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International Review on Computers and Software (I.RE.CO.S.), Vol. xx, n. x

Optimization of Land Suitability for Food Crops using Neural Network and Swarm Optimization Algorithm Rita Layona1, Suharjito2 Abstract – Land quality and suitability are factors that affect productivity. Determining land suitability is important because if we choose inappropriate or unproductive land, it can cause lack of productivity. One way to evaluate and determine the land suitability is to analyze land fitness based on land data to obtain prediction of production for each area. This prediction can be used to predict suitable land for crops. This study proposes the implementation of Neural Network and Swarm Optimization Algorithm (Particle Swarm Optimization & Cat Swarm Optimization) to obtain the prediction of production in order to determine the optimal land suitability. To evaluate the proposed method, this study performed comparative evaluation based on the Mean Square Error (MSE) and accuracy in determining land suitability for each method. Based on the experimental results, Cat Swarm Optimization produces minimum error equal to 0.00439 for training phase and 0.10453 for testing phase. In training phase, Cat Swarm Optimization produces higher accuracy than Particle Swarm Optimization. The accuracy rate of Cat Swarm Optimization is equal to 93% in training phase. In testing phase, Particle Swarm Optimization and Swarm Optimization Cat produce same accuracy rate of 67%.

Keywords: Neural Network, Swarm Optimization Algorithm, Particle Swarm Optimization, Cat Swarm Optimization, Land Suitability

I.

Introduction

Cultivation of food crops often have problems, one of these obstacles is to determine whether a particular land is suitable for use in food crops. Land quality and land suitability are some factors that affect productivity [1]. Determining land suitability is important because if we choose inappropriate land or choose unproductive land, it can cause lack of productivity, loss of time and loss of financial. To overcome this problem, it requires a way to determine crop land suitability. Land quality is usually evaluated and matched for a specific use [2]. One way to evaluate and determine land suitability for food crops is to analyze the suitability of land based on land data and obtain predictions of production for each region [3]. Determining crops land suitability can be done by using Fuzzy Logic, Neural Networks or other models. Previous studies on the determination of the suitability of land for food crops have been done by using Fuzzy Logic [4]. Fuzzy Logic has also been used to evaluate the suitability of land for wheat in the area Ziaran [5]. Expert systems suitability of agricultural land has also been made for the cultivation of fruits by using Artificial Intelligence forward chaining [6]. Neural Network is used in another research study to evaluate land suitability for construction [7]. Neural Network also been used to predict the production of palm oil based parameters determine the suitability of land for oil palm land [8]. To get the minimum error in determining land suitability required an optimization. There are several

Manuscript received January 2007, revised January 2007

optimization algorithms that have been implemented in different problems such Berth Allocation problem using Particle Swarm Optimization Algorithm [9]. A similar optimization algorithm was used for forecasting the stock index [10]. Additionally, prediction accuracy of Neural Network can be improved by using Particle Swarm Optimization (PSO). If compared with Backpropagation algorithm and Genetic Algorithm, Particle Swarm Optimization show higher accuracy [11]. In 2013, Cat Swarm Optimization Algorithm was used for classification using Iris data and Breast Cancer data [12]. Neural Network can be optimized by using Swarm Optimization Algorithm. Swarm Optimization Algorithm can solve several different problems with better results when compared with other algorithms [12]. On this basis, Neural Network and Swarm Optimization Algorithm are used to optimize the crops land suitability determination.

II.

