NDLI: Rainfall forecasting using neural network: A survey

Content Provider	IEEE Xplore Digital Library
Author	Darji, M.P. Dabhi, V.K. Prajapati, H.B.
Copyright Year	2015
Description	Author affiliation: Dept. of Inf. Technol., Dharmsinh Desai Univ., Nadiad, India (Darji, M.P.; Dabhi, V.K.; Prajapati, H.B.)
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.
Starting Page	706
Ending Page	713
File Size	303223
Page Count	8
File Format	PDF
e-ISBN	9781467369114
DOI	10.1109/ICACEA.2015.7164782
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-19
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training ANN Meteorological parameters Neural networks Artificial neural networks Computer architecture Predictive models Rainfall Prediction algorithms Forecasting Biological neural networks Rainfall prediction
Content Type	Text
Resource Type	Article

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