DOI: 10.1007/s11069-021-04547-6
论文题名: Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping
作者: Lucchese L.V. ; de Oliveira G.G. ; Pedrollo O.C.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 106, 期: 3 起始页码: 2381
结束页码: 2405
语种: 英语
中文关键词: Fuzzy rule interpretation
; Map analysis
; Map validation
; Mass movement
; Natural disasters
; Rule set
英文关键词: artificial neural network
; digital elevation model
; fuzzy mathematics
; landslide
; mapping
; natural disaster
; rainfall
; Brazil
英文摘要: Two Artificial Intelligence (AI) methods, Fuzzy Inference System (FIS) and Artificial Neural Network (ANN), are applied to Landslide Susceptibility Mapping (LSM), to compare complementary aspects of the potentials of the two methods and to extract physical relationships from data. An index is proposed in order to rank and filter the FIS rules, selecting a certain number of readable rules for further interpretation of the physical relationships among variables. The area of study is Rolante river basin, southern Brazil. Eleven attributes are generated from a Digital Elevation Model (DEM), and landslide scars from an extreme rainfall event are used. Average accuracy and area under Receiver Operating Characteristic curve (AUC) resulted, respectively, in 81.27% and 0.8886 for FIS, and 89.45% and 0.9409 for ANN. ANN provides a map with more amplitude of outputs and less area classified as high susceptibility. Among the 40 (10%) best-ranked FIS rules, 13 have high susceptibility output, while 27 have low; a cause is that low susceptibility areas are larger on the map. Slope is highly connected to susceptibility. Elevation, when high (plateau) or low (floodplain), inhibits high susceptibility. Six attributes show the same fuzzy set for the 18 best-ranked rules, meaning this fuzzy set is common on the map. Overall findings point out that ANN is best suited for LSM map generation, but, based on them, using FIS is important to help researchers understand more about AI models for LSM and about landslide phenomenon. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169191
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre, RS 91501-970, Brazil; Departamento Interdisciplinar, Universidade Federal do Rio Grande do Sul, Rodovia RS 030, 11700, km 92. Emboaba, Tramandaí, RS 95590-000, Brazil
Recommended Citation:
Lucchese L.V.,de Oliveira G.G.,Pedrollo O.C.. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping[J]. Natural Hazards,2021-01-01,106(3)