Related Work

Previous studies on land suitability have been done by using fuzzy logic to determine the suitability of land by the biggest limiting factor for crops in East Java province. In this study, the method used is Fuzzy Inference Systems (FIS). Some examples of the parameters used in this study is data fuzzy like temperature, texture, drainage, and slope as well as data on non-fuzzy such as high rainfall and groundwater [4]. Moreover, fuzzy set theory has also been used to evaluate the suitability of land for wheat crop in the region Ziaran in Qazvin province, Iran. Some examples of soil parameters measured in this study are texture,

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R. Layona, Suharjito

Organic Carbon (OC), bulk density (Bd), moisture contents, water saturation percentage (SP), and pH [5]. Other studies have been done by Susanti & Winiarti that makes expert system of agricultural land suitability for cultivation of fruits by using the search method Forward Chaining fact which is a method that searches for conclusions based on the facts and then tested the hypothesis. The input data used in this research is data fruit crops, land facts, disease, and how to planting. [6] Subsequently in 2009, Neural Network also been used to predict the production of palm oil to determine the suitability of land for oil palm land. In this study, Backpropagation used to predict the amount of palm oil production using the data input form field parameters such as rainfall, slope, and soil acidity. From this research, it is said that the Neural Network provide accurate, fast and minimize errors. [8] In 2010, another study on land suitability has been done by using Back Propagation Neural Network to evaluate land suitability for construction based on land condition analysis. This study said that Back Propagation Neural Network can provide a solution land suitability problem in Hangzhou [7]. In 2013, RBF Neural Network and MLP (Multi-Layer Perceptron Networks) is used for forecasting the suitability of land in Eghlid, Iran. The results of these studies found that the RBF and MLP is able to predict the land suitability classes with a good estimate, but accuracy using MLP neural network is larger than that using RBF Neural Network. [13] In 2008, Neural Networks have been compared with other conventional methods such as Linear Regression, and found that Neural Network model produces better accuracy. [14] Some related research about Particle Swarm Algorithm has been implemented to solve some problems with better results when compared with other optimization algorithms. An example is the research conducted for forecasting the stock index. [10] In 2007, Backpropagation algorithm (BP) compared with Particle Swarm Optimization (PSO) and BP-PSO for data Iris. From these results, it was found that the PSO algorithm and BP-PSO get better results than BP algorithm. But if the BP-PSO and PSO are compared, BP-PSO produces better results than PSO. [15] In 2011, research use Neural Network and Particle Swarm Optimization for data Cardiotocography. This study compared Back Propagation Neural Network (NNBP) with Neural Network Particle Swarm Optimization (NNPSO). [16] In 2013, PSO is proposed to be used to improve Neural Network’s performance in Iris data. PSO algorithm has strong ability to find the global optimum. Instead, Backpropagation algorithm has strong ability to find a local optimum results but weak to determine the global optimum. From this research, PSO algorithm is proposed to be used to improve Neural Network’s performance. [17]


In the same year, Particle Swarm Optimization been applied to select the initial weight on Backpropagation Neural Network in pattern recognition case to obtain better performance when compared to the Backpropagation without optimization [18] Then in 2014, a similar study was also conducted to predict the rate of inflation by using Particle Swarm Optimization for choosing the initial weight of Backpropagation Neural Network. [19] Moreover, it is mentioned that Neural Network can be optimized by optimization algorithms such as Cat Optimization Algorithm. An optimal Brain Damage (OBD) method has been used to obtain effective neural network training [12]

III. Swarm Intelligence & Neural Network III.1. Particle Swarm Optimization Particle Swarm Optimization (PSO) is an optimization method that is included in the family of Swarm Intelligence. Particle Swarm Optimization was a technique introduced by Eberhart and Kennedy that is inspired by the social behavior of flocking birds. [20]. Particle Swarm Optimization is a simple model of the theory of evolution is based on the principles of the flock of birds, fish, or a swarm of bees that are looking for a source of food which the initial location of the source is unknown. However, they will eventually reach the best location of food sources by communicating each other. [17] Particle Swarm Optimization Algorithm is an optimization algorithm based on the concept of social interaction of a group of birds that simulate the movement of birds that are not predictable. Simulation algorithms aimed to find patterns / pattern of movement of birds that can change suddenly - arrived later make optimal formation. [21]. First, Particle that will be used to solve the problem is determined. Each particle has a position, velocity and the best solution. Then the process of changing velocity, calculate particle movement, if the obtained solution is better than the best solution is replaced with the solution. If not, then there is no change to the global best solution. Repeat the steps until the stop condition is met. [22] In Particle Swarm Optimization, there are three parts of stages / main steps: evaluate the fitness value of each particle, update the fitness value and the position of the best individual / commonly called the global best, and update velocity & position of each particle. [23] Here are the steps of the original Particle Swarm Optimization : [22]: 1. Determine the number of particles that will be used to resolve the problem. Each particle has a position, velocity and best solution. f(Pki ) ≤ f(Pki−1 ) ≤ ⋯ ≤ f(Pk1 ) (1) 2. The process of changing the velocity can be seen by calculating: International Review on Computers and Software, Vol. xx, n. x


