干旱区生态环境知识资源中心(Arid): Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS

Arid

DOI	10.1007/s12665-024-11424-5
	Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS
	Naceur, Hassan Ait; Abdo, Hazem Ghassan; Igmoullan, Brahim; Namous, Mustapha; Alshehri, Fahad; Albanai, Jasem A.
通讯作者	Abdo, HG
来源期刊	ENVIRONMENTAL EARTH SCIENCES
ISSN	1866-6280
EISSN	1866-6299
出版年	2024
卷号	83 期号:3
英文摘要	Gullying is one of the problems that cause soil degradation in semi-arid areas and should be predicted to mitigate its damaging effects. Three machine learning models have been employed in this work to map the susceptibility to gully erosion in the N'fis river basin in the Moroccan High Atlas. Utilizing high-resolution images from Google Earth alongside fieldwork data, we digitized 434 gully erosion events to construct the comprehensive inventory map. These data were divided into two groups: training (70%) and test (30%). Based on the literature research and the multicollinearity test, 11 conditioning factors were selected. The receiver operating characteristic (ROC) approach and other statistical measures were used to quantify the model's accuracy. The study findings highlight the significance of drainage density, slope, NDVI, and distance from roads as crucial factors influencing gully erosion in the study area. Among the evaluated machine learning algorithms, the random forest (RF) model exhibited the highest performance, with an area under the curve (AUC) value of 0.932. It was followed by adaptive boosting (AB) with an AUC of 0.902 and gradient-boosted decision trees (GBDT) with an AUC of 0.893. The maps produced reveal that the southern and central regions of the study area have the classes of very high and high gully erosion susceptibility. The outputs of the current study can be used by decision-makers to improve prevention planning and mitigation techniques against gully erosion damage.
英文关键词	Gully erosion susceptibility N'fis river basin Machine learning model Risk assessment GIS
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001153835500001
WOS关键词	SOIL-EROSION ; LOGISTIC-REGRESSION ; SEDIMENT YIELD ; HIGH ATLAS ; MODELS ; CLASSIFICATION ; PREDICTION ; ENSEMBLE ; WEIGHT ; BASIN
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403549
推荐引用方式 GB/T 7714	Naceur, Hassan Ait,Abdo, Hazem Ghassan,Igmoullan, Brahim,et al. Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS[J],2024,83(3).
APA	Naceur, Hassan Ait,Abdo, Hazem Ghassan,Igmoullan, Brahim,Namous, Mustapha,Alshehri, Fahad,&Albanai, Jasem A..(2024).Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS.ENVIRONMENTAL EARTH SCIENCES,83(3).
MLA	Naceur, Hassan Ait,et al."Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS".ENVIRONMENTAL EARTH SCIENCES 83.3(2024).