Engineering Applications

International Journal on

Engineering Applications (IREA)

Copyright © 2015 Praise Worthy Prize S.r.l. - All rights reserved

Contents Rainfall Prediction in Semi-Arid Regions in Jordan Using Back Propagation Neural Networks by Bilal Zahran, Abdelwadood Mesleh, Mohammed Matouq, Omar Al-Heyasat, Tariq Alwada’n

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Analytical Study on Natural Draught Cooling Tower by FEM Analysis and Piling Analysis Using Rivet Theory by N. V. Vamsee Krishna, K. Rama Mohana Rao

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Logging Airplane Sensors Measurements to a Remote Database Using LabVIEW and FieldPoint by M. Lascu

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Parameters Identification of a Nonlinear System Based on Genetic Algorithms with an Optimized Cost Function by A. C. Megherbi, H. Megherbi, K. Benmahamed, A. G. Aissaoui, A. Tahour

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International Journal on Engineering Applications (I.R.E.A.), Vol. 3, N. 6 ISSN 2281-2881 November 2015

Rainfall Prediction in Semi-Arid Regions in Jordan Using Back Propagation Neural Networks Bilal Zahran1, Abdelwadood Mesleh1, Mohammed Matouq1, Omar Al-Heyasat2, Tariq Alwada'n3 Abstract – In Jordan, agriculture and irrigation depend highly on rainfalls. Rainfall prediction is a challenging area of investigation for scientists. In this paper, a precipitation prediction model using artificial neural networks (ANNs) is proposed. The seasonal amount of rainfall in several areas in Jordan is predicted using rainfall rate time-series data, these rainfall rate data has been recorded from 26 stations located in different areas in Jordan. A feed forward ANN based on backpropagation (BP) is designed and trained to predict the future rainfalls in Jordan. Results are encouraging and accurate for rainfall prediction in Jordan. Copyright © 2015 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Precipitation, Seasonal Rainfall, Back Propagation, Neural Networks, Climate Change, Jordan

ANNs consist of computational elements called neurons, operating in parallel and connected by links with variable weights which are typically adapted during the learning process. ANNs are trained using a BP algorithm that provides a way to calculate the gradient of the error function efficiently using the chain rule of differentiation, moreover, the weights are tuned along the negative gradient of the performance function [3], [4]. ANNs are used widely in classification and prediction problems in several areas [5], [6]. The rest of this paper is organized as follows: Section 2 presents some of the related work, Section 3 presents the proposed prediction algorithm using ANNs model, Section 4 discusses the results, and finally, Section 5 concludes the paper.

Nomenclature ANN BP PE PA

Artificial Neural Network Backpropagation Prediction Error Prediction Accuracy

I.

Introduction

Precipitation is the main source of water in Jordan. Jordan is regarded as a poor country in water resources and thus depends highly on rainfall. It is known that over half of Jordan is covered by an Arabian desert (see Fig. 1), however, the western part is arable lands. The climate in Jordan is semi-dry in summer and relatively cold in winter. The western part of Jordan receives greater precipitation during the winter season from November to April. The weather is humid from November to March and semi dry for the rest of the year. With hot, dry summers and cold winters during which practically all of the precipitation occurs, Jordan has a Mediterranean-style climate. Most of the land receives less than 620 mm (24.4 in) of rain a year; as a result, Jordan can be classified as a semi dry region. In the highlands east of Jordan valley, precipitation increases to around 300 mm (11.8 in) in the south and 500 mm (19.7 in) or more in the north [1], [2]. Fig. 1 shows the average monthly rainfall from Jordan at Amman from 1990 to 2012, moreover, Fig. 2 shows the average annual precipitation in Jordan. ANNs are non-linear mapping structures that are inspired by the function of the human brain and are considered powerful modeling tools especially for data with unknown underlying relationships.

II.

Literature Review

Numerous previous studies have worked in rainfall prediction, this section presents some of them, focusing on the usage of neural networks: Nanda, et al. [7] developed prediction models using time series data, these models include ARIMA (1,1,1), multilayer perceptron (MLP), Functional-link ANNs (FLANNs) and Legendre polynomial equation (LPE) and found that FLANN gives better prediction results as compared to ARIMA model. Kumar, A., et al. [8] studied the possibility of predicting average rainfall over Udupi district of Karnataka, in India, and analyzed the data through ANNs models. Hung et al. [9] developed ANNs model that being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. They aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information.


