干旱区生态环境知识资源中心(Arid): Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation

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DOI	10.1016/j.ecoinf.2022.101653
	Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation
	Saha, Asish; Pal, Subodh Chandra; Chowdhuri, Indrajit; Islam, Abu Reza Md. Towfiqul; Roy, Paramita; Chakrabortty, Rabin
通讯作者	Pal, SC
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2022
卷号	69
英文摘要	The phenomenon of land degradation is a serious environmental issue that affects many countries worldwide, particularly in the developing countries of sub-tropical regions like India. The assessment of land degradation is necessary for designing a mitigation plan that will reduce the adverse effects of land degradation. Recently, sensitivity models for analyzing land degradation have become a popular scientific tool for determining the spatial characteristics of this complicated environmental phenomenon. The objective of the current study is to prepare land degradation susceptibility maps for the gravely undulating red and lateritic agro-climatic zones (ACZ) of the Eastern plateau, India using hybrid techniques, i.e., integration of K-Fold cross-validation (CV) and machine learning algorithms of Reduced Error Pruning Tree (REPTree) and the ensemble of Bagging-REPTree and Boosting-REPTree. For the modelling purpose, sixteen independent land degradation conditioning factors were selected based on a multi-collinearity test, and dependent factors, i.e., gully and ravine points, were collected from published reports and field investigations. The evaluation result of the models indicates that Boosting-REPTree is the most optimal in prediction analysis, as the area under the curve (AUC) of training and validation is 0.944 and 0.928, respectively, in K-Fold 1 followed by Bagging-REPTree and REPTree. As a result, this study suggested that the ensemble of the Boosting-REPTree model can be applied as a new potential method for spatial prediction of land degradation in future research. The study also revealed that ex-situ plant species had been adopted to control soil erosion. Still, it is considered a false measure as ex-situ plant species cannot prevent erosion to an optimal level. Overall, a land degradation prevention planning map has also been suggested to measure soil erosion.
英文关键词	Red and lateritic agro-climatic zones Soil erosion Land degradation K-fold cross validation Boosting-REPTree Ex-situ plant species
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000802182400002
WOS关键词	LANDSLIDE SUSCEPTIBILITY ASSESSMENT ; LOGISTIC-REGRESSION ; SOIL-EROSION ; WATER ; DESERTIFICATION ; PRODUCTIVITY ; INDICATORS ; PREDICTION ; MODELS ; AREAS
WOS类目	Ecology
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/2XILL650/392365
推荐引用方式 GB/T 7714	Saha, Asish,Pal, Subodh Chandra,Chowdhuri, Indrajit,et al. Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation[J],2022,69.
APA	Saha, Asish,Pal, Subodh Chandra,Chowdhuri, Indrajit,Islam, Abu Reza Md. Towfiqul,Roy, Paramita,&Chakrabortty, Rabin.(2022).Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation.ECOLOGICAL INFORMATICS,69.
MLA	Saha, Asish,et al."Land degradation risk dynamics assessment in red and lateritic zones of eastern plateau, India: A combine approach of K-fold CV, data mining and field validation".ECOLOGICAL INFORMATICS 69(2022).