9786202011600 - Short-term Rainfall Forecasting using ANNs and ANFIS Models: Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System de Kyada, Pradip; Kumar, Pravendra... (6 resultados)

Paperback. Condición: Brand New. 100 pages. 8.66x5.91x0.23 inches. In Stock.

Taschenbuch. Condición: Neu. Short-term Rainfall Forecasting using ANNs and ANFIS Models | Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System | Pradip Kyada (u. a.) | Taschenbuch | 100 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202011600 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 100 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kyada PradipThe author, Pradip M. Kyada has completed his B.Tech (Agri. Engg.) in 2011 from College of Agri. Engg. and Tech., J.A.U., Junagadh (Gujarat). He also obtained M. Tech. (Soil and Water Cons. Engg.) degree in 2013 from GBPU.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.