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Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction

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@article{IJASEIT1487,
   author = {Suhaila Zainudin and Dalia Sami Jasim and Azuraliza Abu Bakar},
   title = {Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {1148--1153},
   keywords = {Rainfall prediction; data mining; classification; Random Forest; ensemble.},
   abstract = {Climate change prediction analyses the behaviours of weather for a specific time. Rainfall forecasting is a climate change task where specific features such as humidity and wind will be used to predict rainfall in specific locations. Rainfall prediction can be achieved using classification task under Data Mining. Different techniques lead to different performances depending on rainfall data representation including representation for long term (months) patterns and short-term (daily) patterns. Selecting an appropriate technique for a specific duration of rainfall is a challenging task. This study analyses multiple classifiers such as Naïve Bayes, Support Vector Machine, Decision Tree, Neural Network and Random Forest for rainfall prediction using Malaysian data. The dataset has been collected from multiple stations in Selangor, Malaysia. Several pre-processing tasks have been applied in order to resolve missing values and eliminating noise. The experimental results show that with small training data (10%) from 1581 instances Random Forest correctly classified 1043 instances. This is the strength of an ensemble of trees in Random Forest where a group of classifiers can jointly beat a single classifier.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1487},
   doi = {10.18517/ijaseit.6.6.1487}
}

EndNote

%A Zainudin, Suhaila
%A Jasim, Dalia Sami
%A Abu Bakar, Azuraliza
%D 2016
%T Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction
%B 2016
%9 Rainfall prediction; data mining; classification; Random Forest; ensemble.
%! Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction
%K Rainfall prediction; data mining; classification; Random Forest; ensemble.
%X Climate change prediction analyses the behaviours of weather for a specific time. Rainfall forecasting is a climate change task where specific features such as humidity and wind will be used to predict rainfall in specific locations. Rainfall prediction can be achieved using classification task under Data Mining. Different techniques lead to different performances depending on rainfall data representation including representation for long term (months) patterns and short-term (daily) patterns. Selecting an appropriate technique for a specific duration of rainfall is a challenging task. This study analyses multiple classifiers such as Naïve Bayes, Support Vector Machine, Decision Tree, Neural Network and Random Forest for rainfall prediction using Malaysian data. The dataset has been collected from multiple stations in Selangor, Malaysia. Several pre-processing tasks have been applied in order to resolve missing values and eliminating noise. The experimental results show that with small training data (10%) from 1581 instances Random Forest correctly classified 1043 instances. This is the strength of an ensemble of trees in Random Forest where a group of classifiers can jointly beat a single classifier.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1487
%R doi:10.18517/ijaseit.6.6.1487
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Suhaila Zainudin,Dalia Sami Jasim and Azuraliza Abu Bakar,"Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1148-1153, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1487.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Zainudin, Suhaila
AU  - Jasim, Dalia Sami
AU  - Abu Bakar, Azuraliza
PY  - 2016
TI  - Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 1148
EP  - 1153
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Rainfall prediction; data mining; classification; Random Forest; ensemble.
N2  - Climate change prediction analyses the behaviours of weather for a specific time. Rainfall forecasting is a climate change task where specific features such as humidity and wind will be used to predict rainfall in specific locations. Rainfall prediction can be achieved using classification task under Data Mining. Different techniques lead to different performances depending on rainfall data representation including representation for long term (months) patterns and short-term (daily) patterns. Selecting an appropriate technique for a specific duration of rainfall is a challenging task. This study analyses multiple classifiers such as Naïve Bayes, Support Vector Machine, Decision Tree, Neural Network and Random Forest for rainfall prediction using Malaysian data. The dataset has been collected from multiple stations in Selangor, Malaysia. Several pre-processing tasks have been applied in order to resolve missing values and eliminating noise. The experimental results show that with small training data (10%) from 1581 instances Random Forest correctly classified 1043 instances. This is the strength of an ensemble of trees in Random Forest where a group of classifiers can jointly beat a single classifier.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1487
DO  - 10.18517/ijaseit.6.6.1487

RefWorks

RT Journal Article
ID 1487
A1 Zainudin, Suhaila
A1 Jasim, Dalia Sami
A1 Abu Bakar, Azuraliza
T1 Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 1148
OP 1153
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Rainfall prediction; data mining; classification; Random Forest; ensemble.
AB Climate change prediction analyses the behaviours of weather for a specific time. Rainfall forecasting is a climate change task where specific features such as humidity and wind will be used to predict rainfall in specific locations. Rainfall prediction can be achieved using classification task under Data Mining. Different techniques lead to different performances depending on rainfall data representation including representation for long term (months) patterns and short-term (daily) patterns. Selecting an appropriate technique for a specific duration of rainfall is a challenging task. This study analyses multiple classifiers such as Naïve Bayes, Support Vector Machine, Decision Tree, Neural Network and Random Forest for rainfall prediction using Malaysian data. The dataset has been collected from multiple stations in Selangor, Malaysia. Several pre-processing tasks have been applied in order to resolve missing values and eliminating noise. The experimental results show that with small training data (10%) from 1581 instances Random Forest correctly classified 1043 instances. This is the strength of an ensemble of trees in Random Forest where a group of classifiers can jointly beat a single classifier.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1487
DO  - 10.18517/ijaseit.6.6.1487