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III. The Proposed Anns Based on n BP BP Algorithm A ANNs NNs are non non-linear linear mapping structures based on tthe he function of the human brain. They are powerful tools for modeling, especially when the underlying data relationship is unknown. ANN NN models in recent years were widely used in prediction problems for weather forecasti forecasting ng [[7]--[12 12]. ANNs ANNs consist of many computational elements called neurons, operating in parallel, connected by links with variable weights which are typically adapted during the learning process [3 [3], [4]. 4]. Feed forward multilayer networks trained with the BP algorithm are still the most common kind today. The proposed prediction algorithm is based on ANNs trained by BP algorithm; it compromises the following main steps (see Fig. 3). 3)

Fig. Fig 1. Average monthly rainfall (in mm) from Jordan at Amm Amman an 1990-2012, 1990 2012, Source World Bank

Step 1: Rainfall data pre-processing pre processing processing:: Old rainfall data were collected from 26 stations all over Jordan from 1977 and 2008 [1 [1],, [2], 2], this paper uses the rainfall data collected from 4 stations out of the 26 mentioned stations, these four stations were used because they comes from different areas of Jordan: Amman (Middle area of Jord Jordan), an), Irbid (north area of Jordan), Rawaished (East area of Jordan), an and d Ghour (West area of Jordan ). Table I shows the seasonal rainfall rates collected from November to April 1977 1977-2008. 2008. To facilitate the prediction process using ANNs, the following pre pre-processing processing steps are applied: (i) Missing data manipulation manipulation:: Rainfall missing data are cleaned by filling in the missing values with the mean values. (ii) Data normalization: normalization: The rainfall data are normalized in the range (0.0 to 1.0).

Fig. 2. Average Average annual precipitation in Jordan, source fao.org

Their preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall prediction. Matouq et al. [[10 10]] analyzed the rainfall and temperatures of Jordan from 1979 to 2008, simulated them using geographic information systems and MATLAB and converted them to geographical maps. They studied the annual mean maximum temperature, annual mean minimum temperature, and mean annual rainfall during mentioned period, they forecasted rainfalls from 2009 to 2018 2018. Ghanem [[11 11]] analyzed the annual and monthly rainfalls in October and November for about 50 years in Jordan. He used standard deviation values to predict annual rainfall (less or more than the normal) depending on the mentioned rainfall data and found that the annual rainfall exceeded the normal in some areas, moreover, his regression analysis projected weak increasing trends in November and October and decreasing trends in the annual rainfall in the majority of Jordanian areas. El-Shafie Shafie at al. [[12 12]] implemented ANNs model and multi-Regression multiRegression model to predict the rainfall on yearly and monthly basis in Alexandria, Egypt and concluded that ANNs model performed bett better er that the multi multiregression model. In this paper, Seasonal rainfalls in several areas of Jordan are predicted using rainfall rate time time--series series data using ANNs trained by BP algorithm.

Rainfall data pre-processing pre processing

ANNs model construction

ANN NNs model training and parameters tuning

ANNs model testing

Prediction of Un-seen Un seen rainfall data

Fig. 3. The proposed proposed rainfall prediction using model using ng ANNs trained by BP algorithm

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This tuning process used a validation set of the rainfall data selected from Amman station, i.e. the rainfall data from 1977 to 1983 as training data and the rainfall of 1984 as target predicted data. The best ANNs model with the suitable number of nodes is selected accordance to the minimum forecast error.