Vki+1 = Vki + C1 . r1 (Pki − Xki ) + C2 . r2 . (Gi − Xki )

(2) Particle movement can be seen by calculating: Xki+1 = Xki + Vki+1 , i = 0,1, … , M − 1, (3) M is the particle size −Vmax ≤ Vki+1 ≤ Vmax (Vmax is the maximum velocity) r1 and r2 is a random variable,for example 0 ≤ r1, r2 ≤ 1 4. If a better solution obtained from Gi, then Gi will be replaced with the solution. If not, then there is no change to the global best solution. 5. Repeat step 1 to 4, until the stop condition is met. In 1998, Shi and Eberhart were introducing Inertia Weight concept by using the equation [24] Vki+1 = w ∗ Vki + C1 . r1 (Pki − Xki ) + C2 . r2 . (Gi − Xki ). (4) There are some rules that used to determine the value of C1 and C2 . Kennedy & Eberhart suggested the use of a fixed value of 2.0 for C1 and C2 [20]. But in 2000, Eberhart and Shi suggested the use value of 1.49618 for C1 and C2 [25]. In some paper, said that the swarm size does not affect the performance of Particle Swarm Optimization [24]. Then, it is said also that by using fewer number of smaller particle, will reduce the number of fitness function evaluations are evaluated. So the effect on computing cost [26]. 3.

b.

III.2. Cat Swarm Optimization Cat Swarm Optimization (CSO) is a new algorithm in optimization techniques that mimic the behavior of cats proposed by Chu and Tsai. Cat Swarm Optimization has advantages in settlement optimization problem if compared with previous algorithms, namely Particle Swarm Optimization. Cat Swarm Optimization can provide better performance when compared with Particle Swarm Optimization. [27] Based on observations of the behavior of a cat, there are two major behavior that can be modeled are seeking mode and tracking mode. The merger between the two modes can provide better performance [22]. Here is a Cat Swarm Optimization algorithm: a. The Presentation of Solution Set In optimization algorithm, solution set (result) is displayed in a specific way. For example, chromosome is used to represent the solution set of a Genetic Algorithm. Another example is in the Ant Colony Optimization (ACO), ants (ant) simulated as Agent and the path formed by ants represent the solution set. Cat Swarm Optimization will use the cat (cat) and the modeling of the behavior of the cat (models of cat behavior) to solve optimization problems. Cat Swarm Optimization will determine how many cats that will be used in the iteration. These cats will be used to solve the problem. Each cat will have position, velocity, fitness value and flag that indicate whether the cat was on seeking mode or tracking mode. The final solution is the best position


