Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Water resources in arid and semi-arid regions are susceptible to alteration in hydro-climatic variables, especially under climate change which makes runoff simulations more challenging. This study aims to simulate input runoff to a dam reservoir in an arid region under changing climatic conditions u...

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Published in:	Water resources management Vol. 36; no. 4; pp. 1191 - 1215
Main Authors:	Yoosefdoost, Icen, Khashei-Siuki, Abbas, Tabari, Hossein, Mohammadrezapour, Omolbani
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.03.2022 Springer Nature B.V
Subjects:	administrative management Algorithms Arid regions Arid zones Artificial neural networks Atmospheric Sciences basins Civil Engineering Climate change Climate models Climatic conditions Computer simulation Dams Data analysis Data mining Earth and Environmental Science Earth Sciences Environment Geotechnical Engineering & Applied Earth Sciences Global climate Global climate models Hydrogeology Hydrology/Water Resources Infinite series Intercomparison Neural networks Parameter identification Parameters Reservoirs Runoff Semi arid areas Semiarid zones Support vector machines temperature variance Water resources Artificial intelligence techniques Climate change LARS Rainfall-runoff forecasting model Gamma and M tests
ISSN:	0920-4741, 1573-1650
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Abstract	Water resources in arid and semi-arid regions are susceptible to alteration in hydro-climatic variables, especially under climate change which makes runoff simulations more challenging. This study aims to simulate input runoff to a dam reservoir in an arid region under changing climatic conditions using three data-mining algorithms, including Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Genetic Expression Programming (GEP), and the conceptual HYMOD model. Three parameters containing precipitation and maximum and minimum temperature were simulated from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) and Global Climate Models (GCMs) for the future period (2020–2040) under the high-end RCP8.5 scenario. The Long Ashton Research Station Weather Generator (LARS-WG) was selected as a downscaling method. The Gamma and M tests (This is an exam to determine whether an infinite series of functions will converge uniformly and absolutely or not) were applied to detect the best combinations and number of input parameters for the models, respectively. Among 29 defined input parameters for the models, the gamma test identified 11 parameters with the best functionality to simulate runoff. Based on the reliability estimates of model error variance by the M test, the data were partitioned as 75% for learning and the other 25% for test verification. A comparison of the runoff simulations of the models revealed a remarkable performance of the SVM model by 3, 5, and 14% compared to ANNs, GEP, and HYMOD models, respectively. The SVM model forecasted a 25% decrease in the mean runoff input to the dam reservoir for the 2020–2040 period compared to the study period (2000–2019). This result illustrates necessitating the implementation of sustainable adaptation strategies to protect future water resources in the basin.
AbstractList	Water resources in arid and semi-arid regions are susceptible to alteration in hydro-climatic variables, especially under climate change which makes runoff simulations more challenging. This study aims to simulate input runoff to a dam reservoir in an arid region under changing climatic conditions using three data-mining algorithms, including Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Genetic Expression Programming (GEP), and the conceptual HYMOD model. Three parameters containing precipitation and maximum and minimum temperature were simulated from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) and Global Climate Models (GCMs) for the future period (2020–2040) under the high-end RCP8.5 scenario. The Long Ashton Research Station Weather Generator (LARS-WG) was selected as a downscaling method. The Gamma and M tests (This is an exam to determine whether an infinite series of functions will converge uniformly and absolutely or not) were applied to detect the best combinations and number of input parameters for the models, respectively. Among 29 defined input parameters for the models, the gamma test identified 11 parameters with the best functionality to simulate runoff. Based on the reliability estimates of model error variance by the M test, the data were partitioned as 75% for learning and the other 25% for test verification. A comparison of the runoff simulations of the models revealed a remarkable performance of the SVM model by 3, 5, and 14% compared to ANNs, GEP, and HYMOD models, respectively. The SVM model forecasted a 25% decrease in the mean runoff input to the dam reservoir for the 2020–2040 period compared to the study period (2000–2019). This result illustrates necessitating the implementation of sustainable adaptation strategies to protect future water resources in the basin.
Author	Tabari, Hossein Yoosefdoost, Icen Mohammadrezapour, Omolbani Khashei-Siuki, Abbas
Author_xml	– sequence: 1 givenname: Icen orcidid: 0000-0003-0711-8661 surname: Yoosefdoost fullname: Yoosefdoost, Icen organization: Department of Water Engineering, University of Birjand – sequence: 2 givenname: Abbas orcidid: 0000-0002-2863-8482 surname: Khashei-Siuki fullname: Khashei-Siuki, Abbas email: abbaskhashei@birjand.ac.ir organization: Department of Water Engineering, University of Birjand – sequence: 3 givenname: Hossein orcidid: 0000-0003-2052-4541 surname: Tabari fullname: Tabari, Hossein organization: Department of Civil Engineering, KU Leuven – sequence: 4 givenname: Omolbani orcidid: 0000-0002-3815-6356 surname: Mohammadrezapour fullname: Mohammadrezapour, Omolbani organization: Department of Sciences and Water Engineering, Gorgan University of Agriculture Science and Natural Resources
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SubjectTerms	administrative management Algorithms Arid regions Arid zones Artificial neural networks Atmospheric Sciences basins Civil Engineering Climate change Climate models Climatic conditions Computer simulation Dams Data analysis Data mining Earth and Environmental Science Earth Sciences Environment Geotechnical Engineering & Applied Earth Sciences Global climate Global climate models Hydrogeology Hydrology/Water Resources Infinite series Intercomparison Neural networks Parameter identification Parameters Reservoirs Runoff Semi arid areas Semiarid zones Support vector machines temperature variance Water resources
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Title	Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models
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