气候变化领域集成服务门户(CCPortal): Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms

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DOI	10.1007/s12145-024-01220-x
	Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms
	Jannatkhah, Mahdieh; Davarpanah, Rouhollah; Fakouri, Bahman; Kisi, Ozgur
发表日期	2024
ISSN	1865-0473
EISSN	1865-0481
起始页码	17
结束页码	2
卷号	17 期号:2
英文摘要	Substantial deterioration of surface water quality, mainly caused by human activities and climate change, makes the assessment of water quality a global priority. Thus, in this study, four metaheuristic algorithms, namely the particle swarm optimization (PSO), differential evolution (DE), ant colony optimization algorithm (ACOR), and genetic algorithm (GA), were employed to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in the evaluation of surface water total dissolved solids (TDS). Monthly and annual TDS were considered as target variables in the analysis. In order to evaluate and compare the authenticity of the models, an economic factor (convergence time) and statistical indices of the coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliff efficiency (NSE) were utilized. The results revealed that the hybrid methods used in this study could enhance the classical ANFIS performance in the analysis of the monthly and annual TDS of both stations. For more clarification, the models were ranked using the TOPSIS approach by simultaneously applying the effects of statistical parameters, temporal and spatial change factors, and convergence time. This approach significantly facilitated decision-making in ranking models. The ANFIS-ACOR annual model considering discharge had the best performance in the Vanyar Station; Furthermore, the ANFIS-ACOR monthly model ignoring discharge was outstanding in the Gotvand Station. In total, after utilizing two defined and proposed temporal and spatial change factors, the ANFIS-ACOR and ANFIS-DE hybrid models had the best and worst performance in TDS prediction, respectively.
英文关键词	Surface water quality; TDS; Metaheuristic algorithms; ANFIS; Hybrid models; TOPSIS
语种	英语
WOS研究方向	Computer Science ; Geology
WOS类目	Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS记录号	WOS:001142211800003
来源期刊	EARTH SCIENCE INFORMATICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300614
作者单位	University of Tabriz; Tarbiat Modares University; Luebeck University of Applied Sciences; Ilia State University
推荐引用方式 GB/T 7714	Jannatkhah, Mahdieh,Davarpanah, Rouhollah,Fakouri, Bahman,et al. Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms[J],2024,17(2).
APA	Jannatkhah, Mahdieh,Davarpanah, Rouhollah,Fakouri, Bahman,&Kisi, Ozgur.(2024).Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms.EARTH SCIENCE INFORMATICS,17(2).
MLA	Jannatkhah, Mahdieh,et al."Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms".EARTH SCIENCE INFORMATICS 17.2(2024).