气候变化领域集成服务门户(CCPortal): Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios

CCPortal

DOI	10.1007/s13201-024-02126-1
	Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios
	Azizi, Ehsan; Yosefvand, Fariborz; Yaghoubi, Behrouz; Izadbakhsh, Mohammad Ali; Shabanlou, Saeid
发表日期	2024
ISSN	2190-5487
EISSN	2190-5495
起始页码	14
结束页码	4
卷号	14 期号:4
英文摘要	One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.
英文关键词	SDSM model; Climate change; GMDH; Rcp scenarios; Sahneh Plain; Greenhouse gas emission scenario
语种	英语
WOS研究方向	Water Resources
WOS类目	Water Resources
WOS记录号	WOS:001185742400003
来源期刊	APPLIED WATER SCIENCE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/307804
作者单位	Islamic Azad University
推荐引用方式 GB/T 7714	Azizi, Ehsan,Yosefvand, Fariborz,Yaghoubi, Behrouz,et al. Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios[J],2024,14(4).
APA	Azizi, Ehsan,Yosefvand, Fariborz,Yaghoubi, Behrouz,Izadbakhsh, Mohammad Ali,&Shabanlou, Saeid.(2024).Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios.APPLIED WATER SCIENCE,14(4).
MLA	Azizi, Ehsan,et al."Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios".APPLIED WATER SCIENCE 14.4(2024).