quest of this study. Mitigating the negative human impact on groundwater resources particularly its degrade quality status arising from the uncontrollable anthropogenic practices both at local and regional scale necessitates quality-checking approach that will allow its sustainable developmental strategies (Chen et al. 2013; Rahman 2008; Redhaounia et al. 2015). Groundwater is such a mineral resources type that is held in the underground soil or in pores and crevices in rock (Machiwal and Jha 2014). Such soil or pores and crevices in rock are hydrologically referred to as aquifers or groundwater reservoir formation (Kayode et al. 2016). The quantity magnitude and quality status of groundwater are largely influenced by the geometrical properties and nature of aquifer formation (Ravi Shankar and Mohan 2006; Sirhan et al.

2011; Gupta et al. 2015; Lateef 2012). To efficiently manage the groundwater quality status mostly in a typical hard rock terrain (HRT), the holistic evaluation of aquifer formation embracing its geometrical properties such as its superficial overlying material and its occupying materials can largely provide an excellent insight into the knowing of the nature and status of an area underlain aquifer unit’s fluid content. The prominent conventional approach for assessing the nature and status of the aquifer containing fluid in the subsurface is the uses of groundwater vulnerability model map based on DRASTIC vulnerability method (Shirazi et al. 2012; Tsai et al. 2013; Antonakos and Lambrakis 2007). However, according to Gorai et al. (2014) and Nobre et al. (2007), the DRASTIC modeling algorithm has limitations that require enhancement for precision in vulnerability assessment. A robust analysis of the subsurface lithology intrinsic parameters including the analyzed geoelectrical properties of the superficial overlying materials (resistivity and thickness) and the hydraulic properties of the aquifer unit (i.e., hydraulic conductivity; aquifer transmissivity) via application of data mining models can address the often employed DRASTIC model approach’s limitations and provide an alternate model usable for the same task. An innovative concept of modeling groundwater vulnerability decision-making tool using the subsurface lithological sequence’s physical parameters determined from the interpreted surface geoelectric data is introduced in this study. By inference, through applying the existing hydrologic equations to model subsurface lithologic geoelectrical parameters, vital groundwater vulnerability causative factors/parameters for analyzing and assessing an area vulnerability to pollution can be derived. The multi-criterial analysis of such groundwater vulnerability causative factors considering their relative importance towards groundwater quality mapping, the groundwater vulnerability potential prediction model of higher reliability and precision in any given area can be produced. For the purpose of achieving this laudable task, this study explored the potential of data mining models. The analytic hierarchy process (AHP) technique is an efficient data mining model that has been found relevant in both hydrology and environmental field of

Development of AHPDST Vulnerability Indexing Model

studies such as groundwater potential prediction (Adiat et al. 2012; Jha et al. 2010; Chowdhury et al. 2009), landfill site selection (Yal and Akgun 2013), Tourism (Ghamgosar et al. 2011), agriculture (Montazar and Behbahani 2007), technology (Ariff et al. 2008); Site selection (Vahidnia et al. 2009) and mineral exploration (De Araoujo and Macedo 2002). The Dempster–Shafer theory (DST)-based evidential belief (EBF) technique, on the other hand, is another data mining model which according to Al-Abadi (2015) has the ability to combining beliefs obtained from multiple factors as potential evidences for the prospective target. The applicability of DST-EBF data mining model has been proficient in several studies including mineral potential mapping, landslide susceptibility, groundwater potential mapping, groundwater vulnerability assessment and so on (Moon 1990; Carranza and Hale 2002; Carranza et al. 2005; Park 2011; Mohammady et al. 2012; Lee et al. 2012; Pourghasemi et al. 2013; Nampak et al. 2014; Mogaji et al. 2015a; Al-Abadi 2015). The hybrid application of these aforementioned data mining models has also been explored for sustainable transportation solution assessment with appealed results (Awasthi and Chauhan 2011). Additionally, for the effective implementation of these aforementioned data mining models in decision making studies, the potential of GIS technique is often explored. The relevance of GIS tool is such that it has the functionality for manipulating and storing large volumes of data, integrating spatial and non-spatial information in a single system that are traceable to these data mining models’ mechanism (Manap et al. 2011; Pradhan et al. 2010). Thus, the proficiency of GIS technique involving the application of data mining techniques in the field of groundwater hydrological study has been reported (Rahmati et al. 2016; Machiwal et al. 2015; Mogaji et al. 2015a; Nampak et al. 2014; Adiat et al. 2013). However, the concept of hybridizing the applied results of AHP and DST-EBF data mining models involving the spatial analysis module of GIS application in assessing aquifer vulnerability has never been investigated. This research attempts to develop groundwater vulnerability prediction model from geoelectrical derived-based groundwater vulnerability conditioning factors (GVCFs) for sustainability management of

groundwater in a typical HRT area. A novel groundwater vulnerability potential index (GVPI)-based AHPDST modeling approach developed from the hybridization of both DST-EBF and AHP-MCDA data mining models application to the derived GVCFs’ themes was adopted for the vulnerability indexing assessment. The uniqueness of the developed model includes flexibility, reliability, uncertainty analysis as well as the regional prospectivity compliance of the model’s output. This aforementioned uniqueness of the proposed approach can efficiently address the limitations of the conventional DRASTIC model often used for the same purpose (Gorai et al. 2014; Pradhan et al. 2013; Sahoo et al. 2016). Thus, the methodologies are illustrated using a case study in the HRT geology, southwestern, Nigeria with the view of establishing formidable hydrological database system for onward effective groundwater resources management in the area.

2. Geography, Hydrology, and Hydrogeology of the Study Area The study area is located in the northern region of Ondo state, the southwestern part of Nigeria (Fig. 1a). The area (Fig. 1b) is bounded by latitudes 6 400 000 and 7 4500 0000 N and longitudes 4 200 0000 and 6 050 0000 E. The geological map of the study area (Fig. 1c) shows that the area is covered by the Basement Complex rocks such as the granitic rocks, the migmatite–gneiss complex rock, the charnockitic meta-intrusive rock and the quartzite series rock (Jone and Hockey 1964; Rahaman and Ocan 1978; Rahaman 1988). In accordance with Ozdemir (2011), the geology of these varying rock types defines the occurrences of groundwater. The occurences of aquifer formation on these rock types are largely space dependent because of their low porosity and negligible permeability properties which vary from one place to another. The occasioning of multiple tectonic events in hard rock geologic terrain resulting to fracturing, faulting and incipient joint on underlain rocks do create secondary porosities for developing aquifer unit where exist these rock units (Deolankar 1980; Hazell et al. 1988). The developed aquifers on these rocks could either be unconfined or confined

K. A. Mogaji

Pure Appl. Geophys.

(b)

(a)

(c)

Figure 1 The study area descriptions’ materials showing a map of Nigeria, b site map depicting the VES locations, c site geologic map

types which according to Satpathy and Kanungo (1976), Dan-Hassan and Olorunfemi (1999), and Bala and Ike (2001) are often localized and discontinuous. The varying permeability properties of these aquifer types largely determined the contained groundwater discharge rate, i.e., borehole yield in an area. Thus, the determination of the aquifer intrinsic properties including storage capacity, specific yield, hydraulic conductivity is essential for efficient groundwater resources management viz-a-viz its quality status assessment in a given study area. The climatical and terrain characteristics of the area are characterized with moderate to high elevation values, ranging from 200 m to 250 m and with annual mean monthly temperature and rainfall of 6–33 C and range of 650–1800 mm, respectively.

