Ijciet 10 01_161

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 01, January 2019, pp. 1745–1750, Article ID: IJCIET_10_01_161
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=1
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
©IAEME Publication Scopus Indexed
A WRAPPER BASED FEATURE SELECTION
APPROACH USING BEES ALGORITHM FOR
EXTREME RAINFALL PREDICTION VIA
WEATHER PATTERN RECOGNITION
THROUGH SVM CLASSIFIER
V. Karunakaran
Department of Computer Science and Engineering,
Karunya Institute of Technology and Sciences, Coimbatore, India
S. Iwin Joseph
Ravi Teja
M. Suganthi
Department of Electronics and Communication Engineering,
Mahendra College of Engineering, Salem, India
V. Rajasekar
AMC Engineering College, Bangalore, India
ABSTRACT
Rainfall prediction is a major problem in metrological department and it is closely
associated with the economy and life of human. Accuracy of rainfall prediction is a
very important for countries like india, because Indian economy is mainly dependent
up on the agriculture. Most of the statistical methods are unsuccessful due to dynamic
nature of atmosphere. In this paper, SVM classifier is used for classification, before
SVM classifier, bees algorithm is used for feature selection. Keeping all the features
and instances in the training set is not a good approach for better classification
because all the features are not contributing more information during classification,
removing those features will not affect any classification accuracy. The experimental
result shows that the proposed method provides better detection rate, false positive

A Wrapper Based Feature Selection Approach using Bees Algorithm for Extreme Rainfall
Prediction via Weather Pattern Recognition Through SVM Classifier
rate, accuracy rate and less training time of classifier what we obtained with entire
dataset.
Key words: Support Vector machine, Feature selection, Bees algorithm, Rainfall.
Cite this Article: V. Karunakaran, S. Iwin Joseph, Ravi Teja, M. Suganthi,
V. Rajasekar. A Wrapper Based Feature Selection Approach using Bees Algorithm for
Extreme Rainfall Prediction via Weather Pattern Recognition Through SVM
Classifier, International Journal of Civil Engineering and Technology (IJCIET) 10(1),
2019, pp. 1745–1750.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=1
1. INTRODUCTION
Rainfall predication is very supportive to avoid flood, that will save lives of human and it also
helps to manage resources of water. Rainfall prediction also helps the farmers to manage their
crops better. Rainfall prediction is a challenging task for meteorological department due to
dynamic change of atmosphere. Most of the researchers tried with lot of machine learning
algorithms for rainfall predictions and they achieved some fair prediction result. Prashant
Goswami et al proposed artificial neural network for rainfall prediction and the experimental
shows good prediction accuracy of annual rainfall compared to the existing neural networks
[1]. Venkatesan et al used error back propagation neural network for rainfall prediction. The
experimental results compared with some existing statistical methods, error back propagation
neural network out performed in prediction accuracy [2]. A.K shai used artificial neural
network with error back propagation algorithm for rainfall prediction. The model is compared
with few statistical methods and this method provides better prediction rate and less Mean
squared value [3]. Ninan Sajeeth Philip et al used ABF neural network for rainfall prediction
and the method is compared with some statistical methods, ABF neural network is out
performed [4]. Ninan Sajeeth Philip et al used adaptive basis function network for rainfall
prediction and the method is compared with some traditional methods like principal
component analysis and spectral analysis. Adaptive basis function network provides good
prediction results [5]. Norraseth Chantasut et al used artificial neural network for rainfall
predication in the area of Chao Phraya River. The experimental result shows, 96.6% of
prediction accuracy is obtained by the model while using training dataset and 96.9% of
prediction accuracy while using testing dataset [6]. V.K.Somvanshi et al used artificial neural
network and ARIMA techniques for rainfall prediction. The experimental result shows
artificial neural network and ARIMA techniques is a promising tool for annual rainfall
predictions [7]. S. Chattopadhyay et al used multilayer perceptron for rainfall prediction. The
experimental result shows better prediction accuracy compared to the conventional methods
[8]. Smita Kulkarni et al discussed three prediction methods based on backpropagation neural
network learning rules 1. Momentum learning, 2. Conjugate gradient descent (CGD) learning,
and 3. Levenberg -- Marquardt (LM) learning. Among the three prediction methods conjugate
gradient descent methods provides better prediction accuracy compared to momentum
learning and LM learning [9]. P. Guhathakurta used deterministic artificial neural network
for rainfall prediction. This method is compared with single time series, the derivation of
smaller scale (sub-divisions) forecast model may be more useful than the all India forecast
[10]. Most of the researchers tried with different approaches for rainfall prediction, but
still need some improvement in prediction accuracy. In this paper, bees algorithm used for
feature selection, after feature selection, the obtained dataset is given to artificial neural
network for prediction. The proposed system provides better prediction rate and less mean
square value.

