Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area

2017 ◽  
Vol 78 (2) ◽  
pp. 1173-1187 ◽  
Author(s):  
Christos Polykretis ◽  
Christos Chalkias ◽  
Maria Ferentinou
Keyword(s):  
Fuzzy Inference System ◽  
Landslide Susceptibility ◽  
Fuzzy Inference ◽  
Susceptibility Assessment ◽  
Inference System ◽  
Hilly Area ◽  
Landslide Susceptibility Assessment ◽  
Neuro Fuzzy

scholarly journals Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

2019 ◽  
Vol 10 (1) ◽  
pp. 1879-1911 ◽  
Author(s):  
Hossein Moayedi ◽  
Mohammad Mehrabi ◽  
Bahareh Kalantar ◽  
Mohammed Abdullahi Mu’azu ◽  
Ahmad Safuan A. Rashid ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Metaheuristic Algorithms ◽  
Susceptibility Assessment ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1723 ◽  
Author(s):  
Mohammad Mehrabi ◽  
Biswajeet Pradhan ◽  
Hossein Moayedi ◽  
Abdullah Alamri
Keyword(s):  
Fuzzy Inference System ◽  
Landslide Susceptibility ◽  
Fuzzy Inference ◽  
State Of The Art ◽  
Spatial Prediction ◽  
Inference System ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Neuro Fuzzy ◽  
Landslide Susceptibility Maps

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.

Sign in / Sign up

Export Citation Format

Share Document