from one of the cat. Cat Swarm Optimization will save the best solution until the iteration ends. Rest and Alert - Seeking Mode In this mode, cat is in rest mode, look around, and look for the next position to move. At this stage, there are four important variables / factors that defined are: 1. SMP : seeking memory pool SMP is a variable that is used to define the size of the search memory for each Cat that indicates the points that have been passed by the Cat. Cat will then select a point of memory groups based on certain rules. 2. SRD : seeking range of the selected dimension SRD is a variable that is declared in the movement of the selected dimension. In seeking mode, if the dimensions decided to move, the difference between the new with the old value does not exceed the range defined by the SRD. 3. CDC : counts of dimension to change CDC is a variable that shows how large dimensions will change. 4. SPC : self-position considering SPC is a Boolean variable (true / false), to indicate whether a point that once the position of the Cat will be a candidate for the position moves. Here are steps in Seeking Mode [12] : 1. Make a copy of the position of the cat as much j, where j = Seeking Memory Pool (SMP). If Seeking Memory Pool is true, then j = SMP - 1, after that, it will maintain its current position to be one candidate. 2. For each cat position’s copy, add or subtract SRD percent of the current value at random and change the value. This change is based on the CDC. 3. Calculate fitness value (FS) for all candidates. If all the fitness value is not the same, then the probability calculation for each selected candidate point using the equation: |FSi − FSb | Pi = , where 0 < i < j (5) FSmax − FSmin

If same then set the selected probability for all points into 1. Randomly select a point to move from the candidates and move the position of the Cat k |FSi − FSb | Pi = , where 0 < i < j (6) FSmax − FSmin

c.

If the goal of the fitness function is to find the minimal solution: FSb = FSmax . If the goal is to find a solution maximum function then: FSb = FSmin . Tracking Mode Tracing mode is a condition that describes the state when the Cat is following the footsteps target. When the Cat is in tracking mode, then the Cat will move with velocities for each dimension. Here is a step in tracking mode: International Review on Computers and Software, Vol. xx, n. x


1.

Update the velocities for each dimension (vk,d) based on the following equation: vk,d = vk,d + r1 . c1 . (xbest,d − xk,d ) (7) Where d = 1,2,…,M 2. If the new velocities exceed max velocities, then set the value to max value. 3. Change the position of cat k in accordance with the following equation: xk,d = xk,d + vk,d (8) d. Core description of cat swarm optimization Cat Swarm Optimization algorithm is based on the behavior of cats. If the observed behavior of the cat, it is known that cats spend most of his time to rest and change position slowly and carefully and sometimes right in place. This behavior is called seeking mode. Cat's behavior to achieve the target is called the tracking mode. To combine both modes in the algorithm, it is defined mixture ratio (MR). MR should be of little value to ensure Cat spends most of his time in seeking mode. The general process in Cat Swarm Optimization: 1. Generate N Cat 2. Spread Cat randomly in space dimension D then select random velocity value which is within the range to be used as speed Cat. Select Cat randomly, and input into tracking mode Cat corresponding MR. While the rest, is inserted into the seeking mode. 3. Calculate fitness value for each of the Cat by entering values into the function position Cat match then the best Cat store in memory. 4. Treat Cat k accordance with the flag. If Cat k is in seeking mode, then run the process of seeking mode. But if Cat k is on tracking mode then run the tracking process mode. 5. Select the number of cat based on MR to the tracking mode. The rest is inserted into the seeking mode. If the stop condition has been reached, then the program is completed. However, if the stop condition is not met, then repeat step 3 to step 5.

presented [28]. Performance of Artificial Neural Networks depends on several factors such as Neural Network architecture, neurons number in hidden layer, neuron activation function, and the initial selection of network parameters (connection weights). Trial and error usually conducted to determine the network parameters and the initial connection weight. Particle Swarm Optimization can be used at pre-training, network training, validation, and testing performance to increase significantly. [18]

IV. Methodology Methods for determining the suitability of land in crops are as follows: 1. Data Preparation & Data Normalization Data normalization is done with the Min-Max method Normalization. This method will change the value to the new value. Typically the value is converted to a value range of 0 to 1 or -1 to 1. Changing this value is done by the following formula [29]: (x−xmin ) x ′ = (high − low) + low (9) (xmax −xmin )

2.

3.