V. Karunakaran, S. Iwin Joseph, Ravi Teja, M. Suganthi, V. Rajasekar
The rest of the paper is organized as follows: Section 2 presents introduction to wrapper
method and bees algorithm. Section 3 presents overall architecture of the proposed system.
Section 4 presents feature selection using bees algorithm. Section 5 presents experimental
results. Finally section 6 presents conclusion.
2. INTRODUCTION TO WRAPPER METHOD AND BEES
ALGORITHM
Feature selection is mostly carried out by two methods, 1. Filter method and 2. Wrapper
method. In filter method, feature selection is carried out by selection function. It is not
dependent upon the classification accuracy. In wrapper method, feature selection is carried
out by classification accuracy. Most of the wrapper method provides better classification
accuracy compared to the filter methods. Bees algorithm is a population-based search
algorithm and it was developed by Pham, Ghanbarzadeh and et al. in the year 2005 [11]. .
It
mimics the food foraging behaviour of honey bee colonies. The algorithm mainly developed
for escaping from local maxima. The basic version of the algorithm performs a kind of
neighbourhood search combined with global search, and it can be used for both combinatorial
optimization and continuous optimization. A basic version of the bees algorithm is shown
in figure 1.
Figure 1 Basic version of Bees algorithm
3. OVERALL ARCHITECTURE OF PROPOSED SYSTEM
Figure 2 Overall architecture of proposed system

In this paper, the first phase, bees algorithm is used for finding a best subset of features
through decision tree classifier. In the second phase, the input is obtained reduced dataset
from the first phase. The obtained reduced dataset is given to SVM classifier for rainfall
prediction. The proposed system gives detection rate, accuracy rate, less false positive rate
and less training time of classifier.
4. FEATURE SELECTION USING BEES ALGORITHM
In this paper, Bees algorithm is used for finding optimal subset of features. In our work, we
used weather forecasting dataset and the dataset consists of 60 features, all the 60 features are
not contributing equal information. In the data set few of the features contributing more
information and few of the features contributing less information. In our work, we used bees
algorithm to identify those features are contributing more information. Weather forecasting
dataset consists of 60 features. First we want to identify the best subset size. In this work, we
tried with different subset sizes like 55 features, 50 features, 45 features, 40 features, 35
features, 30 features 25 features, 20 features, 15 features and 10 features. The range of subset
size 10 to 20 gives more classification accuracy compared to remaining subset. In this work,
subset size is taken as 15.
 Initialize population with random solutions:
Initially create randomly N solutions. Weather forecasting dataset consists of 60 features.
These features are ranked according to the location. In this work the features are divided into
two subsets, one is selected subset features and the size of the subset is 15 and another one is
unselected subset of features and the size of the subset is 45. The intersection of unselected
subset of features and selected subset of features is null. Randomly, create N initial solution
and evaluate each initial solution with fitness value.
 While stopping criteria is not met
 Evaluate each initial solution with fitness value
Evaluate each initial solution with fitness value and arrange it descending order according
to the fitness value. Example p1,p2,p3…………pn. In this example, p1 is assigned as best
subset.
 Neighborhood operations.
Take first n/2 solutions. Apply exchange operator to those solutions.
Example :
P1 (Selected Subset):
F1 F4 F8 F12 F16 F20 F24 F28 F32 F38 F42 F46 F50 F54 F60
P1 (Unselected Subset)
Apply exchange operator between selected subset and unselected subset. Randomly pick
ceiling of 20% of features from the selected subset and randomly pick same number of
features from the unselected sunset of features. In this example, 20% of features contain 3
features. So randomly picks 3 features from the selected subset and randomly picks 3 features
from the unselected subset and apply exchange operator to this two sets and the example
shown below.

