Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility

doi:10.1016/j.catena.2018.08.025

CORC > 武汉岩土力学研究所 > 中科院武汉岩土力学所 > 岩土力学所知识全产出 > 期刊论文

	Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
	Chen, Wei ; Panahi, Mandi ; Tsangaratos, Paraskevas ; Shahabi, Himan ; Ilia, Ioanna ; Panahi, Somayeh ; Li, Shaojun ; Jaafari, Abolfazl ; Bin Ahmad, Baharin
刊名	CATENA
	2019
卷号	172 期号:-页码:212-231
关键词	Landslide susceptibility SWARA ANFIS SFLA PSO
ISSN号	0341-8162
DOI	10.1016/j.catena.2018.08.025
英文摘要	The main objective of the present study was to produce a novel ensemble data mining technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by Shuffled Frog Leaping Algorithm (SFLA) and Particle Swarm Optimization (PSO) for spatial modeling of landslide susceptibility. Step-wise Assessment Ratio Analysis (SWARA) was utilized for the evaluation of the relation between landslides and landslide-related factors providing ANFIS with the necessary weighting values. The developed methods were applied in Langao County, Shaanxi Province, China. Eighteen factors were selected based on the experience gained from studying landslide phenomena, the local geo-environmental conditions as well as the availability of data, namely; elevation, slope aspect, slope angle, profile curvature, plan curvature, sediment transport index, stream power index, topographic wetness index, land use, normalized difference vegetation index, rainfall, lithology, distance to faults, fault density, distance to roads, road density, distance to rivers and river density. A total of 288 landslides were identified after analyzing previous technical surveys, airborne imagery and conducting field surveys. Also, 288 non-landslide areas were identified with the usage of Google Earth imagery and the analysis of a digital elevation model. The two datasets were merged and later divided into two subsets, training and testing, based on a random selection scheme. The produced landslide susceptibility maps were evaluated by the receiving operating characteristic and the area under the success and predictive rate curves (AUC). The results showed that AUC based on the training and testing dataset was similar and equal to 0.89. However, the processing time during the training and implementation phase was considerable different. SWARA-ANFIS-PSO appeared six times faster in respect to the processing time achieved by SWARA-ANFIS-SFLA. The proposed novel approach, which combines expert knowledge, neuro-fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility.
WOS研究方向	Geology ; Agriculture ; Water Resources
语种	英语
WOS记录号	WOS:000449136800022
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/15098]
专题	岩土力学所知识全产出_期刊论文
作者单位	1.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran 2.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; 3.Islamic Azad Univ, North Tehran Branch, Young Researchers & Elites Club, Tehran, Iran; 4.Natl Tech Univ Athens, Sch Min & Met Engn, Dept Geol Sci, Lab Engn Geol & Hydrogeol, Zografou Campus Heroon Polytechniou 9, Zografos 15780, Greece;
推荐引用方式 GB/T 7714	Chen, Wei,Panahi, Mandi,Tsangaratos, Paraskevas,et al. Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility[J]. CATENA,2019,172(-):212-231.
APA	Chen, Wei.,Panahi, Mandi.,Tsangaratos, Paraskevas.,Shahabi, Himan.,Ilia, Ioanna.,...&Bin Ahmad, Baharin.(2019).Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility.CATENA,172(-),212-231.
MLA	Chen, Wei,et al."Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility".CATENA 172.-(2019):212-231.