III.3. Neural Network Artificial Neural Network or often simply referred to as a Neural Network is a mathematical model inspired by biological neural networks [17] Backpropagation introduced by Werbos consists of two stages [21] 1. Feedforward pass This stage is the stage where the Neural Network calculates the output value for each training pattern. 2. Backward propagation This stage was largely a stage where Neural Network provides an error signal from the output layer to the input layer. Weights / weight will be adjusted at this stage. Neural Network will learn the data during the training to produce valid forecasts when new data are Copyright © 2007 Praise Worthy Prize S.r.l. - All rights reserved

4.

Determination of Neural Network Parameters In this stage, several parameters that affect the performance of Neural Network are determined. parameters that affect the performance of Neural Network includes the number of neurons in the hidden layer / Neural Network architecture, cycle training, learning rate and momentum [21]. To determine this, trial and error used based on the minimum error. [18] Determination of Particle Swarm Optimization & Cat Swarm Optimization Parameters In this stage, particle number and cat number that will be used in Particle Swarm Optimization & Cat Swarm Optimization Algorithm is determine. To determine this, trial and error used based on the minimum error. For Particle Swarm optimization, inertia weight (w) used is the Constant Weight Inertia where: w = c, c = 0.7 [30]. For the parameters used in Particle Swarm Optimization is the original parameters are introduced Kennedy & Eberhart C1 = C2 = 2.0 [25]. For the parameters used in Cat Swarm Optimization is the original parameters are introduced Chu and Tsai MP: 5, SRD: 20%, CDC: 80%, MR: 2%, C1 ): 2.0 [27] Initialize Initial Weights & Bias Value & Training Process Neural Network weight Initialization is done in three different ways: a. Random Initial Weight & Bias Value Initialization Initial weights and bias values initialized with random way. After initialization, training is done until the stop condition is reached. Tests conducted on some models Neural Network International Review on Computers and Software, Vol. xx, n. x


5.

6.

architecture that models 7-4-1, 7-5-1, 7-6-1, 77-1. b. Initial Weight & Bias Value Initialization Using Particle Swarm Optimization (PSO) The initial weight Neural Network initialized using Particle Swarm Optimization algorithm by evaluating the fitness value of each particle, update the value of fitness and the best individual position / which is called global best and update speed and position of each particle. PSO model is developed to obtain the initial weights and bias values Neural Network. After initialization, training is done until the stop condition is reached. Tests conducted on some models Neural Network architecture that models 7-4-1, 7-5-1, 7-6-1, 7-7-1. c. Initial Weight & Bias Value Initialization Using Cat Swarm Optimization (CSO) The initial weight Neural Network initialized using CSO algorithm by evaluating fitness value of each particle, update the value of fitness and the best individual position / which is called global best and update speed and position of each particle. CSO model is developed to obtain the initial weights and bias values Neural Network. After initialization, training is done until the stop condition is reached. Tests conducted on some models Neural Network architecture that models 7-4-1, 7-5-1, 7-6-1, 7-7-1. Determination of Land Suitability After get final weights and biases value, then testing is done to predict crop production. The prediction results of this production will be used to determine the suitability of land [3]. Determination of land suitability is determined based on the prediction of production where: a. The land is said to be appropriate if the output above or equal to the productivity b. Land said to be less appropriate if the result is less than the productivity Evaluation The evaluation method in this study is done by doing a comparison evaluation based on the Mean Square Error (MSE) and the accuracy of land suitability. a. Mean Square Error (MSE) Mean Square Error (MSE) is used to measure the accuracy of the estimates. Mean Square Error is the absolute average of the squared prediction wrong. Mean Square Error is calculated using the following formula [31] ∑m |f −p |2

b.

MSE = t=1 t t m ft = actual production pt = predicted production m = number of predictions Accuracy of Land Suitability

(10)


Accuracy of land suitability is measured based on how much truth determining the suitability of land for each method. land suitability true Accuracy = (11) total number of land suitability

V.

Result and Discussion

V.1.