V. Karunakaran, S. Iwin Joseph, Ravi Teja, M. Suganthi, V. Rajasekar
P1 (Selected Subset)
P1 (Unselected Subset)
This process continues upto first n/2 solutions. If any of the new subset has fitness value
greater than best subset. Assign new subset as best subset.
 Take a remaining population and randomly, create n solutions and evaluate each solutions
using decision tree. If any of the new subset is better than current best subset than assign new
subset as best subset.
 End while
In this algorithm stopping criteria is defined by user. i.e., Number of iteration is given by
user and finally the algorithm returns best subset of features.
5. EXPERIMENT AND RESULTS
Take input as weather forecast dataset, it consists of 60 features and then apply bees algorithm
to find best subset of features after finding the best subset of features. Evaluate the best subset
of features by using SVM Classifier. The result obtained from SVM classifier for accuracy
and training time of classifier shown in table1 and 2 respectively.
Table 1 Detection rate, false positive rate and Accuracy belongs to feature selection and without
feature selection
Dataset Detection rate False positive rate Accuracy Rate
Weather forecasting
without feature
selection
86.28 5.93 88.98
Weather forecasting
with feature selection
87.12 5.64 89.18
Figure 1 Detection rate, false positive rate and Accuracy belongs to feature selection and without
feature selection.

Table 2 Training time of SVM classifier for with feature selection and without feature selection
Dateset Training time of SVM
classifier (Without feature
selection)
Training time of SVM
classifier (Without feature
selection)
Weather Forecasting 15.6 min 7.3 min
From the table 1 and 2, clearly shows proposed system gives better detection rate, false
positive rate, accuracy rate and less training time of classifier.
6. CONCLUSIONS
Accuracy of rainfall prediction is a very important for most of the countries, which are mainly
dependent up on the agriculture. Most of the statistical methods are unsuccessful due to
dynamic nature of atmosphere. In this paper, SVM classifier is used for prediction, before
SVM classifier, bees algorithm is used for feature selection. The experimental result shows
that the proposed method provides better detection rate, accuracy rate and less training time of
classifier what we obtained with entire dataset.
REFERENCES
[1] P. Goswami and Srividya, “A novel Neural Network design for long range prediction of
rainfall pattern,” Current Sci.(Bangalore), vol. 70, no. 6, pp. 447-457, 1996.
[2] C. Venkatesanet, S. D. Raskar , S. S. Tambe , B. D. Kulkarni , and R.N. Keshavamurty ,
“Prediction of all India summer monsoon rainfall using Error-Back-Propagation Neural
Networks,” Meteorology and Atmospheric Physics, pp. 225-240, 1997.
[3] A. K. Sahai, M. K. Soman, and V. Satyan, “All India summer monsoon rainfall prediction
using an Artificial Neural Network,” Climate dynamics, vol. 16, no. 4, pp. 291-302, 2000.
[4] N. S. Philip and K. B. Joseph, “On the predictability of rainfall in Kerala-An application
of ABF neural network,” Computational Science- ICCS, Springer Berlin Heidelberg, pp.
1-12, 2001.
[5] N. S. Philip and K. B. Joseph, “A Neural Network tool for analyzing trends in rainfall,”
Comput. & Geosci.,vol. 29, no. 2, pp. 215-223, 2003.
[6] N. Chantasut, C. Charoenjit, and C. Tanprasert, “Predictive mining of rainfall predictions
using artificial neural networks for Chao Phraya River,” 4th Int Conf. of the Asian
Federation of Inform. Technology in Agriculture and the 2nd World Congr. on Comput. in
Agriculture and Natural Resources, Bangkok, Thailand, pp. 117-122, 2004.
[7] V. K. Somvanshi, O. P. Pandey, P. K. Agrawal, N.V.Kalanker1, M.Ravi Prakash, and
Ramesh Chand, “Modeling and prediction of rainfall using Artificial Neural Network and
ARIMA techniques,” J. Ind. Geophys. Union, vol. 10, no. 2, pp. 141-151, 2006.
[8] S. Chattopadhyay and M. Chattopadhyay, “A Soft Computing technique in
rainfallforecasting,” Int. Conf. on IT, HIT, pp. 19-21, 2007.
[9] Smita Kulkarni, Milind Mushrif, “Comparative Study among Different Neural Net
Learning Algorithms Applied to Rainfall Predication”, ICSC’ 14 Proceedings of the 2014
International conference on electronic systems, Signal processing and Computing
Technologies pages 209-216.
[10] P.Guhathakurta, “Long lead monsoon rainfall prediction using deterministic Artificial
Neural Network model,” Meteorology and Atmospheric Physics 101, pp. 93-108, 2008.
[11] Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm.
Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
[12] Osama A., Zulaiha A. O., Bees Algorithm for Feature Selection in Network Anomaly
Detection. Journal of Applied Sciences Research, 8(3) pp. 1748-1756, 2012.

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