干旱区生态环境知识资源中心(Arid): Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models

Arid

DOI	10.1007/s11356-023-28276-4
	Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models
	Das, Bappa; Desai, Sujeet; Daripa, Amrita; Anand, Gurav Chandrakant; Kumar, Uttam; Khalkho, Dhiraj; Thangavel, Velumani; Kumar, Nirmal; Reddy, Gangalakunta Obi P.; Kumar, Parveen
通讯作者	Kumar, P
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2023
卷号	30 期号:35 页码:83975-83990
英文摘要	Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP-based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF), and support vector machine (AHP-SVM) model. The model performances were assessed through an area under the receiver operating characteristic (AUC). The AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977), and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.
英文关键词	AHP Machine learning models Hybrid modelling Land degradation vulnerability index Mandovi river basin
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:001014741600009
WOS关键词	SOIL-MOISTURE ; DESERTIFICATION ; QUALITY ; CARBON ; NDVI
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/2XILL650/396280
推荐引用方式 GB/T 7714	Das, Bappa,Desai, Sujeet,Daripa, Amrita,et al. Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models[J],2023,30(35):83975-83990.
APA	Das, Bappa.,Desai, Sujeet.,Daripa, Amrita.,Anand, Gurav Chandrakant.,Kumar, Uttam.,...&Kumar, Parveen.(2023).Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(35),83975-83990.
MLA	Das, Bappa,et al."Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.35(2023):83975-83990.