3. Materials and Methodology The materials used in this study encompass the existing borehole data records and the field acquired geophysical data. Figure 2 presents the embraced methodological procedural flow for assessing the regional groundwater vulnerability in the studied area. The procedures were implemented at eight different stages. Stage 1 entails (i): the analysis of the observed aquifer yield rates based on the pumping test measurement, (ii) inferring from the determined borehole yield rate, the vulnerability of the underlain aquifer to pollution. Stage 2 involved geophysical data acquisition, processing, interpretation and determination of geoelectrical parameters [layer thickness (h) and layer resistivity (q)]. The modeling


Figure 2 The methodological flowchart adopted for the study

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Pure Appl. Geophys.

of geoelectrical-based groundwater vulnerability conditioning factors (GVCFs) and evaluations of their hydrological significance make up stage 3. At stage 4, the potential of GIS technique was explored. At this stage, thematic maps of the derived GVCFs were generated in GIS platform using the obtained results in stage 3. The theories and applications of the adopted data mining models such as AHP-MCDA and DST-EBF models to vulnerability analysis were accounted for in stage 5. In stage 6, the development of AHPDST vulnerability indexing algorithm was discussed. The application of GVPI-based AHPDST algorithm and modeling of groundwater vulnerability potential zone (GVPZ) map is the 7th stage. Validation of the produced GVPZ map using the relative operating characteristics (ROC) technique was carried out at stage 8 to establish the reliability of the model map in environmental decision-making studies.

aquifer received its sources mainly from the surface water, particularly rainfall serving as the major recharging sources (Adiat et al. 2012; Dhar et al. 2014; Mogaji et al. 2015a). However, the pollution of groundwater is often facilitated with the surface water recharging the aquifer because the conduit medium for contaminant infiltrate into aquifer is the surface water. It thus implies that at higher surface water recharging rate, the aquifer potentiality will be higher and thus higher will be the aquifer’s fluid content contamination and vice versa. Therefore, a high yield rated aquifer unit is more liable to pollution unlike a low yield rated aquifer unit. The liability of the underlain aquifer formations in the study area to pollution was evaluated via analyzing the available aquifer yield rates. Figure 3 shows the spatial analysis of the well-yield rates based on the groundwater wellpumping test records in the range of 0.72–69.89 m3/h values observed in the study area. The seventy-eight (78) occupied productive borehole locations (Fig. 3) were randomly divided into a training dataset of 70% (54 groundwater wells) and a validation dataset of 30% (24 groundwater wells) to quantitatively evaluate the aquifer vulnerability in the area. With this analysis, the objective of providing a complete and accurate groundwater management viz-a-viz its quality status monitoring and preservation, the prediction and mapping of groundwater vulnerability can be greatly achieved.

3.1. The Assessed Aquifer Unit’s Productivity and Their Contamination Vulnerability In the HRT geology area, the two major aquifer types that characterized the subsurface lithology are the confined and unconfined aquifers (Mogaji et al. 2011). The hydrological implications of these aquifer types are defined by the permeability and porosity properties of their overlying materials (Fedkiw 1991; Whitsell and Hutchinson 1973). In terms of quality assessment of groundwater occurrences in the subsurface geologic structures, the susceptibility of the containing groundwater to pollution in the unconfined aquifer type is very high due to high porosity property of the overlying materials unlike the confined aquifer’s fluid content that is less prone to pollution for its impervious covered materials (Barker et al. 2001). Besides, the potentiality of these aquifers is measured by the groundwater containing volume and this is varied from one place to another in HRT area because of the different properties of the overlying materials. Usually, the yield rate parameter determined based on pumping test survey in an area often gives the assessment of the area underlain aquifers’ potentiality. Thus, the likely aquifer yield rate in an area is mostly directly related to its groundwater volume contained. The groundwater volume in the

3.2. 1-D Resistivity Imaging Geophysical Data Acquisition, Processing, and Interpretations 3.2.1 Geophysical Data Acquisition The electrical resistivity method (ERM) was adopted for the 1D resistivity imaging data acquisition at 450 locations (Fig. 1b). This method uses artificial source of current (I) introduced into the ground through a set of metallic rods referred to as the current electrodes, while a measurement of the potentials generated was made using another set of metallic rods referred to as the potential electrodes. The R-50 DC Resistivity meter was used to measure the potential difference (DV) from which the resistance (R) is computed (R = DV/I) and converted into apparent resistivity (qa) (geometric factor 9 resistance). In this study, the


Figure 3 Borehole yield rate analyzed locations map

Schlumberger array among other arrays was used for the acquisition of vertical electrical sounding (VES) because of its comparable depth of investigation, speed, and convenience (Loke 2001). The spread length of the Schlumberger current electrode varies from 2 and 200 m. The Global Positioning System (GPS) device was used to record geographical coordinates (in degrees) of the occupied VES stations. 3.2.2 Data Processing and Interpretation Technique The acquired vertical electrical soundings data were processed by plotting the measured apparent resistivity values against half-current electrode spacing (AB/2) at each station on a log–log graph sheets to generate the resistivity field curves. The VES curves were interpreted using the conventional partial curve matching technique (Orellana and Mooney 1966; Bhattacharya and Patra 1968) to obtain an initial model as input into an inversion procedure run with a modified Marquardt–Levenberg inversion algorithm involving the WinResistTM Software (Vander-Velper 2004) as used in the studies of Kayode et al. (2016)

and Mogaji (2016). As the inversion proceeded, the method aimed to iteratively minimize the difference between the field and theoretical curve, this continues until a reasonable fit was achieved. The quality of the data fit was expressed in terms of the RMS error, and once the misfit is less than 5%, the iteration was stopped. The final model curve displays the subsurface model parameters in terms of apparent resistivities and thicknesses (qi, hi, i = 1, 2, …, n) for a given subsurface geological formation/layers that underlain the area. Typical theoretical/resistivity model curves obtained based on the underlain geology in the area are presented in Fig. 4. The representative summary of the interpreted geoelectric parameters is presented in Table 1. 3.3. The Modeling of Geoelectrical-Based Groundwater Vulnerability Conditioning Factors From the concept of DRASTIC model according to Aller et al. (1987), the geological and hydrogeological factors that affect and control groundwater

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Figure 4 Typical resistivity model curves obtained in the area; a quartizite series, b migmatite group complex, c charnokitic rock and d granite gneiss

contamination in the subsurface are referred to as groundwater vulnerability conditioning factors (GVCFs). The renowned GVCFs for DRASTIC method such as [depth to water table (D), net recharge (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I), and hydraulic conductivity (C) are often obtained from difference data sources including borehole data, remote sensing data, etc. (Shirazi et al. 2012; Al-Adamat et al. 2003; Gorai et al. 2014; Samake et al. 2011). However, this study modeled its GVCFs such as recharge rate (Re), aquifer transmissivity (Taq), hydraulic conductivity (K), transverse resistance (TR) and longitudinal unit conductance (S) mainly from the determined geoelectric parameters in Table 1. The GVCFs’ modeling processes are detailed in the following subsection: 3.3.1 Recharge Rate (Re) The principle of direct relationship/interaction that exist between surface water and groundwater largely

defined the recharge rate characteristics of an area (Flint et al. 2002). Thus, considering the effect of aquifer unit top’s overlying unsaturated zone layers’ physical properties on the surface water that often move from land surface through subsurface lithologies into groundwater containing medium (aquifer formation) were conceptualized for evaluating surface water recharge rate of the area underlain aquifers. With the geophysical measurement, the resistivity (q) and thickness (D) of the unsaturated layers (vadose zone) were estimated (Table 1). The potential of recharge rate model Eq. (1) established for a typical basement rock terrain using the approach reported in Mogaji et al. (2015b) was explored for modeling the area recharge rate variation. The validity of the used Eq. (1) was such that (i) with the application of the Charturvedi formula (Mogaji et al. 2015b) to the area observed rainfall record (650–1800 mm) that returned the rainfallrecharge rate in the range 170.23–285.26 mm/year relatively correlated with the results of the recharge rates of 173–273 mm/year estimated based on the


Table 1 The summary of the interpreted geoelectric parameters VE no.

