干旱区生态环境知识资源中心(Arid): Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya

Arid

DOI	10.3390/land12040890
	Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya
	Were, Kennedy; Kebeney, Syphyline; Churu, Harrison; Mutio, James Mumo; Njoroge, Ruth; Mugaa, Denis; Alkamoi, Boniface; Ng'etich, Wilson; Singh, Bal Ram
通讯作者	Were, K
来源期刊	LAND
EISSN	2073-445X
出版年	2023
卷号	12 期号:4
英文摘要	This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) in a Kenyan semi-arid landscape. A total of 431 geo-referenced gully erosion points were gathered through a field survey and visual interpretation of high-resolution satellite imagery on Google Earth, while 24 raster-based GECFs were retrieved from the existing geodatabases for spatial modeling and prediction. The resultant models exhibited excellent performance, although the machine learners outperformed the benchmark LR technique. Specifically, the RF and BRT models returned the highest area under the receiver operating characteristic curve (AUC = 0.89 each) and overall accuracy (OA = 80.2%; 79.7%, respectively), followed by the SVM and LR models (AUC = 0.86; 0.85 & OA = 79.1%; 79.6%, respectively). In addition, the importance of the GECFs varied among the models. The best-performing RF model ranked the distance to a stream, drainage density and valley depth as the three most important GECFs in the region. The output gully erosion susceptibility maps can support the efficient allocation of resources for sustainable land management in the area.
英文关键词	soil erosion land degradation sustainable land management landscape restoration spatial prediction machine learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SSCI
WOS记录号	WOS:000979000200001
WOS关键词	LANDSLIDE SUSCEPTIBILITY ; MODELS ; REGRESSION ; PERFORMANCE ; LAND ; TREE
WOS类目	Environmental Studies
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/2XILL650/397650
推荐引用方式 GB/T 7714	Were, Kennedy,Kebeney, Syphyline,Churu, Harrison,et al. Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya[J],2023,12(4).
APA	Were, Kennedy.,Kebeney, Syphyline.,Churu, Harrison.,Mutio, James Mumo.,Njoroge, Ruth.,...&Singh, Bal Ram.(2023).Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya.LAND,12(4).
MLA	Were, Kennedy,et al."Spatial Prediction and Mapping of Gully Erosion Susceptibility Using Machine Learning Techniques in a Degraded Semi-Arid Region of Kenya".LAND 12.4(2023).