气候变化领域集成服务门户(CCPortal): Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques

CCPortal

DOI	10.1007/s11069-021-04805-7
	Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques
	Naemitabar M.; Zanganeh Asadi M.
发表日期	2021
ISSN	0921030X
起始页码	2423
结束页码	2453
卷号	108 期号:3
英文摘要	A landslide is a geomorphological hazard with significant ecological and economic damages. The present study aimed to identify landslide-prone areas in Farizi watershed via the Support Vector Machine (SVM), the boosted regression trees (BRT) model, a Logistic Model Tree (LMT), and the Random Forest (RF) algorithm with high computability. The effects on landslide occurrences in this study include altitude, slope, slope direction, distance to road, lithology, distance to waterway, land use, distance to fault, slope cross-section profile, slope longitudinal profile, precipitation, topographic wetness index, and soil layers. To use the soil layer, texture, bulk density, permeability, structure, and plasticity were conducted for analyses of soil physical properties. Geomorphologists examined each parameter according to its effect size on the landslide hazards and used it as a raster as background image ror other layers for the main layers in landslide susceptibility zoning. In order to evaluate the results of the models, data analysis was based on the calculation of the total area under the ROC curve obtained from 30% of landslides. The results showed that the SVM with the AUC as 0.86 and the RF algorithm with the AUC as 0.89 had better operating characteristic in landslide susceptibility zoning of the studied watershed. Prioritization of effective factors showed that lithology, slope, slope direction, distance to fault, and land use had the highest effects on landslide occurrences in the study area. As a result, our proposed methods can improve prediction performance, and the landslide prediction system can give warnings. Landslide susceptibility assessment is a complex and multistep process that has been studied by many researchers. In this study, the SVM, BRT, LMT, and RF algorithms to assess landslide susceptibility and its performance based on various statistical measurements have been discussed. The SVM map shows a high-risk zone covering 71% of the study area. Also, there are also scattered points in the landslide zone throughout the area. In the landslide susceptibility map extracted from the BRT algorithm, a large part of the high-risk zone covers 51% of the area. In the landslide susceptibility map extracted from the LMT algorithm, the high-risk zone covers 69% of the area, and in the landslide susceptibility map extracted from the RF algorithm, the high-risk covers 61% of the area. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Farizi watershed Landslide Logistic model tree (LMT)Support vector machine (SVM)The boosted regression trees (BRT) model The random forest (RF) algorithm
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206445
作者单位	Department of Physical Geography, Faculty of Geography and Environmental Science, Hakim Sabzevari University, Sabzevar, Iran
推荐引用方式 GB/T 7714	Naemitabar M.,Zanganeh Asadi M.. Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques[J],2021,108(3).
APA	Naemitabar M.,&Zanganeh Asadi M..(2021).Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques.Natural Hazards,108(3).
MLA	Naemitabar M.,et al."Landslide zonation and assessment of Farizi watershed in northeastern Iran using data mining techniques".Natural Hazards 108.3(2021).