热门
Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
文献类型：期刊论文
| 作者 | Chen, Wei; Panahi, Mandi; Tsangaratos, Paraskevas; Shahabi, Himan; Ilia, Ioanna; Panahi, Somayeh; Li, Shaojun; Jaafari, Abolfazl; Bin Ahmad, Baharin |
| 刊名 | CATENA
 |
| 出版日期 | 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 |
| 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 国家重点实验室知识产出_期刊论文 |
| 作者单位 | 1.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Shaanxi, Peoples R China; 2.Islamic Azad Univ, North Tehran Branch, Young Researchers & Elites Club, Tehran, Iran; 3.Natl Tech Univ Athens, Sch Min & Met Engn, Dept Geol Sci, Lab Engn Geol & Hydrogeol, Zografou Campus Heroon Polytechniou 9, Zografos 15780, Greece; 4.Univ Kurdistan, Fac Nat Resources, Dept Geomorphol, Sanandaj, Iran |
| 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. |
入库方式： OAI收割
来源：武汉岩土力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。