TABLE I THE ANNUAL SEASONAL RAINFALL RATES (IN MM) Year Amman Irbid Rawaished Ghour 1977 257.8 273.7 71.00 35.40 1978 184.6 315.7 16.60 25.10 1979 311.9 476.1 36.20 56.90 1980 428.0 545.7 78.50 107.2 1981 142.1 332.4 29.70 43.00 1982 222.4 397.2 135.0 70.00 1983 378.9 558.9 36.70 66.80 1984 220.2 509.6 56.70 28.70 1985 251.4 349.4 69.00 97.30 1986 232.0 600.2 66.60 83.30 1987 178.6 383.7 47.50 58.70 1988 389.0 557.4 150.1 106.1 1989 126.0 241.3 47.90 59.10 1990 223.0 393.7 75.00 90.20 1991 394.4 642.8 74.80 121.6 1992 248.1 847.6 87.00 75.80 1993 174.4 208.6 48.6 74.30 1994 332.3 464.5 88.00 129.7 1995 97.90 245.7 71.20 33.90 1996 225.1 355.2 92.20 63.20 1997 277.0 351.5 95.90 91.30 1998 176.2 394.2 56.40 67.35 1999 109.4 214.4 21.9 49.90 2000 222.4 424.7 35.6 40.80 2001 196.0 282.2 84.9 42.10 2002 309.8 598.6 76.00 70.80 2003 325.3 730.6 98.80 57.90 2004 177.8 417.9 62.70 88.50 2005 223.8 428.7 75.90 65.50 2006 184.8 325.4 68.3 59.00 2007 210.2 321.4 77.9 44.90 2008 174.1 275.8 47.2 32.70

Step 4: ANNs model testing: One third of the rainfall rate data are used to test the accuracy of the proposed prediction model. Step 5: prediction of future rainfall data: After training and tuning the proposed prediction algorithm, it can be used to predict future rainfall data.

IV.

Results

The selected rainfalls from the mentioned 4 stations are used to train, and test the prediction algorithm. After tuning the neural networks parameters, such as the number of layers, number of neurons in each layer and the learning rate, the proposed model is used to predict the rainfalls from 1998 to 2008. Results are recorded, and the prediction errors are calculated using the following error equation: =

|

−

|

(1)

where is the prediction error, is the actual rainfall value, is the predicted rainfall value, and || is the absolute value. Moreover, the prediction accuracy is defined as follows:

Step 2: ANNs model construction: The processed seasonal rainfall data are used to construct the ANNs model. Accordingly, these data are used to train and test the ANNs rainfall prediction model. Two thirds of the data are selected to train the model and the other third is used to test it. Investigating the seasonal rainfall rates in the four stations (see Table I) leads to the usage of ANNs model. It is clear that the rainfall rates do not follow any specific pattern. As a result, simple prediction methods such as linear regression are not applicable. The proposed prediction algorithm is constructed. It uses 3 layers: an input layer, a single hidden layer and an output layer. It uses sigmoid activation function and initializes the weights randomly.

= (1 −

) × 100%

(2)

where is the prediction accuracy. Tables II, III, IV and V show the prediction results in Amman, Irbed, Ghour and Rwished respectively.

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Step 3: ANNs model training and parameter tuning: In this paper, the proposed ANNs model performs time series predictions using sliding window technique [13]. It uses a set of N-tuple inputs and a single output as a target of the ANNs. In the proposed 3-layer neural network, the number of nodes in the input layer is set to 7, the number of nodes in the hidden layer is varied from 1 to 10 and the learning rate is varied from 0.1 to 0.9, (the number of neurons of the hidden layer is set to 5), the learning rate is set to 0.5, and the number of neurons in the output layer is set to 1, as a result, this proposed ANNs model achieves the best performance.

TABLE II PREDICTION ERROR IN AMMAN STATION Actual Value Predicted Value Prediction Error 176.2 200.5 0.137 109.4 131.6 0.202 222.4 251.5 0.130 196 215 0.096 309.8 352.4 0.137 325.3 302 0.071 177.8 203.4 0.143 223.8 182.6 0.184 184.8 210.5 0.139 210.2 152.3 0.275 174.1 163.5 0.060

The average prediction error is 14.37% (Average accuracy is 85.63%), 13.74 (Average accuracy is 86.26), 22.26 (Average accuracy is 77.73) and 21.00 (Average accuracy is 79.00) in Amman, Irbed, Ghour and Rwished respectively. It is clear that the proposed prediction algorithm can successfully predict seasonal rainfalls in Jordan even


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when the rainfall rates are fluctuated. Furthermore, the proposed method outperforms other algorithms usually used in prediction problem such as fuzzy system, linear regression and Bayes' theorem. More experiments were conducted to predict the rainfall data of Amman station using linear regression, however, the rainfall data are changing accidently (See Fig. 4), therefore, linear regression is not applicable (See Fig. 5). It is clear from Fig. 5 that the data is not fitting the regression line.

