干旱区生态环境知识资源中心(Arid): Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Arid

DOI	10.1007/s11269-022-03068-6
	Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models
	Yoosefdoost, Icen; Khashei-Siuki, Abbas; Tabari, Hossein; Mohammadrezapour, Omolbani
通讯作者	Khashei-Siuki, A
来源期刊	WATER RESOURCES MANAGEMENT
ISSN	0920-4741
EISSN	1573-1650
出版年	2022
卷号	36 期号:4 页码:1191-1215
英文摘要	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.
英文关键词	Rainfall-runoff forecasting model Artificial intelligence techniques Climate change LARS Gamma and M tests
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000764506200004
WOS关键词	HYDROLOGICAL MODEL ; NEURAL-NETWORKS
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394885
推荐引用方式 GB/T 7714	Yoosefdoost, Icen,Khashei-Siuki, Abbas,Tabari, Hossein,et al. Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models[J],2022,36(4):1191-1215.
APA	Yoosefdoost, Icen,Khashei-Siuki, Abbas,Tabari, Hossein,&Mohammadrezapour, Omolbani.(2022).Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models.WATER RESOURCES MANAGEMENT,36(4),1191-1215.
MLA	Yoosefdoost, Icen,et al."Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models".WATER RESOURCES MANAGEMENT 36.4(2022):1191-1215.