气候变化领域集成服务门户(CCPortal): Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping

CCPortal

DOI	10.1007/s11069-021-04547-6
	Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping
	Lucchese L.V.; de Oliveira G.G.; Pedrollo O.C.
发表日期	2021
ISSN	0921030X
起始页码	2381
结束页码	2405
卷号	106 期号:3
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.
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
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/206392
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
推荐引用方式 GB/T 7714	Lucchese L.V.,de Oliveira G.G.,Pedrollo O.C.. Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping[J],2021,106(3).
APA	Lucchese L.V.,de Oliveira G.G.,&Pedrollo O.C..(2021).Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping.Natural Hazards,106(3).
MLA	Lucchese L.V.,et al."Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping".Natural Hazards 106.3(2021).