Rainfall Forecasting Using Neural Network - Darji, Mohini;Joshi, Sachi;Darji, Yashesh
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An accurate rainfall forecasting is a challenging problem for agriculture dependent countries like India for analyzing the crop productivity, use of water resources and pre-planning of water resources. This book provides issues involved in prediction of daily, monthly, and yearly rainfall data using neural network. We incorporate various models such as ARIMA, Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN), and Time Delay Neural Network (TDNN) techniques for rainfall prediction. All the models implemented using MATLAB software. The purpose of the book is to…mehr

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Produktbeschreibung
An accurate rainfall forecasting is a challenging problem for agriculture dependent countries like India for analyzing the crop productivity, use of water resources and pre-planning of water resources. This book provides issues involved in prediction of daily, monthly, and yearly rainfall data using neural network. We incorporate various models such as ARIMA, Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN), and Time Delay Neural Network (TDNN) techniques for rainfall prediction. All the models implemented using MATLAB software. The purpose of the book is to uses Genetic Algorithm (GA) for optimizing biases and weights of neural network. The results of ARIMA model are compared with the results obtained using three neural network models.
Autorenporträt
Ms. Mohini Darji received her B.E and M.Tech degrees in 2013 and 2015 respectively from Gujarat Technological University (GTU) and Dharmsinh Desai University (DDU). Presently she is working as a Assistant Professor in Computer Science & Engineering Department at Devang Patel Institute of Advance Technology and Research, CHARUSAT and pursuing Ph.D.