Short-term Rainfall Forecasting using ANNs and ANFIS Models: Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System

Kyada, Pradip/ Kumar, Pravendra/ Sojitra, Manoj

ISBN 10: 6202011602 / ISBN 13: 9786202011600

Edité par LAP LAMBERT Academic Publishing, 2017

A propos de cet article

100 pages. 8.66x5.91x0.23 inches. In Stock.

N° de réf. du vendeur zk6202011602

Détails bibliographiques

• Titre: Short-term Rainfall Forecasting using ANNs and ANFIS Models: Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System
• Éditeur: LAP LAMBERT Academic Publishing
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 6202011602
• ISBN à 13 chiffres: 9786202011600
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

Rainfall forecasting still represents an extremely important issue in hydrology. On the other hand, rainfall is one of the most complicated effective hydrologic processes in runoff prediction. In the present study an attempt has been made to develop artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for forecasting of daily rainfall for monsoon period of Junagadh, Gujarat, India. The data of period (1st June to 30th October) of years 1979-1981, 1984-1989 and 1991-2007 were used to train the models and data of years 2008-2011 were used for test the models. The sensitivity analysis was used to identify the most important parameter for rainfall prediction. In ANN model, back-propagation algorithm and sigmoid activation function used to train and test the models while in ANFIS models, gaussian and generalized bell membership function are used. It was found from the study that the performance of the ANN double hidden layer model with four input parameters is better than the ANFIS model. The sensitivity analysis indicated that the most important input parameter besides rainfall itself is the vapour pressure in rainfall forecasting.