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Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates

Bayesian networks; Climate models; Forecasting; Knowledge acquisition; Machine learning; Particle swarm optimization (PSO); Uncertainty analysis; Water resources; Wind; Bayesian model averaging; Daily pan evaporation; Gamma test; Kernel extreme learning machine model; Learning machines; Machine mode...

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Main Authors: Ehteram M., Graf R., Ahmed A.N., El-Shafie A.
Other Authors: 57113510800
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-267062023-05-29T17:36:15Z Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates Ehteram M. Graf R. Ahmed A.N. El-Shafie A. 57113510800 7201736795 57214837520 16068189400 Bayesian networks; Climate models; Forecasting; Knowledge acquisition; Machine learning; Particle swarm optimization (PSO); Uncertainty analysis; Water resources; Wind; Bayesian model averaging; Daily pan evaporation; Gamma test; Kernel extreme learning machine model; Learning machines; Machine modelling; Optimization algorithms; Uncertainty; Water planning; Water resources management; Evaporation; algorithm; Bayesian analysis; evaporation; machine learning; numerical model; optimization; stochasticity; uncertainty analysis Evaporation is one of the most important parameters of meteorological science. Therefore, predicting evaporation is necessary for both water resources and planning management. The present study uses Bayesian Model Averaging (BMA) based on developed and optimized Kernel Extreme Learning Machine (KELM) models for predicting daily evaporation in different provinces of Iran with different climates. The Water Strider Algorithm, Salp Swarm Algorithm, Shark Algorithm, and Particle Swarm Optimization were combined with the KELM to predict daily evaporation in the Hormozgan, Mazandaran, Fars, Yazd, and Isfahan provinces. The models� inputs were average temperature, rainfall, number of sunny hours, wind speed, and relative humidity. The introducing a new hybrid gamma test for determining the adequate inputs, using hybrid and optimized KELM based on developing ELM for predicting evaporation, integrating individual models for predicting evaporation, and quantifying the uncertainty of outputs are the main innovations of the current study. Multiple error indices were used to evaluate the ability of models for predicting evaporation. The standalone and optimized KELM models were used to predict daily evaporation in the first level. In the next level, the BMA based on outputs of standalone and optimized KELM models predicted daily pan evaporation. The general results indicated that the BMA provided the best accuracy among other models in all stations. This study also introduced the new hybrid gamma test (GT-WSA) for choosing the best input combinations. The hybrid GT-WSA gave the best input combination without computing all input combinations (25�?�1). The uncertainty analysis of models also indicated that the uncertainty of BMA and optimized KELM models was lower than that of the KELM model. � 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:36:15Z 2023-05-29T09:36:15Z 2022 Article 10.1007/s00477-022-02235-w 2-s2.0-85129275026 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129275026&doi=10.1007%2fs00477-022-02235-w&partnerID=40&md5=ac1ee16464184793435dc894e6f16f4d https://irepository.uniten.edu.my/handle/123456789/26706 36 11 3875 3910 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Bayesian networks; Climate models; Forecasting; Knowledge acquisition; Machine learning; Particle swarm optimization (PSO); Uncertainty analysis; Water resources; Wind; Bayesian model averaging; Daily pan evaporation; Gamma test; Kernel extreme learning machine model; Learning machines; Machine modelling; Optimization algorithms; Uncertainty; Water planning; Water resources management; Evaporation; algorithm; Bayesian analysis; evaporation; machine learning; numerical model; optimization; stochasticity; uncertainty analysis
author2 57113510800
author_facet 57113510800
Ehteram M.
Graf R.
Ahmed A.N.
El-Shafie A.
format Article
author Ehteram M.
Graf R.
Ahmed A.N.
El-Shafie A.
spellingShingle Ehteram M.
Graf R.
Ahmed A.N.
El-Shafie A.
Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
author_sort Ehteram M.
title Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
title_short Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
title_full Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
title_fullStr Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
title_full_unstemmed Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates
title_sort improved prediction of daily pan evaporation using bayesian model averaging and optimized kernel extreme machine models in different climates
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806423501805977600
score 13.211869

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