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Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir

Evaporation as a major meteorological component of the hydrologic cycle plays a key role in water resources studies and climate change. The estimation of evaporation is a complex and unsteady process, so it is difficult to derive an accurate physical-based formula to represent all parameters that ef...

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Main Authors: Allawi, Mohammed Falah, El-Shafie, Ahmed
Format: Article
Published: Springer Verlag 2016
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/18245/
https://doi.org/10.1007/s11269-016-1452-1
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spelling my.um.eprints.182452019-09-11T04:39:51Z http://eprints.um.edu.my/18245/ Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir Allawi, Mohammed Falah El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) Evaporation as a major meteorological component of the hydrologic cycle plays a key role in water resources studies and climate change. The estimation of evaporation is a complex and unsteady process, so it is difficult to derive an accurate physical-based formula to represent all parameters that effect on estimate evaporation. Artificial intelligence-based methods may provide reliable prediction models for several applications in engineering. In this research have been introduced twelve networks with the RBF-NN and ANFIS methods. These models have applied to prediction daily evaporation at Layang reservoir, located in the southeast part of Malaysia. The used meteorological data set to develop the models for prediction daily evaporation rate from water surface for Layang reservoir includes daily air temperature, solar radiation, pan evaporation, and relative humidity that measured at a case study for fourteen years. The obtained result denote to the superiority of the RBF-NN models on the ANFIS models. A comparison of the model performance between RBF-NN and ANFIS models indicated that RBF-NN method presents the best estimates of daily evaporation rate with the minimum MSE 0.0471 , MAE 0.0032, RE and maximum R2 0.963. Springer Verlag 2016 Article PeerReviewed Allawi, Mohammed Falah and El-Shafie, Ahmed (2016) Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir. Water Resources Management, 30 (13). pp. 4773-4788. ISSN 0920-4741 https://doi.org/10.1007/s11269-016-1452-1 doi:10.1007/s11269-016-1452-1
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Allawi, Mohammed Falah
El-Shafie, Ahmed
Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir
description Evaporation as a major meteorological component of the hydrologic cycle plays a key role in water resources studies and climate change. The estimation of evaporation is a complex and unsteady process, so it is difficult to derive an accurate physical-based formula to represent all parameters that effect on estimate evaporation. Artificial intelligence-based methods may provide reliable prediction models for several applications in engineering. In this research have been introduced twelve networks with the RBF-NN and ANFIS methods. These models have applied to prediction daily evaporation at Layang reservoir, located in the southeast part of Malaysia. The used meteorological data set to develop the models for prediction daily evaporation rate from water surface for Layang reservoir includes daily air temperature, solar radiation, pan evaporation, and relative humidity that measured at a case study for fourteen years. The obtained result denote to the superiority of the RBF-NN models on the ANFIS models. A comparison of the model performance between RBF-NN and ANFIS models indicated that RBF-NN method presents the best estimates of daily evaporation rate with the minimum MSE 0.0471 , MAE 0.0032, RE and maximum R2 0.963.
format Article
author Allawi, Mohammed Falah
El-Shafie, Ahmed
author_facet Allawi, Mohammed Falah
El-Shafie, Ahmed
author_sort Allawi, Mohammed Falah
title Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir
title_short Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir
title_full Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir
title_fullStr Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir
title_full_unstemmed Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir
title_sort utilizing rbf-nn and anfis methods for multi-lead ahead prediction model of evaporation from reservoir
publisher Springer Verlag
publishDate 2016
url http://eprints.um.edu.my/18245/
https://doi.org/10.1007/s11269-016-1452-1
_version_ 1646210156377145344
score 13.211869

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