DocumentCode :
3253691
Title :
Rainfall forecasting using neural network: A survey
Author :
Darji, Mohini P. ; Dabhi, Vipul K. ; Prajapati, Harshadkumar B.
Author_Institution :
Dept. of Inf. Technol., Dharmsinh Desai Univ., Nadiad, India
fYear :
2015
fDate :
19-20 March 2015
Firstpage :
706
Lastpage :
713
Abstract :
An accurate rainfall forecasting is very important for agriculture dependent countries like India. For analyzing the crop productivity, use of water resources and pre-planning of water resources, rainfall prediction is important. Statistical techniques for rainfall forecasting cannot perform well for long-term rainfall forecasting due to the dynamic nature of climate phenomena. Artificial Neural Networks (ANNs) have become very popular, and prediction using ANN is one of the most widely used techniques for rainfall forecasting. This paper provides a detailed survey and comparison of different neural network architectures used by researchers for rainfall forecasting. The paper also discusses the issues while applying different neural networks for yearly/monthly/daily rainfall forecasting. Moreover, the paper also presents different accuracy measures used by researchers for evaluating performance of ANN.
Keywords :
agriculture; crops; geophysics computing; neural nets; rain; statistical analysis; ANN; India; accurate rainfall forecasting; agriculture dependent countries; artificial neural networks; climate phenomena; crop productivity; rainfall prediction; statistical techniques; water resources; Artificial neural networks; Biological neural networks; Computer architecture; Forecasting; Prediction algorithms; Predictive models; Training; ANN; forecasting; meteorological parameters; neural networks; rainfall; rainfall prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in
Conference_Location :
Ghaziabad
Type :
conf
DOI :
10.1109/ICACEA.2015.7164782
Filename :
7164782
Link To Document :
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