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Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia

Correct inflow prediction is a critical non-engineering measure for ensuring flood control and increasing water supply efficiency. In addition, accurate inflow prediction can offer reservoir planning and management guidance since inflow is the major input into reservoirs. This study aims at generali...

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Main Authors: Latif S.D., Ahmed A.N.
Other Authors: 57216081524
Format: Article
Published: Springer Science and Business Media B.V. 2025
Subjects:
Australia
Iraq
Kurdistan [Iraq]
flood control
forecasting method
inflow
machine learning
rainfall
regression analysis
reservoir
timescale
water management
water supply
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spelling my.uniten.dspace-366182025-03-03T15:43:26Z Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia Latif S.D. Ahmed A.N. 57216081524 57214837520 Australia Iraq Kurdistan [Iraq] flood control forecasting method inflow machine learning rainfall regression analysis reservoir timescale water management water supply Correct inflow prediction is a critical non-engineering measure for ensuring flood control and increasing water supply efficiency. In addition, accurate inflow prediction can offer reservoir planning and management guidance since inflow is the major input into reservoirs. This study aims at generalizing a machine learning model for forecasting reservoir inflow. Daily, weekly, and monthly inflow and rainfall time-series data have been collected as two hydrological parameters to forecast reservoir inflow using a machine learning method, namely, support vector regression (SVR). Four different SVR kernels have been applied in this study. The kernels are radial basis function (RBF), linear, normalized polynomial, and sigmoid. Two scenarios for input selection have been implemented. Dokan dam in Kurdistan region of Iraq and Warragamba Dam in Australia were selected as the case studies for this research. For the purpose of generalization, the proposed models have been applied to two countries with a different climate condition. The findings showed that daily timescale outperformed weekly and monthly, while RBF outperformed the other SVR kernels with root-mean-square error (RMSE) = 145.7 and coefficient of determination (R2) = 0.85 for forecasting daily inflow at Dokan dam. However, RBF kernel could not perform well for forecasting daily inflow in Warragamba dam. The results showed that the proposed machine learning model performed well at Kurdistan region of Iraq only, while the result for Australia was not accurate. Therefore, the proposed models could not be generalized. ? The Author(s), under exclusive licence to Springer Nature B.V. 2023. Final 2025-03-03T07:43:26Z 2025-03-03T07:43:26Z 2024 Article 10.1007/s10668-023-03885-8 2-s2.0-85171338674 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171338674&doi=10.1007%2fs10668-023-03885-8&partnerID=40&md5=fe02e203d0ed1a4e9650f284f5ffea6b https://irepository.uniten.edu.my/handle/123456789/36618 26 5 12513 12544 Springer Science and Business Media B.V. 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/
topic Australia
Iraq
Kurdistan [Iraq]
flood control
forecasting method
inflow
machine learning
rainfall
regression analysis
reservoir
timescale
water management
water supply
spellingShingle Australia
Iraq
Kurdistan [Iraq]
flood control
forecasting method
inflow
machine learning
rainfall
regression analysis
reservoir
timescale
water management
water supply
Latif S.D.
Ahmed A.N.
Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
description Correct inflow prediction is a critical non-engineering measure for ensuring flood control and increasing water supply efficiency. In addition, accurate inflow prediction can offer reservoir planning and management guidance since inflow is the major input into reservoirs. This study aims at generalizing a machine learning model for forecasting reservoir inflow. Daily, weekly, and monthly inflow and rainfall time-series data have been collected as two hydrological parameters to forecast reservoir inflow using a machine learning method, namely, support vector regression (SVR). Four different SVR kernels have been applied in this study. The kernels are radial basis function (RBF), linear, normalized polynomial, and sigmoid. Two scenarios for input selection have been implemented. Dokan dam in Kurdistan region of Iraq and Warragamba Dam in Australia were selected as the case studies for this research. For the purpose of generalization, the proposed models have been applied to two countries with a different climate condition. The findings showed that daily timescale outperformed weekly and monthly, while RBF outperformed the other SVR kernels with root-mean-square error (RMSE) = 145.7 and coefficient of determination (R2) = 0.85 for forecasting daily inflow at Dokan dam. However, RBF kernel could not perform well for forecasting daily inflow in Warragamba dam. The results showed that the proposed machine learning model performed well at Kurdistan region of Iraq only, while the result for Australia was not accurate. Therefore, the proposed models could not be generalized. ? The Author(s), under exclusive licence to Springer Nature B.V. 2023.
author2 57216081524
author_facet 57216081524
Latif S.D.
Ahmed A.N.
format Article
author Latif S.D.
Ahmed A.N.
author_sort Latif S.D.
title Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
title_short Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
title_full Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
title_fullStr Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
title_full_unstemmed Ensuring a generalizable machine learning model for forecasting reservoir inflow in Kurdistan region of Iraq and Australia
title_sort ensuring a generalizable machine learning model for forecasting reservoir inflow in kurdistan region of iraq and australia
publisher Springer Science and Business Media B.V.
publishDate 2025
_version_ 1825816068087611392
score 13.244413

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