Evaluation of Each Method

Evaluation of Neural Network training is done by calculating Mean Square Error (MSE) on Neural Network model of 7-4-1, 7-5-1, 7-6-1, 7-7-1 for each method. Here is a comparison of three methods:

Fig 1. Comparison MSE on Neural Network Model 7-4-1, 7-5-1, 7-6-1, 7-7-1

Based on the experimental results, found that by using Cat Swarm Optimization (CSO) and Particle Swarm Optimization (PSO) to initialize initial weights and bias values in Backpropagation Neural Network training, will get a smaller error rate when compared with random initialization. But if we compare PSO algorithm and CSO algorithm, error rate CSO algorithm is smaller than PSO. In addition, found that the architecture / model of Neural Network is used affects the performance of Neural Network Training. This can be seen from the results of MSE for each method. Here is a summary of Accuracy and MSE obtained in testing phase: Table 1 MSE & Accuracy

In first experiment, where the initial weights and bias values of Neural Network is randomized (Backpropagation), accuracy obtained is 80% with Mean Square Error 0.01449 in training phase. In testing phase, it obtains 42% of accuracy and 0.14846 for Mean Square Error. In second experiment, in which the initial weights and bias values is done using the Neural Network algorithm Particle Swarm Optimization (PSO + Backpropagation), accuracy obtained is 83% with Mean Square Error 0.00603. In testing phase, it obtains 67% of accuracy and 0.14805 for Mean Square Error. International Review on Computers and Software, Vol. xx, n. x


In third experiment, in which the initial weights and bias values is done using the Neural Network algorithm Cat Swarm Optimization (CSO Backpropagation +), accuracy obtained is 93% with Mean Square Error 0.00439. In testing phase, it obtains 67% of accuracy and 0.10453 for Mean Square Error. From those three experiments, found that Backpropagation+CSO and Backpropagation+PSO produce same level of accuracy equal to 67%. The accuracy rate is higher when compared to Backpropagation without optimization algorithm. However, when compared to that obtained by MSE, Backpropagation + CSO produce the smallest error. V.2.

Fitness Value Evaluation of PSO & CSO

Based on the experimental results, showed that implementation suing CSO get better results than PSO. Here is a summary of fitness value PSO and CSO of the first iteration until the last iteration to get the initial weight and bias value. For more details can be seen in the chart below:

training stage is 0.00439 and in testing stage reached 0.10453. The use of optimization algorithms such as Particle Swarm Optimization and Cat Swarm Optimization for land suitability, get the same level of accuracy. It is seen from the level of accuracy obtained on the use of Particle Swarm Optimization and Cat Swarm Optimization by 67%. The level of accuracy obtained is greater when compared with the level of accuracy Backpropagation without optimization. For further research, there are some points that must be considered: researcher can combine optimization algorithms Particle Swarm Optimization and Cat Swarm Optimization or research on the influence of other optimizations algorithm such as Ant Colony Optimization and Bee Colony Optimization. Moreover, it can be done research to determine the effect of parameters / coefficients used in Particle Swarm Optimization and Cat Swarm Optimization.

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Fig 2. Fitness Value PSO & CSO

From the graph above, can be seen that the implementation of optimization algorithms CSO have a fitness value is smaller than the PSO iteration to 81 (in minimum case function, smaller / minimum is better).

VI. Conclusion and Future Work This research has been successfully implementing Swarm Optimization Algorithm in Neural Network Training for land suitability. Swarm Optimization Algorithm implemented in the Neural Network training to gain weight and bias initialization value. Based on the research results, the optimal architecture for determining the suitability of land is Neural Network 7-5-1 by using Cat Swarm Optimization. This can be seen from the Mean Square Error is reaching 0.00439 at the training stage. Optimization of Neural Network by using Cat Swarm Optimization in land suitability generates minimum Mean Square Error when compared with Neural Network without optimization and Neural Networks using Particle Swarm Optimization. Mean Square Error obtained in

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Authors’ information 1

Computer Science, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 2 Magister in Information Technology, Binus Graduate program, Bina Nusantara University, Jakarta, Indonesia Rita Layona [email protected]

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Suharjito [email protected]

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