LAT

LONG

q1 q2 q3 (Xm) (Xm) (Xm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 ?. ? 450

815,896.8 815,872.5 801,509.2 801,527.6 797,873.3 823,907.7 823,895.6 823,892.6 803,404.7 803,401.6 803,312.6 803,525.7 803,516.5 802,386.9 803,176.6 803,136.5 803,146.4 803,167.3 803,177 803,177 816,233.7 816,187.6 800,485.3 800,516.4 800,444 796,914.4 807,934.3 798,722.3 819,450 796,715.4 796,718.5 796,854.3 793,601.7 793,493.6 799,085.2 799,050.8 797,093.6 799,003 799,000 798,214 – –

807,147.3 807,196.6 746,343.9 746,334.6 740,475.3 800,671.3 800,702 800,711.2 743,741.5 743,741.5 743,769.6 739,530.7 739,543.1 743,611.4 743,607.6 743,571 743,712.1 743,589.2 743,687.4 743,684.3 801,973.6 801,983.1 777,998.8 778,063.1 742,147.8 743,634.9 743,808.3 743,859.4 727,582.5 784,288.2 784,294.4 786,565.4 746,753.9 746,644 743,289.9 743,167.3 741,927.6 740,239.8 740,255.1 742,944.2 – –

227 618 506 659 909 682 1785 973 4328 134 552 650 634 1082 725 3531 643 1206 4363 1161 2545 1067 664 2712 2289 2435 2224 1588 562 427 5758 148 96 65 417 285 45 111 40 517 – – 420

200 104 296 179 136 147 239 111 245 3885 347 63 135 987 268 128 311 156 195 148 312 194 373 299 362 51 659 66 31 13 206 306 319 63 260 105 832 5626 1056 256 – – 79

1508 561 2427 33,092 6199 7547 8497 9054 2330 958 2484 1304 150 2013 707 1551 1122 5106 2065 3615 1140 7158 13,835 1554 1489 7516 1718 23 168 2423 80 50 72 2446 65 1929 12,780 160 22 2315 – – 837

q4 (Xm)

q5 q6 h1 (Xm) (Xm) (m)

753

24,972 59,270 796 121

326 13,834

248 713

585

2431 733 1529 2795

4426 5888 89 – –

1249 – –

395

0.50 0.80 1.01 0.70 0.51 2.20 0.53 2.01 0.51 0.40 0.70 1.30 0.71 0.60 0.40 0.41 0.92 1.10 0.53 0.60 0.62 1.01 0.70 0.94 0.52 0.44 0.51 0.32 0.51 1.02 0.33 0.63 0.94 0.52 0.43 2.22 0.32 0.43 0.40 0.44 – – 1.43

h2 (m) 4.32 2.73 4.12 3.91 6.04 8.83 14.91 5.40 2.41 1.10 2.01 13.71 8.30 32.42 0.92 5.60 8.33 5.03 4.20 3.03 5.01 4.23 3.72 11.1 23.63 4.82 0.52 4.43 0.93 1.73 2.93 1.52 0.50 7.12 7.02 15.61 11.12 0.22 0.70 1.10 –

h3 (m)

12.61

7.11 10.80 13.92 1.41

6.43 10.62

1.83 12.20

7.91 12.03 12.01 9.30

5.04 4.72 0.91 –

10.42 –

h4 (m)

h5 (m)

Ar

200 561 296 179 136 147 239 111 245 958 347 63 150 947 3.83 121 128 311 157 7.62 326 148 312 194 373 299 362 51 2.04 14.1 584 23 31 13 80 50 72 63 65 105 832 160 22 14.22 – 89 – – – – – – – – 79

At

Vr

Vt

CT

4.31 12.60 4.11 3.93 6.02 8.82 14.92 5.43 2.41 7.10 2.04 13.72 13.90 32.40 3.84 5.62 8.30 5.04 7.62 3.03 5.02 4.22 3.72 11.11 23.64 4.83 14.10 12.23 0.92 1.73 7.92 12.02 12.03 7.03 9.32 15.61 11.12 5.01 4.71 14.23 – – 10.41

227 361 506 659 909 682 1785 973 4328 2001 552 650 390 1082 566 3531 643 1206 5246 1161 2545 1067 664 2712 2289 2435 4663 1621 562 427 5861 301 255 65 544 285 45 2924 568 1545 – – 420

0.52 3.51 1.01 0.72 0.51 2.23 0.54 2.03 0.53 1.52 0.71 1.32 9.03 0.62 2.83 0.42 0.93 1.12 114 0.63 0.64 1.02 0.72 0.94 0.53 0.43 4.82 4.73 0.51 1.01 3.31 2.11 1.42 0.54 7.41 2.23 0.34 0.62 1.10 2.54 – – 1.43

H HA H H H H H H H KH HA H HA H HA H H H HK H HA H H H H H HKHK QH H H QH KH KH H QH H A KH KH HKH – – H

q1 - qn = resistivity values for each layer (Xm), h1 - h2 = true thickness for each layer (m), Ar = aquifer layer resistivity, At = aquifer layer thickness, Vr = vadose layer resistivity, Vt = vadose layer thickness and CT = Curve types

applied Eq. 1 (Table 2) and (ii) the coefficient of determination (R2) of 0.7504 and 0.8213 for the generated linear graphs showing relationship of the estimated recharge values versus the vadose zone resistivity (q) values and its thickness (D) values determined in the area (not shown), respectively,

established their appropriateness as predictor parameters in the developed Eq. 1. Re ¼ 34:41 log10 ðqÞ þ 1:05ðDÞ þ 128:38

ð1Þ

where Re is the recharge rate, q is the vadose layer resistivity and D is the thickness of the vadose layer.

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Table 2 The geoelectrical-based GVCFs modeled results VES no.

LAT

LONG

Re (mm/year)

K (m/day)

Taq m2/day

TR (X2m)

S (X-1)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 ? ? 450

815,896.8 815,872.5 801,509.2 801,527.6 797,873.3 823,907.7 823,895.6 823,892.6 803,404.7 803,401.6 803,312.6 803,525.7 803,516.5 802,386.9 803,176.6 803,136.5 803,146.4 803,167.3 803,177 803,177 816,233.7 816,187.6 800,485.3 800,516.4 800,444 796,914.4 807,934.3 798,722.3 819,450 796,715.4 796,718.5 796,854.3 793,601.7 793,493.6 799,085.2 799,050.8 797,093.6 799,003 799,000 798,214 – – 823,695.32

807,147.3 807,196.6 746,343.9 746,334.6 740,475.3 800,671.3 800,702 800,711.2 743,741.5 743,741.5 743,769.6 739,530.7 739,543.1 743,611.4 743,607.6 743,571 743,712.1 743,589.2 743,687.4 743,684.3 801,973.6 801,983.1 777,998.8 778,063.1 742,147.8 743,634.9 743,808.3 743,859.4 727,582.5 784,288.2 784,294.4 786,565.4 746,753.9 746,644 743,289.9 743,167.3 741,927.6 740,239.8 740,255.1 742,944.2 – – 804,052.71

213.94 229.59 225.71 229.44 236.45 235.10 255.88 236.84 255.99 249.40 224.80 239.53 232.11 266.78 227.07 256.31 233.70 239.63 264.32 236.96 250.79 236.96 229.35 258.14 268.73 249.92 269.39 251.61 223.92 220.65 266.29 226.24 223.76 198.09 232.25 229.21 196.91 252.86 228.06 252.99 – – 229.54

