干旱区生态环境知识资源中心(Arid): Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion

Arid

DOI	10.3389/fenvs.2023.1207027
	Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion
	Aboutaib, Fatima; Krimissa, Samira; Pradhan, Biswajeet; Elaloui, Abdenbi; Ismaili, Maryem; Abdelrahman, Kamal; Eloudi, Hasna; Ouayah, Mustapha; Ourribane, Malika; Namous, Mustapha
通讯作者	Ismaili, M
来源期刊	FRONTIERS IN ENVIRONMENTAL SCIENCE
EISSN	2296-665X
出版年	2023
卷号	11
英文摘要	Assessing and mapping the vulnerability of gully erosion in mountainous and semi-arid areas is a crucial field of research due to the significant environmental degradation observed in such regions. In order to tackle this problem, the present study aims to evaluate the effectiveness of three commonly used machine learning models: Random Forest, Support Vector Machine, and Logistic Regression. Several geographic and environmental factors including topographic, geomorphological, environmental, and hydrologic factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 191 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. The models' performance was assessed by calculating the area under the ROC curve (AUC). The findings indicate that the RF model exhibited the highest performance (AUC = 89%), followed by the SVM (AUC = 87%) and LR (AUC = 87%) models. Furthermore, the results highlight those factors such as NDVI, lithology, drainage, and density were the most influential, as determined by the RF, SVM, and LR methods. This study provides a valuable tool for enhancing the mapping of soil erosion and identifying the most important influencing factors that primarily cause soil deterioration in mountainous and semi-arid regions.
英文关键词	gully erosion vulnerability machine learning conditioning factors Ahmed El Hanssali watershed mountainous region vulnerability mapping El Hanssali watershed
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001039260300001
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; LOGISTIC-REGRESSION ; SOIL-EROSION ; SUSCEPTIBILITY ASSESSMENT ; SEMIARID REGION ; RIVER-BASIN ; BIVARIATE ; MULTIVARIATE ; CATCHMENT ; IMPACTS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396515
推荐引用方式 GB/T 7714	Aboutaib, Fatima,Krimissa, Samira,Pradhan, Biswajeet,et al. Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion[J],2023,11.
APA	Aboutaib, Fatima.,Krimissa, Samira.,Pradhan, Biswajeet.,Elaloui, Abdenbi.,Ismaili, Maryem.,...&Namous, Mustapha.(2023).Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion.FRONTIERS IN ENVIRONMENTAL SCIENCE,11.
MLA	Aboutaib, Fatima,et al."Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion".FRONTIERS IN ENVIRONMENTAL SCIENCE 11(2023).