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

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.

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Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility

CATENA ◽

10.1016/j.catena.2018.12.033 ◽

2019 ◽

Vol 175 ◽

pp. 430-445 ◽

Cited By ~ 92

Author(s):

Abolfazl Jaafari ◽

Mahdi Panahi ◽

Binh Thai Pham ◽

Himan Shahabi ◽

Dieu Tien Bui ◽

...

Keyword(s):

Fuzzy Inference System ◽

Landslide Susceptibility ◽

Fuzzy Inference ◽

Optimization Algorithms ◽

Spatial Prediction ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Inference System ◽

Neuro Fuzzy ◽

Meta Optimization

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Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

10.21203/rs.3.rs-299575/v1 ◽

2021 ◽

Author(s):

Sina Paryani ◽

Aminreza Neshat ◽

Biswajeet Pradhan

Keyword(s):

Decision Making ◽

Land Use ◽

Landslide Susceptibility ◽

Fuzzy Inference ◽

Landslide Susceptibility Mapping ◽

Multi Criteria Decision Making ◽

Inference System ◽

Susceptibility Maps ◽

Neuro Fuzzy ◽

Landslide Susceptibility Maps

Abstract Landslide is a type of slope processes causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e. the stepwise weight assessment ratio analysis (SWARA) and the new best-worst method (BWM) techniques. For this purpose, the first step was to prepare a landslide inventory map, which were then divided randomly by the ratio of 30/70 for model training and validation. Thirteen conditioning factors were used as slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, profile curvature, distance to roads, distance to streams, distance to faults, lithology, land use, rainfall and normalized difference vegetation index (NDVI). After the database was created, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-SWARA and ANFIS-BWM hybrid models, and the ROC curve was employed to appraise the predictive accuracy of each model. The results showed that the areas under curves (AUC) for the ANFIS-SWARA and ANFIS-BWM models were 73.6% and 75% respectively, and that the novel BWM yielded more realistic relationships between effective factors and the landslides. As a result, it was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon.

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Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

Geomorphology ◽

10.1016/j.geomorph.2017.09.007 ◽

2017 ◽

Vol 297 ◽

pp. 69-85 ◽

Cited By ~ 106

Author(s):

Wei Chen ◽

Hamid Reza Pourghasemi ◽

Mahdi Panahi ◽

Aiding Kornejady ◽

Jiale Wang ◽

...

Keyword(s):

Support Vector Machine ◽

Fuzzy Inference System ◽

Landslide Susceptibility ◽

Generalized Additive Model ◽

Fuzzy Inference ◽

Additive Model ◽

Spatial Prediction ◽

Support Vector ◽

Inference System ◽

Neuro Fuzzy

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Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4771-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 4771-4792 ◽

Cited By ~ 44

Author(s):

Khabat Khosravi ◽

Mahdi Panahi ◽

Dieu Tien Bui

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Spatial Prediction ◽

Bees Algorithm ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Inference System ◽

Conditioning Factors ◽

Neuro Fuzzy ◽

Potential Mapping

Abstract. Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70 % (1725 locations) were used for training models and the remaining 30 % (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC = 0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.

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