0.23 3.05 0.45 0.20 0.14 0.16 0.30 0.12 0.31 53.26 0.65 0.08 0.16 49.20 0.13 0.14 0.50 0.17 0.56 0.16 0.51 0.22 0.79 0.46 0.73 0.08 3.61 0.06 0.07 0.06 0.10 0.08 0.09 0.08 0.09 0.11 21.50 0.17 0.06 0.10 – – 0.10

0.98 38.49 1.86 0.76 0.86 1.36 4.48 0.65 0.75 378.14 1.31 1.16 2.20 1594.19 0.49 0.76 4.19 0.83 4.28 0.47 2.54 0.91 2.92 5.14 17.20 0.37 50.83 0.77 0.06 0.10 0.76 0.93 1.08 0.59 0.80 1.79 238.63 0.85 0.30 1.45 – – 0.99

113.50 775.20 506.00 461.30 454.50 1500.40 892.50 1946.00 2164.00 4327.10 1080.40 845.00 1564.30 649.20 1521.00 1412.40 578.70 1326.60 16,216.50 696.60 3087.00 1067.00 464.80 2440.80 1144.50 974.00 5029.90 766.80 281.00 427.00 2324.80 547.80 117.90 32.50 1986.80 627.00 13.50 1169.60 755.20 2571.90 – – 588.00

0.0022 0.0273 0.0020 0.0011 0.0006 0.0032 0.0003 0.0021 0.0001 0.0033 0.0013 0.0020 0.0626 0.0006 0.0059 0.0001 0.0014 0.0009 0.0248 0.0005 0.0002 0.0009 0.0011 0.0003 0.0002 0.0002 0.0101 0.0669 0.0009 0.0023 0.0141 0.0090 0.0094 0.0077 0.0279 0.0077 0.0067 0.0036 0.0107 0.0055 – – 0.0033

Re recharge rate, K hydraulic conductivity, Taq aquifer transmissivity, TR transverse resistance; S longitudinal conductance, GVCFs groundwater vulnerability conditioning factors

3.3.2 Hydraulic Conductivity (K) In accordance with Kaliraj et al. (2014) and Jasrotia et al. (2016), the K parameter defined the rate of flow of water under a unit cross-sectional area of the aquifer. The movement of fluid within the aquifer

rock materials is largely controlled by K. The aquifer hydraulic conductivity of the area was computed using the established non-linear relationship between hydraulic conductivity (K) and aquifer resistivity by Singh (2005) given in Eq. 2. The appropriateness of Eq. 2 in the study area has been substantiated in the


studies of Adepelumi et al. (2006) and Mogaji et al. (2011) who have applied the same equation to solve hydrogeological problems in Crystalline Basement rock terrain with appealed results. K ¼ 0:0538‘0:0072q

ð2Þ

where K is the formation hydraulic conductivity and q is the aquifer layer resistivity. 3.3.3 Aquifer Transmissivity (Taq) The aquifer transmissivity (Taq) parameter according to Jasrotia et al. (2016) has been reportedly described as the rate of flow of water under a unit hydraulic gradient transmitted through a cross section of unit width over the whole saturated thickness of the aquifer. The porosity and permeability properties of a mapped saturated formation defined an area Taq parameter (Opara et al. 2014). The Taq values for the area were computed via exploring relevant groundwater flow model equation as used in the studies of Ahamed and De Marsily (1987), Khan et al. (2002) and Pradhan et al. (2013). The given Eq. 3 was applied to the interpreted results in Table 1 to model the area aquifer transmissivity (Taq) variations (Table 2). Taq ¼ K h

ð3Þ

where Taq is the formation aquifer transmissivity, K is the formation hydraulic conductivity, and h is the estimated aquifer thickness based on the interpreted surface geophysical measurement. 3.3.4 Transverse Resistance (TR) The TR is one of the second-order parameters (Dar– Zarrouk parameters) that has its origin from geophysical measurement. In accordance with Karim et al. (2013), the TR is described as the total transverse unit resistances that measured through a column cut perpendicular to the bedding plane of the sequence of layers with resistivity (qn) and thicknesses (hn). Equation (4) was used for computing TR values at each VES location (Table 2), using interpreted results in Table 1 TR ¼

n X l¼1

qi hi ¼ q1 h1 þ q2 h2 . . .qn hn

ð4Þ

where h and q are the resistivity and thickness properties of the layers that overlain the delineated aquifer layer. 3.3.5 Longitudinal Conductance Unit (S) This parameter defined the total conductance along the direction of bedding plane of layers when there is occurrence of formation with high conductivity existing between two media of high resistivity (Karim et al. 2013). The S values were estimated using Eq. (5). X h 1 h2 hn þ þ ð5Þ S¼ q1 q2 qn where h and q are the resistivity and thickness properties of the layers that overlain the delineated aquifer layer. The area S varied values estimated are presented in Table 2.

3.4. The Groundwater Vulnerability Conditioning Factors (GVCFs) Hydrological Significance Characteristics In this study, five GVCFs modeled from geoelectrical point of view were considered for the vulnerability assessment analysis in the investigated area. A brief account of their hydrological significances towards monitoring groundwater quality status is thus provided. The recharge rate as one of the considered factors has been described by Samake et al. (2011) and Anirban et al. (2016) as the prime factor of pollutant transport to aquifer. It controls the dispersion of the pollutants to vadose zone. Reports according to Aller et al. (1987) corroborate the fact that susceptibility of an area underlain groundwater reservoir to pollution is often triggered by its varying surface water recharge rate. The knowledge of hydraulic conductivity (K) is very important in solving various environmental problems as it is one of the most important sol properties for the determination of infiltration rate. Thus, the controlling of water infiltration, leaching of pesticides from cultivated land, surface run-off and migration of pollutants from contaminated sites to the nearby groundwater resources are largely influenced by K parameter (Gulser and Candemir 2008 and

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Pure Appl. Geophys.

Bagarello and Sgroi 2007). The transmissivity property of the mapped saturated formation is largely defined by the formation permeability characteristics. In accordance with George et al. (2015), the easy of groundwater contamination depends mostly on the aquifer formation permeability property. Thus, at an increase in Taq rate, an area groundwater reservoir formation is more vulnerable and vice versa. The TR factor has great influence on the contaminant percolation in an area. This is due to the fact that TR is directly proportional to Taq (Adiat et al. 2013). Consequently, any area with higher TR values, has greater tendency to vulnerability compared to areas of lower TR values. The significance of longitudinal conductance unit (S) factor has been reported by Abiola et al. (2009) as a key parameter for evaluating the protective capacity of the soil medial column overlying the aquifer layer because the content of clay material composed of the subsurface lithology can be evaluated based on S values estimate. At higher S values, subsurface soil formation is often interpreted as high clay content formation and vice versa. Invariably, subsoil formations characterized with higher clay materials content are less prone to pollution unlike that with low clay materials content.

dependent (Mogaji et al. 2014; Mogaji 2016; Mogaji and Lim 2017). Thus, in order to produce a groundwater vulnerability potential prediction model of higher reliability and precision in the investigated area, the effects of all the relevant GVCFs must be integrated (Adiat et al. 2013). For the purpose of harnessing the significance contributions of these factors, this study employed the principles and theories of AHP-MCDA and DST-EBF data mining models. The uniqueness of the proposed AHPMCDA and DST-EBF techniques is such that the inconsistency of the parameters’ weightage assignment is accounted for using the AHP-MCDA technique while the DST-EBF model has the ability to deal with ignorance (uncertainty) and missing information that are traceable to groundwater pollution vulnerability mapping (Mogaji and Lim 2017; Mogaji et al. 2015a).