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

TABLE IV PREDICTION ERROR IN GHOUR STATION Actual Value Predicted Value Prediction Error 67.35 95.3 0.414 49.9 70.6 0.414 40.8 30.6 0.250 42.1 55.6 0.320 70.8 62.5 0.117 57.9 60.1 0.037 88.5 105.3 0.189 65.5 82.6 0.261 59 70.4 0.193 44.9 37.4 0.167 32.7 35.4 0.082

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

TABLE V PREDICTION ERROR IN RWISHED STATION Actual Value Predicted Value Prediction Error 56.4 30.8 0.453 21.9 25.3 0.155 35.6 40 0.123 84.9 60 0.293 76 80.7 0.061 98.8 87.6 0.113 62.7 42.1 0.328 75.9 77.8 0.025 68.3 36.8 0.461 77.9 92.4 0.186 47.2 52.3 0.108

V.

Fig. 4. Rainfall rates in Amman station.

Conclusion

In this paper, a rainfall prediction ANNs model is proposed, it predicts the seasonal rainfalls of different areas in Jordan. The accuracy of the predicted results varied from 86.26 % to 77.73 %. In future, a combination of meteorological parameters such as relative humidity, air pressure, temperature, and cloudiness, along with rainfall data shall be used in the prediction model.

References [1] [2] [3] [4] [5]

Department of Meteorology in Jordan: www.jometeo.gov.jo. Royal Jordanian geographic center: www.rjgc.gov.jo. Mitchell, T., Machine Learning, McGraw-Hill, 1997. S. Haykin. Neural networks and learning machines. Pearson 2008. Zahran, B., Classification of Brain Tumor Using Neural Network, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 673-678. [6] Derras, B., Peak Ground Acceleration Prediction Using Artificial Neural Networks Approach: Application to the Kik-Net Data, (2014) International Journal of Earthquake Engineering and Hazard Mitigation (IREHM), 2 (4), pp. 144-153. [7] Nanda, S.K., Tripathy, D.P., Nayak, S.K., Mohapatra, S., Prediction of Rainfall in India using Artificial Neural Network (ANN) Models, International Journal of Intelligent Systems and Applications, vol. 12, pp. 1-22, 2013. [8] Kumar, A., Kumar, A., Ranjan, R. and Kumar, S. A rainfall prediction model using artificial neural network, IEEE Control and System Graduate Research Colloquium (ICSGRC), pp. 82-87, 2012. [9] Hung, N., Babel, M., Weesakul, S., Tripathi, N., An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, vol. 13, pp. 1413–1425, 2009. [10] Matouq, M., El-Hasan, T., Al-Bilbisi, H., Abdelhadi, M., Hindiyeh, M., Eslamian, S., Duheisat, S., The climate change implication on Jordan: A case study using GIS and Artificial

Fig. 5. Regression for Amman rainfall data

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

TABLE III PREDICTION ERROR IN IRBED STATION Actual Value Predicted Value Prediction Error 1998 394.2 0.339 1999 214.4 0.027 2000 424.7 0.165 2001 282.2 0.046 2002 598.6 0.087 2003 730.6 0.078 2004 417.9 0.245 2005 428.7 0.006 2006 325.4 0.260 2007 321.4 0.127 2008 275.8 0.127


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Neural Networks for weather forecasting, Journal of Taibah University for Science, vol. 7, Issue 2, pp. 44 44-55, 55, 2013. [11 11] Ghanem, A., Case Study: Trends and Early Prediction of Rainfall in Jordan, American Journal of Climate Change Change,, vol. 2, pp. 203 203208, 2013. [12 12] El El-Shafie, Shafie, A., El El-Shafie, Shafie, A, El Mazoghi, H., Shehata, A., Taha, M., Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt, International Journal of the Physical Sciences Sciences,, vol. 6, no. 6, pp. 1306 1306-1316, 1316, 2011. [13 13] Gershenfeld N.A., Weigend, A.S., The Future of Time Series, In Time Series Prediction: Forecasting the Future and Understanding the Past, Weigen, A.S. and Gershenfeld A.N. eds. Reading, MA: Addiso Wesley, pp 11--70, Addison-Wesley, 70, 1993.

