干旱区生态环境知识资源中心(Arid): Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping

Arid

DOI	10.3390/su15032499
	Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping
	AlAyyash, Saad; Al-Fugara, A'kif; Shatnawi, Rania; Al-Shabeeb, Abdel Rahman; Al-Adamat, Rida; Al-Amoush, Hani
通讯作者	AlAyyash, S
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2023
卷号	15 期号:3
英文摘要	The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well-known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching-learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM-GA, SVM-IWO, SVM-TLBO, ANFIS-GA, ANFIS-IWO, and ANFIS-TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM-GA and SVM-TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS-GA and ANFIS-TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM-IWO and ANFIS-IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.
英文关键词	azraq basin Jordan groundwater potential mapping ANFIS SVM GA TLBO IWO
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000930780400001
WOS关键词	FUZZY INFERENCE SYSTEM ; ARTIFICIAL NEURAL-NETWORK ; SPATIAL PREDICTION ; GENETIC ALGORITHM ; DECISION-ANALYSIS ; FREQUENCY RATIO ; GIS ; RECHARGE ; ANFIS ; INTEGRATION
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398755
推荐引用方式 GB/T 7714	AlAyyash, Saad,Al-Fugara, A'kif,Shatnawi, Rania,et al. Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping[J],2023,15(3).
APA	AlAyyash, Saad,Al-Fugara, A'kif,Shatnawi, Rania,Al-Shabeeb, Abdel Rahman,Al-Adamat, Rida,&Al-Amoush, Hani.(2023).Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping.SUSTAINABILITY,15(3).
MLA	AlAyyash, Saad,et al."Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping".SUSTAINABILITY 15.3(2023).