3.5. Application of GIS Tool 3.5.1 Production of Groundwater Vulnerability Conditioning Factor Maps To effectively preserve the contained groundwater in an area underlain aquifer formation regionally, the spatially mapping of an area aquifer vulnerability potentiality obtainable from different GVCFs’ attributes is a laudable approach (Antonakos and Lambrakis 2007; Chen et al. 2013). This study explored the potential of GIS technique to spatially and quantitatively analyze the relevance contribution of these aforementioned GVCFs on regional aquifer vulnerability prospect in the investigated area. Consequently, the application results of Eqs. 1–5 presented in Table 2 were processed in GIS environment to produce maps of Re, K, Taq, TR and S (Fig. 5). According to the literatures, the degrees of contribution of each GVCFs’ map to aquifer vulnerability assessment vary and are largely space

3.6. Applied Data Mining Models Theories and Principles 3.6.1 The MCDA-AHP Method Approach Analytic hierarchy process (AHP) technique is one of the multi-criteria decision-analysis (MCDA) models that has the functionality of allowing map layers or criteria to be weighed to reflect their relative influence/importance (Voogd 1983; Eastman 1996; Navalgund (1997). Its mechanism is apt for decision making in the presence of two or more conflicting objectives. The model has gainfully been explored in vast numbers of environmental decision-making studies because of its capability of capturing both subjective and objective evaluation measures. Besides, this model has useful mechanism for checking the consistency of the evaluation measures which largely reduced bias in decision making (Ariff et al. 2008; Ghamgosar et al. 2011; Montazar and Behbahani 2007; Vahidnia et al. 2009; De Araoujo and Macedo 2002). With the used of standard Saaty scale of 1–9 (Table 3) according to Saaty and Vargsa (1991) wherein the value of 1 suggests that the criteria are equally important and a value of 9 leads one to infer that the criterion under consideration is extremely important in relation to the other criterion considered, the construction of Pair-Wise comparison


Figure 5 The groundwater vulnerability conditioning factors’ thematic layers: a recharge rate, b hydraulic conductivity (K), c aquifer transmissivity (T), d transverse resistance (TR) and e longitudinal conductance (S)

Matrices (PCM) upon which the AHP theory largely based can be created. The typical PCM for this study is presented in Table 3. Exploring this model theory and principle, similar procedures reported in the

studies of Thirumalaivasan et al. (2003), Jha et al. (2010), Adiat et al. (2012), Zhou and Chen (2014) Mogaji et al. (2014) and Mogaji (2016) were adopted to solve the generated PCM (Table 3) and the

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Pure Appl. Geophys.

Table 3 A matrix of pairwise comparisons of groundwater vulnerability conditioning factors (GVCFs) Pairwise comparison 9 point continuous rating scale Less important 1/9 Extremely

Re T K TR S

1/7 Very strongly

More important 1/5 strongly

1/3 Moderately Re

1 Equally T

3 Moderately K

5 Strongly TR

7 Very strongly S

9 Extremely Weights

1 1/3 1/3 1/5 1/7

3 1 1/3 1/5 1/7

3 3 1 1/3 1/5

5 5 3 1 1/3

7 7 5 3 1

0.44 0.29 0.16 0.08 0.04

CR 0.06

Re recharge rate, T transmissivity, K hydraulic conductivity, TR transverse resistance, S longitudinal conductance, CR consistency ratio

normalized weight for the GVCFs’ layer was determined. However, for the computation of the level of consistency in the pairwise comparison matrices, the sound mathematical basis including the relevant formula equations of AHP technique as detailed in the studies of Zhou and Chen (2014) and Mogaji and Lim (2016) was used. The determined normalized weight for the GVCFs is populated in Column 6 of Table 3 and the estimated consistency ratio (CR) of 0.06 in column 7. Based on the submission of Feizizadeh and Blaschke (2013), the CR = 0.06 certified that the estimated normalized weights (W) of 0.44, 0.29, 0.16, 0.08, 0.03, and 0.04 can be assigned to the recharge rate layer (Re), aquifer transmissivity layer (Taq), hydraulic conductivity layer (K), transverse resistance layer (TR) and longitudinal conductance unit layer (S), respectively (Table 4). 3.6.2 The Dempster–Shafer Evidential Belief Function Theory The basic principle of Dempster–Shafer (DS) model is driven by the generalization of Bayesian theory where the subjective probabilities are defined under the framework of the evidential belief function (EBF) as lower and upper probabilities (Dempster 1967, 1968). In accordance with Dempster (2008), these lower and upper probabilities have been established as the belief (Bel) and the plausibility (Pls) mass function series, respectively, as applicable to EBF modeling mechanism. However, for the

effective application of the DS model, other relevant mass function series besides the Bel and Pls are the degree of uncertainty (Unc) and the degree of disbelief (Dis) as shown in Fig. 6. It is importance to note that the analysis of basic probability assignment of mass function (bpa or m); the Belief function (Bel) and the Plausibility function (Pls) are the vital elements for the DST-EBF model operating mechanism (Mogaji et al. 2015a; Awasthi and Chauhan 2011). Understanding the concept of practical applications of DST model theoretical mechanism in the field of groundwater hydrology according to Nampak et al. (2014), Mogaji et al. (2016), Rahmati and Melesse (2016), this model was applied to determine the mass functions series for the GVCFs’ maps class boundaries. The mass functions series were computed employing the model’s likelihood ratio functions (LRF) relationship where the quantitative spatial relationship between the aquifer yield rate locations (Fig. 3) and the GVCFs’ thematic layer (Fig. 5) was observed. The brief on the LRF analysis is such that if we have ‘ representing the multiple spatial thematic layers (GVCFs) in an area where aquifer yield rate observed locations exist, then each GVCFs’ thematic layer is regarded as evidence Ei (i = 1, 2, 3, …, ‘) for the target proposition Tp . In accordance with Park (2011), if Eij is the jth class attribute of the evidence Eij and frequency distribution function of positive and opposite target propositions, the likelihood ratio can be quantitatively determined using the following relationship Eqs. 6–11 as detailed below:


Table 4 The DST-EBF class values and the MCDA-AHP normalized weight assignment values for the groundwater vulnerability (GV) conditioning factors GV conditioning-hydrologic themes Category (classes) Groundwater vulnerability A potentiality

Recharge rate (Re)

Aquifer transmissivity (Taq)

Hydraulic conductivity (K)

Transverse resistance (TR)

Longitudinal conductance unit (S)

173.10–213.00 213.00–221.00 221.00–229.30 229.30–242.00 242.00–269.00 0.07–65.61 65.61–169.38 169.38–305.92 305.92–568.09 568.09–1392.81 0.07–2.50 2.50–6.92 6.92–12.67 12.67–24.16 24.16–56.44 44–5241 5241 -1196 11,966–20,830 20,830–39,477 39,477–77,992 0.00–1.21 1.21–4.19 4.19–8.10 8.10–13.03 13.03–23.74

Very low Low Medium Medium high High Very low Low Medium Medium high High Very low Low Medium Medium high High Very low Low Medium Medium high High High Medium high Medium Low Very low

B

C

3177 0.44 11,110 7640 7403 2031 244 0.29 6323 1957 351 22,659 244 4911 0.16 4108 2111 19,987 21,993 4586 0.08 3949 704 129 25,661 2701 0.04 1372 1159 468

