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DOI	10.1007/s11053-019-09490-9
	Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms
	Zamanirad, Mahtab 1; Sarraf, Amirpouya 2; Sedghi, Hossein 1; Saremi, Ali 1; Rezaee, Payman 3
通讯作者	Sarraf, Amirpouya
来源期刊	NATURAL RESOURCES RESEARCH
ISSN	1520-7439
EISSN	1573-8981
出版年	2020
卷号	29 期号:2 页码:1127-1141
英文摘要	Groundwater over-exploitation in arid and semiarid environments has led to many land subsidence cases. Immense economic losses incurred from land subsidence occurrences prompted many scientists to model this phenomenon. To this end, we used three machine learning models, boosted regression trees (BRTs), generalized additive model (GAM), and random forest (RF), together with four anthropological and geo-environmental predictors, to produce a spatial prediction map across land subsidence-prone area in the south of Iran. The inventory map and preparatory thematic layers were generated through extensive field surveys, using Google Earth images, local information, and organizational archives. The results revealed that the GAM significantly out-performs the BRT in terms of high goodness of fit (84.3% vs. 80.2%) and predictive power (81.6% vs. 70.1%). The RF model, as a benchmark model, showed slightly higher goodness of fit (85.45%) compared to the GAM; however, its prediction power was evidently lower than the GAM. Hence, the GAM was found as the best susceptibility model in the study area. According to the relative contribution test, the drawdown of groundwater level with 77.5% contribution was found to be the main causative predictor of land subsidence occurrence, followed by lithology (19.2%), distance from streams (2.5%), and altitude (0.8%). The results of the GAM suggest that almost 31.6% of the study area is highly susceptible zone to land subsidence occurrence, which can be of interest for further pragmatic actions.
英文关键词	Land subsidence Boosted regression trees Generalized additive model GIS Kerdi Shirazi Plain
类型	Article
语种	英语
国家	Iran
收录类别	SCI-E
WOS记录号	WOS:000520600500032
WOS关键词	GENERALIZED ADDITIVE-MODELS ; FUZZY INFERENCE SYSTEM ; LANDSLIDE SUSCEPTIBILITY ; SPATIAL PREDICTION ; FREQUENCY RATIO ; REGRESSION ; INFORMATION ; DECISION ; HABITAT ; BASIN
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315213
作者单位	1.Islamic Azad Univ, Dept Water Engn, Sci & Res Branch, Tehran, Iran; 2.Islamic Azad Univ, Roudehen Branch, Dept Civil Engn, Roudehen, Iran; 3.Univ Hormozgan, Fac Sci, Dept Geol, Bandar Abbas, Hormuzgan, Iran
推荐引用方式 GB/T 7714	Zamanirad, Mahtab,Sarraf, Amirpouya,Sedghi, Hossein,et al. Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms[J],2020,29(2):1127-1141.
APA	Zamanirad, Mahtab,Sarraf, Amirpouya,Sedghi, Hossein,Saremi, Ali,&Rezaee, Payman.(2020).Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms.NATURAL RESOURCES RESEARCH,29(2),1127-1141.
MLA	Zamanirad, Mahtab,et al."Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms".NATURAL RESOURCES RESEARCH 29.2(2020):1127-1141.