Mohammed Matouq received the B.Sc degree in Chemical Eng. from University of Jordan, Jordan, in 1987 1987,, the M.Sc degree in Biotechnology from University of Jordan, Jordan, in 199 1990,, and the PhD degree in Chemical Eng. from Nagoya University, University Japan apan,, in 1994. 1994. He is currently working as Professor at department of C Chemical hemical Engineering, Faculty of Engineering Technology, Al Al--Balqa’ Balqa’ Applied University, Jordan. Al-Balqa Applied University / Faculty of Engineering Technology. Department Department of C Chemical hemical Engineering Engineering. P.O.Box 15008 Amman 11134 Jordan Jordan. E-mail: E mail: [email protected] Omar AlAl-Heyasat Heyasat is an Associate professor at the Computer Engineering Department at the Faculty of Engineering, Al Al--Balq’a Balq’a Applied University. He received his PhD in Computer Engineering from Vinnitsia National Technical University, Vinnitsia, Ukraine, His research interests include social networks analysis, artificial intelligence, image and signal processing, CPU and GPU Al-Balqa Al Balqa Applied Unive University rsity / Faculty of Engineering, Department of Computer Engineering Engineering,, Al-Salt Al Salt 19117 Jordan Jordan. E-mail: E mail: [email protected]

Authors’ information nformation 1

Computer Engineering Department, Faculty of Engineering Technology, AlAl-Balqa Balqa Applied University, Amman, Jordan. 2 Computer Engineering Department, Engineering College, Al Al--Balqa Balqa Applied University, Salt, Jordan. 3 Computer Science Department, Faculty of Information Technology, The World Islamic Sciences and Education University, Amman, Jordan.

Tariq Alwada’n received the B.S degree in Computer Engineering from Al Al-Balqa Balqa Applied University, Jordan, in 2005, the M.S degree in Computer and Information Networks from University of Essex, United Kingdom, in 2007 and the PhD degree in Computer Science from De Montfor Montfortt University, United Kingdom, in 2012. He is currently working as an Assistant Professor at department of Computer Science, Faculty of Information Technology, The World Islamic Sciences and Education University, Jordan. His research interests include Cloud Computing, Grid Computing, Security, Security Management and Wireless Networks. The World Islamic Sciences and Education University, University, Faculty of Information Technology. Department of Computer Science Science, P.O. Box 1101 Amman 11947 Jordan Jordan. Tel: 00962 772329461. E-mail: E mail: [email protected]

Bilal Zahran received the B.Sc degree in Electrical & Electronic Eng. from Middle East Technical University, Turkey, in 1996, the M.Sc degree in Communications Eng. from University of Jordan, Jordan, in 1999, and the PhD degree in Computer Information System (CIS) from Arab Academy for Banking and Financial Financial Sciences, Jordan, in 2009. He is currently working as an Assistant Professor at department of Computer Engineering, Faculty of Engineering Technology, Al Al-Balqa’ Balqa’ Applied University, Jordan. His research interests include machine learning, data mining and optimization fields. Al Balqa Applied University / Al-Balqa Faculty of Engineering Technology. Department of Computer Engineering Engineering, P.O.Box 15008 Amman 11134 Jordan Jordan. Tel 00962 785284732 Tel: E-mail mailss: [email protected] [email protected] Abdelwadood Mesleh is an Associate Professor at the Computer Engineering Department at the Faculty of Engineering Technology – Al-Balqa' Balqa' Applied University. He received his Ph.D. in Computer Information Systems from The Arab Academy for Banking and Financial Sciences in 2008 and his BSc & MSc in Computer Engineering from Shanghai University, Shanghai in 1995 and 1998 respectively. His research interests include Optimization, Fuzzy logic, GA, ACO, Arabic N Natural atural Language Processing, Information Retrieval, Data Mining, Feature Subset Selection, Arabic Speech Recognition, MANETs, Parallel Processing & R-Mesh, Mesh, Cryptanalysis, Control, Medical Image and Signal Processing, Operating Systems, CPU and GPU schedulin scheduling. g. He is currently on sabbatical leave at Al Ahliyya Amman University. Al Ahliyya Amman University / Faculty of Engineering Department of Computer Engineering E-mail mailss: [email protected] [email protected]

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