Mass function factors (MFFs) computed values based on DST model

6 11 19 14 4 0 10 6 0 38 0 8 10 3 33 49 1 4 0 0 50 2 1 1 0

D

E

F

G

0.20 0.14 0.35 0.26 0.27 0.00 0.23 0.44 0.00 0.26 0.00 0.18 0.28 0.16 0.23 0.64 0.03 0.12 0.00 0.00 0.46 0.06 0.06 0.07 0.00

0.20 0.25 0.17 0.19 0.20 0.20 0.20 0.19 0.20 0.21 0.20 0.20 0.19 0.20 0.21 0.07 0.23 0.21 0.20 0.20 0.12 0.22 0.22 0.21 1.00

0.60 0.62 0.48 0.54 0.53 0.80 0.57 0.37 0.80 0.52 0.80 0.61 0.54 0.64 0.56 0.29 0.75 0.67 0.80 0.80 0.43 0.72 0.73 0.72 0.00

0.80 0.75 0.83 0.81 0.80 0.80 0.80 0.81 0.80 0.79 0.80 0.80 0.81 0.80 0.79 0.93 0.77 0.79 0.80 0.80 0.88 0.78 0.78 0.79 0.00

A; AHP criteria weight estimated; B: No of class pixels; C: No of aquifer yield rate location wells per class; D: Believe (Bel) factor; E: Disbelieve (Dis) factor; F: Uncertainty (Unc) factor; G: Plausibility (Pls) factor for each classes boundary level estimated; GV: Groundwater vulnerability

k Tp ¼

N ðL\Eij Þ N ð LÞ N ðEij ÞN ðL\Eij Þ N ð AÞN ðLÞ

;

ð6Þ

where N L \ Eij is the number of groundwater wells that occurred in Eij ; N ðLÞ is the total number of existing wells with a productive yield in the study area; and N Eij is the pixel number in the study area. The Bel function can be calculated as: k Tp Eij Bel ¼ P ; ð7Þ k Tp Eij The likelihood ratio to support the opposite target proposition is calculated as:

k Tp ¼

N ðLÞN ðL\Eij Þ N ð LÞ N ð AÞN ðLÞN ðEij ÞþN ðL\Eij Þ N ð AÞN ðLÞ

The Dis function is calculated as: k Tp Eij Dis ¼ P ; k Tp Eij

;

ð8Þ

ð9Þ

The uncertainty (Unc) and plausibility (Pls) value areas were obtained using Eqs. (10) and (11) as follows: Unc ¼ 1 Dis Bel,

ð10Þ

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Pure Appl. Geophys.

the mass function factors (MFFs) and the normalized weight (w), respectively, to produce Eq. (13): U ðOiÞ ¼

R¼n X

MFFsðOiÞ W ðCR Þ

ð13Þ

R¼i

The LHS of Eq. (13) is substituted with the GVPI and, thus, a simple groundwater vulnerability potential index (GVPI) algorithm similar to that of DRASTIC index (DI) algorithm (Gorai et al. 2014) and used for modeling vulnerability potential attributes interpreted from the GVCFs’ thematic maps is given in Eq. (14). Figure 6 Schematic relationships of evidential belief functions. Modified after Wright and Bonham-Carter (1996), Carranza et al. (2005), Althuwaynee et al. (2012)

Pls ¼ 1 Dis,

ð11Þ

Quantitatively, the output of Eqs. 7, 9, 10 and 11 is the DST-EBF model determined mass function factors (MFFs) for the class boundaries of each of the GVCFs’ thematic maps (Fig. 5). 3.7. Development of AHPDST Vulnerability Index Model Algorithm This work developed a new vulnerability indexing modeling algorithm through the hybrid of the parameters estimation obtained from both AHPMCDA and DST-EBF data mining models. The development involved the application of weighted linear average (WLA) technique. The WLA technique as reported in the study of Adiat et al. (2012) is a simple weightage approach that is driven in terms of normalized weightage of criteria and normalized scores/rating for all options relative to each of the criteria. Equation 12 gives the WLA technique adopted in this study: U ðOiÞ ¼

R¼n X

ZR ðOiÞ W ðCR Þ;

ð12Þ

R¼i

U(Oi) is the expected output U for each option Oi, ZR(Oi) is the normalized score of option Oi under criterion CR, while w(CR) is the normalized weighting for each criterion CR. Equation (12) was modified by replacing its components ZR(Oi) and w(CR) with the estimated parameters from both DST and AHP, i.e.,

GVPIðOiÞ ¼

R¼n X

MFFsðOiÞ W ðCR Þ

ð14Þ

R¼i

However, according to DST model theory, the MFFs parameter can be defined by the Bel, Dis, Unc and Pls components (Table 4) describing the Oi for each of the GVCFs class boundaries, while the W was determined by the AHP model described the CR for each of the GVCFs. To apply Eq. 14 for computing the groundwater vulnerability potential index values at each grid location (Fig. 7), an indexing algorithm was derived similar to that reported in Chowdhury et al. (2009) and Adiat et al. (2013). Equation 15 is an indexing algorithm relationship typically for the GVPIBel computation at each grid location (Fig. 7). GVPIBel ¼ ReW ReBel þ TW TBel þ KW KBel þ TRW TRBel þ SW SBel

ð15Þ

The GVPIDis, GVPIUnc and GVPIPls indexing algorithm relationships were also established as Eq. 15 above. Equation (15) is the developed GVPI-based AHPDST vulnerability indexing modeling algorithm for the given study area.

4. Results and Discussion 4.1. VES Investigation Results The results of the geophysically based interpreted vertical electrical sounding data were analyzed in terms of the distribution of apparent resistivity values and thicknesses from which the earth subsurface models’ strata/layers were delineated. The resistivity curves obtained from the geoelectric survey are the 3-geoelectric layers (A, H), the 4-geoelectric layers


Figure 7 The spatial attributes scoring template for the developed GVCFs-based AHPDST model application

(HA, KH, QH) and the complex 5-geoelectric layers (HKH and HKHK). The summary of the interpreted VES results is presented in Table 1. The application results of Eqs. 1, 2, 3, 4 and 5 to solve Table 1 gives estimate values for the Re, K, Taq, TR and S beneath each VES location (Table 2). According to Table 2, the estimated values are in the range 173.10–269.00 mm/year, 0.07–56.44 m/day, 0.07–1392.81 m2/day, 44–77,992 X m2 and 0.00–23.74 X-1, respectively. Table 2 results were processed in GIS environment to produce the thematic maps for the geoelectrical-derived groundwater vulnerability conditioning factors (GVCFs) (Fig. 5). 4.2. Application of GVPI-Based AHPDST Algorithm in Vulnerability Potential Mapping The basic components of the developed GVPIbased AHPDST algorithm are the normalized weight (W) and mass function factors (MFFs) parameters (Bel, Dis, Unc and Pls), determined based on the applied theories of AHP and DST models, respectively. The summary results of the applied AHP and

DST models in assessing the area groundwater vulnerability potentiality are presented in Tables 3 and 4. In Table 4, the populated records in columns 7, 8, 9 and 10 were based on the applied DST-EBF model’s Eqs. 6–11 to the prepared GVCFs themes (Fig. 5) and the analyzed well location (Fig. 3). This entails considering each of the class boundaries of the GVCFs’ map sequentially. In a typical theme of the transverse resistance (TR) factor (Fig. 5d) where class boundary 3 was referenced, the number of observed location wells in the class = 4, total number of training observed location wells in the study area = 54, number of pixels in the class = 3949, total number pixels in the study area = 31,361 (Table 4 row 19), and the steps for the MFFs components values computation via the application of those DSTEBF equations are as follows: k Tp class3 ¼

