气候变化领域集成服务门户(CCPortal): Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping

CCPortal

DOI	10.1007/s11069-020-04067-9
	Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping
	Paryani S.; Neshat A.; Javadi S.; Pradhan B.
发表日期	2020
ISSN	0921030X
起始页码	1961
结束页码	1988
卷号	103 期号:2
英文摘要	Abstract: Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWO SWARA, ANFIS - PSO SWARA, ANFIS - GWO CF and ANFIS - PSO CF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSO CF and ANFIS - GWO SWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk. Graphic abstract: [Figure not available: see fulltext.]. © 2020, Springer Nature B.V.
关键词	ANFIS GIS Grey wolf optimization Landslide susceptibility Particle swarm optimization
英文关键词	algorithm; comparative study; fuzzy mathematics; geological mapping; landslide; multicriteria analysis; numerical model; optimization; probability; qualitative analysis; quantitative analysis
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/205867
作者单位	Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran; Department of Irrigation and Drainage, College of Abouraihan, University of Tehran, Tehran, Iran; Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdong-roGwangin-gu, Seoul, 05006, South Korea
推荐引用方式 GB/T 7714	Paryani S.,Neshat A.,Javadi S.,et al. Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping[J],2020,103(2).
APA	Paryani S.,Neshat A.,Javadi S.,&Pradhan B..(2020).Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping.Natural Hazards,103(2).
MLA	Paryani S.,et al."Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping".Natural Hazards 103.2(2020).