4 54 39494 3136154

¼ 0:59

544 54 k Tp class3 ¼ 31361543949þ4 ¼ 1:06 3136154

K. A. Mogaji

The other k Tp for 1, 2, 4 and 5 classes were 1.30, 0.13, 0 and 0, respectively. P Therefore, kðT ÞTR ¼ 4:81 The Bel function was then calculated using Eq. 7 as: Bel ¼

0:59 ¼ 0:12 4:81

The values of k Tp for 1, 2, 4 and 5 classes were 0.30, 1.15, 1.02, and 1.00, respectively. Therefore, P k Tp TR ¼ 5:08 Dis ¼

1:06 ¼ 0:21 5:08

Using Eqs. 10 and 11, the other mass functions: Unc and Pls were calculated, thus, Unc ¼ 1 0:12 0:21 ¼ 0:67 Pls ¼ 1 0:21 ¼ 0:79 The computed mass functions results for the considered class are analyzed and marked in red font color as highlighted in row 19 of Table 4. Following this trend of DST-EBF model application, the MFFs component values for all other class boundaries characterizing the GVCFs’ thematic maps were computed (Table 4). To apply the computed MFFs’ component values, the templated grid point model in Fig. 7 was employed where the spatial attribute at each gridded points is scored or rated with the determined MFFs’ values depending on the observed class boundary ‘attribute towards groundwater vulnerability potentiality mapping interpretation (Column 3). Using the computed MFFs’ values as spatial map’s attributes’ scores/rates observed, Eq. 15, was applied to Fig. 7 in GIS environment to compute values for GVPIBel, GVPIDis, GVPIUnc and GVPIPls values for each grid location. The estimated GVPI-based mass function values for all the grids are presented in Table 5. Table 5 results were further analyzed via applying the GIS-based geostatistical kriging interpolation technique to produce the GVPI-based mass functions maps (Fig. 8a–d).

Pure Appl. Geophys.

4.3. The Mass Function Maps Produced Based on the GVPI-Based AHPDST Synthesis To gain excellent insight to evaluating the area groundwater vulnerability to pollution, mass functional series index maps were produced (Fig. 8). The produced maps were hydrologically interpreted adopting similar approach reported in the studies of Carranza and Hale (2002), Carranza et al. (2005), Pourghasemi and Beheshtirad (2015), Mogaji et al. (2016). The interpretation is such that the produced Fig. 8 maps were comparatively analyzed for decision-making process. For instance, the GVPI belief map (Fig. 8a) was compared with the GVPI disbelief map (Fig. 8b) and it was observed that zones with high GVPIBel values were covered with the zones characterized with low GVPIDis values and vice versa. In addition, the low GVPIUnc value zones (Fig. 8c) and high GVPIPls (Fig. 8d) value zones were also observed associated with zones of high GVPIBel (Fig. 8a). Among these mass function maps, the suitability of the GVPIBel map for decision making exercise has been substantiated by the interpreted information provided by the GVPIDis, GVPIUnc and GVPIPls maps (Lee et al. 2012; Al-Abadi 2015; Mogaji et al. 2016). Similarly, the quantitative results in Table 4 also established that the low Dis, the high Pls and the low Unc values estimate validated the computed high Bel values estimate (Column 7). Besides, the vulnerability potential interpretation degrees in Column (3) regarding each classified groundwater vulnerability conditioning hydrologic themes (Column 1) show good correlation with the sequential degrees of Bel values estimate down the trend of GVCFs’ maps class boundaries. Invariably, it was interpreted that zones with high GVPIBel attribute imply areas with high probabilities’ occurrences for high vulnerability and vice versa for areas with low GVPIBel attribute (Al-Abadi 2015). Additionally, the provided cautionary information based on the predicted GVPIUnc values (column 9 of Table 5) across the study area revealed strong evidences for the vulnerability occurences targeted (Lee et al. 2012; Althuwaynee et al. 2012). This unique Unc modeling feature of the developed GVPI-


Table 5 The computed groundwater vulnerability potential index (GVPI)-based AHPDST model results Grids Centers’ coordinates

The GVPI-based integrated mass functions values

Northings

Eastings

Bel

Dis

Unc

Pls

851,697 851,274 840,698 841,967 840,698 830,122 830,122 830,545 819,545 819,122 819,968 819,545 819,968 820,307 809,210 809,210 809,936 809,210 809,210 809,573 809,573 809,936 799,057 798,332 798,332 798,332 798,332 798,332 799,057 799,057 788,178 788,178 788,178 787,816 788,178 787,453 787,453 788,178 787,816 777,662 777,662 776,574 777,662 777,662 777,662 777,662 778,025 778,025 766,784 766,784 766,784 767,872 766,784 766,784

804,996 820,226 790,612 805,842 820,226 790,612 805,419 820,226 790,612 805,842 819,803 835,033 733,076 746,529 702,978 717,484 733,076 746,493 776,590 790,732 805,237 819,742 702,978 717,846 732,350 747,218 762,085 776,590 790,732 805,237 688,473 703,703 718,208 732,350 746,855 761,360 776,228 791,095 805,600 688,836 703,341 717,483 732,350 747,218 761,723 776,228 790,370 805,600 673,968 688,473 703,703 717,846 732,350 746,855

0.29 0.23 0.24 0.30 0.30 0.24 0.24 0.24 0.30 0.34 0.29 0.24 0.24 0.24 0.19 0.22 0.23 0.30 0.34 0.29 0.34 0.34 0.34 0.25 0.29 0.25 0.24 0.30 0.30 0.24 0.24 0.34 0.25 0.34 0.24 0.24 0.30 0.27 0.23 0.27 0.24 0.25 0.25 0.24 0.24 0.24 0.23 0.24 0.24 0.31 0.29 0.32 0.27 0.24

0.19 0.20 0.21 0.19 0.19 0.21 0.21 0.21 0.19 0.18 0.19 0.21 0.21 0.21 0.23 0.20 0.21 0.19 0.18 0.19 0.18 0.18 0.18 0.22 0.19 0.20 0.21 0.19 0.19 0.21 0.21 0.18 0.19 0.18 0.21 0.21 0.19 0.19 0.22 0.19 0.21 0.19 0.19 0.21 0.21 0.21 0.21 0.21 0.20 0.22 0.19 0.19 0.19 0.21

0.53 0.58 0.55 0.52 0.52 0.55 0.55 0.55 0.52 0.49 0.53 0.55 0.55 0.55 0.58 0.59 0.58 0.52 0.49 0.53 0.49 0.49 0.49 0.54 0.52 0.56 0.55 0.52 0.52 0.55 0.55 0.49 0.57 0.49 0.55 0.55 0.52 0.54 0.57 0.55 0.56 0.57 0.57 0.55 0.55 0.55 0.57 0.55 0.57 0.48 0.52 0.50 0.55 0.55

0.82 0.81 0.80 0.82 0.82 0.80 0.80 0.80 0.82 0.83 0.82 0.80 0.80 0.80 0.78 0.81 0.80 0.82 0.83 0.82 0.83 0.83 0.83 0.79 0.82 0.81 0.80 0.82 0.82 0.80 0.80 0.83 0.82 0.83 0.80 0.80 0.82 0.82 0.79 0.82 0.79 0.82 0.82 0.80 0.80 0.80 0.80 0.80 0.81 0.79 0.82 0.82 0.82 0.80

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Pure Appl. Geophys.

Table 5 continued Grids Centers’ coordinates

The GVPI-based integrated mass functions values

Northings

Eastings

Bel

Dis

Unc

Pls

767,147 768,597 766,784 767,146 757,356 755,905 756,268 756,268 755,412 756,630 745,026 745,026 745,389 746,114 745,752 745,752

761,723 776,590 790,732 804,875 673,968 688,836 703,341 717,846 732,924 791,095 659,101 673,968 688,836 703,703 717,483 731,988

0.24 0.24 0.24 0.27 0.24 0.28 0.29 0.16 0.35 0.24 0.25 0.24 0.30 0.33 0.20 0.14

0.21 0.21 0.21 0.19 0.20 0.20 0.19 0.20 0.18 0.21 0.20 0.20 0.19 0.19 0.19 0.20

0.55 0.55 0.55 0.54 0.57 0.53 0.52 0.65 0.48 0.55 0.56 0.56 0.51 0.49 0.62 0.66

0.80 0.80 0.80 0.82 0.81 0.81 0.82 0.81 0.83 0.80 0.81 0.81 0.82 0.82 0.82 0.81

based AHPDST index algorithm can offer greater level of confidence which according to Feizizadeh et al. (2014) will eliminate biasness in decisionmaking process. This submission is corroborated with the reports documented in the study of Mogaji and Lim (2017). Thus, the integrated map of degrees of Bel (Fig. 8a) is considered the most suitable decisionmaking model tool for the area groundwater vulnerability assessment. 4.3.1 Modeling of Groundwater Vulnerability Potential Zone (GVPZ) Map The computed GVPIBel values in column (3) of Table 5 were processed in GIS environment to produce the area groundwater vulnerability potential zone (GVPZ) map (Fig. 9). This is in agreement with the submissions of Nampak et al. (2014), Lee et al. (2012), Althuwaynee et al. (2012) and Mogaji et al. (2016). For the vulnerability potential zone demarcation, the appropriateness of the quantile classification method as applied by Razandi et al. (2015) and Rahmati et al. (2016) was employed. Table 6 presents the results of the vulnerable zones’ classification. Furthermore, the areal and percentage distribution of these predicted vulnerable zones were evaluated in GIS environment and established that the coexisting areas under VL, L, M, MH and H categories are 132 km2

(1.2%), 507.14 km2 (4.7%), 4442.55 km2 (41.7%), 3490.44 km2 (32.6%) and 2124.63 km2 (19.8%), respectively column 3 of Table 6. 4.4. GVPZ Map Validation 4.4.1 The Relative Operating Characteristics (ROC) Validation Approach The produced GVPZ (Fig. 9) was validated via employing the relative operating characteristics (ROC) technique. This was with the view to establishing the reliability of the GVPI-based AHPDST produced GVPZ map (Fig. 9) precision in environmental decision-making studies (Manap et al. 2011; Chung and Fabbri 2003). According to Akgun et al. (2012), Mohammady et al. (2012), Pourghasemi et al. (2013), ROC technique is highly efficient in examining the quality of deterministic, probabilistic detection and forecast system. This study carried out its validation employing the ROC technique via exploring the ROC module in IDRISI software environment. The procedures entail randomly partitioning of the area borehole location inventory (Fig. 1d) into two mutually exclusive groups, namely training data set 70% (54 borehole wells) and 30% as testing data set (24 borehole well) (Fig. 2). The ROC curve was constructed for quantitative prediction accuracy, and the AUC was then calculated. Figure 10 illustrates the ROC curve for the


Figure 8 The groundwater vulnerability potential index-based mass function maps generated from AHPDST model results: a belief; b disbelief; c uncertainty and d plausibility

predictive capability reliability assessment of the GVPIbased AHPDST produced GVPZ map. According to this figure, the evaluated success rate result explains how well the predictive map classified the area of existing training wells, while the determined prediction rate output established the measures of the developed model predictive performance (Al-Abadi et al. 2016). The said figure shows the AUC value of 0.777 and success rate of 0.823 which imply suitable prediction accuracy of 78% and success rate of 82% for the developed GVPI-based AHPDST data mining model (Table 6).

5. Conclusion and Future Work The modeling and prediction of groundwater vulnerability risk mapping are essential decision-

making schemes to be considered in optimizing groundwater resources uses viz-a-viz its quality status evaluation worldwide. A decision model map apt for predicting and mapping the spatial distribution of groundwater vulnerability potential locations viable for efficient groundwater resources management in an investigated area was produced in this study. The study conducted in the hard rock terrain (HRT), Nigeria involved the application of GISbased driven data mining models to the subsurface physical parameters obtained from the acquired and interpreted borehole and geophysical data. The embraced analytic hierarchy process (AHP) and Dempster–Shafer evidential belief function (DSEBF) data mining models were used to develop GVPI-based AHPDST modeling algorithm from the analysis of the derived geoelectrical-based

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Pure Appl. Geophys.

Figure 9 Groundwater vulnerability potential zone (GVPZ) map of the study area

Table 6 The vulnerability potential classifications and the areal coverage percentage GPVI classes

Vulnerable zones

Areal coverage km2 (%)

0.15–0.21 0.21–0.24 0.24–0.26 0.26–0.29 0.29–0.35

VLv Lv Mv MHv Hv

132 507.14 4442.55 3490.44 2124.63

(1.2) (4.7) (41.7) (32.6) (19.8)

VLv very low vulnerable, Lv low vulnerable, Mv moderately vulnerable, MHv moderately high vulnerable, Hv high vulnerable

groundwater vulnerability conditioning factors (GVCFs) consisting of recharge rate, traverse resistance, longitudinal conductance, hydraulic conductivity and transmissivity. The quantitative mechanism of the developed model algorithm was applied to multi-criterially synthesized the prepared GVCFs’ thematic themes in GIS environment for the establishment of diverse groundwater vulnerability potential index (GVPI) mass function/belief modeling algorithms such as GVPIBel, GVPIDis, GVPIUnc and GVPIPls. The estimates of the applied

GVPIBel modeling algorithm were processed in GIS environment to produce the area groundwater vulnerability potential zones (GVPZ) map. Based on the produced GVPZ map, the areal extent for the lows/moderate and high vulnerable zones is 48 and 52%, respectively, and this implies that the aquifer system in the study area is vulnerable. For the validation of the GVPZ map, the relative operating characteristic (ROC) technique was used. The validation results indicated that the developed GVPIbased AHPDST model algorithm has very good


TETFUND scholarship for Academic Staff Training and Development intervention. The author expresses appreciation to the management of Federal University of Technology Akure, Ondo state, Nigeria for supports. The author also thank the associate editor and anonymous reviewers for their comments, which improved the quality of the manuscript significantly. REFERENCES

Figure 10 The ROC technique success and prediction rate curve for groundwater vulnerability potential prediction index map

capability for predicting groundwater vulnerability potential zones with a model accuracy of 78% and success rate of 82%. The groundwater vulnerability potential zone map produced by this study can provide valuable information for hydrogeologist, planners, and decision makers to put suitable plans for managing groundwater in the study area. Furthermore, the developed AHPDST vulnerability index model algorithm allows not only the predictive mapping of groundwater vulnerable zones but also the modeling of the degrees of uncertainty in the prediction output which may be transferable to other districts with similar topographic and hydrogeological characteristics. For consequent research work, the use of the nitrate contamination index record can better enhance the proficiency of this model in mapping intrinsic and specific groundwater vulnerability of an area. The developed model can also be adopted in other studies, such as geothermal potential mapping, geo-hazard potential mapping and other fields of groundwater hydrology where challenges of making accurate and reliable decision from set of multiple criteria are faced.

Acknowledgements The author would like to acknowledge Tertiary Education Trust Fund for funding this research under the auspices of Federal Republic of Nigeria

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K. A. Mogaji

Pure Appl. Geophys.

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(Received March 2, 2016, revised January 22, 2017, accepted February 9, 2017)

Development of AHPDST Vulnerability Indexing ...

Development of AHPDST Vulnerability Indexing ...

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