scholarly journals A comprehensive study of new hybrid models for Adaptive Neuro-Fuzzy Inference System (ANFIS) with Invasive Weed Optimization (IWO), Differential Evolution (DE), Firefly (FA), Particle Swarm Optimization (PSO) and Bees (BA) algorithms for spatial prediction of groundwater spring potential mapping

2018 ◽  
Author(s):  
Khabat Khosravi ◽  
Mahdi Panahi ◽  
Dieu Tien Bui
Keyword(s):  
Sustainable Management ◽  
Fuzzy Inference ◽  
Spatial Prediction ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Inference System ◽  
Conditioning Factors ◽  
Neuro Fuzzy ◽  
Potential Mapping

Abstract. Groundwater are one of the most valuable natural resources in the world and their sustainable management is necessary. One of the most important methods in managing groundwater is developing groundwater potential mapping (GPM). The current study benefits from a new hybrids of Adaptive Neuro-Fuzzy Inference System (ANFIS) with five meta-heuristic algorithms, namely Invasive Weed Optimization (IWO), Differential Evolution (DE), Firefly (FA), Particle Swarm Optimization (PSO) and Bees (BA) algorithms for spatial prediction of groundwater spring potential mapping at Koohdasht-Nourabad plain, Lorestan province, Iran. A total number of 2463 springs were identified and then divided in two classes randomly, including 70 % (1725 locations) of the springs were applied for model training and the remaining 30 % (738 spring locations), which were excluded in the training phase, were utilized for the model valuation. Thirteen groundwater occurrence conditioning factors, namely slope degree, slope aspect, altitude, curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land-use, rainfall, soil order and lithology (units) have been selected for modeling. The stepwise assessment ratio analysis (SWARA) method was applied to determine the spatial correlation between springs and conditioning factors. The accuracy of the map achieved after applying these five hybrid models was determined using the area under the receiver operating characteristic (ROC) curve (AUC). The results showed that ANFIS-DE has the highest prediction capability (0.875) for groundwater spring potential mapping in the study area, followed by ANFIS-IWO and ANFIS-FA (0.873), ANFIS-PSO (0.865) and ANFIS-BA (0.839). Results of Freidman and Wilcoxon signed rank test revealed that there were statistically significant differences between the models' performances except for ANFIS-FA vs. ANFIS-DE and ANFIS-PSO vs. ANFIS-DE. The results of this research can be useful for decision makers to sustainable management of groundwater resources.

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

2018 ◽  
Vol 22 (9) ◽  
pp. 4771-4792 ◽  
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.

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.

scholarly journals Efficiency of coupled invasive weed optimization-adaptive neuro fuzzy inference system method to assess physical habitats in streams

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Mahdi Sedighkia ◽  
Asghar Abdoli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Physical Habitat ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method ◽  
Habitat Simulation ◽  
Fish Habitats

AbstractThis study presents a coupled invasive weed optimization-adaptive neuro fuzzy inference system method to simulate physical habitat in streams. We implement proposed method in Lar national park in Iran as one of the habitats of Brown trout in southern Caspian Sea basin. Five indices consisting of root mean square error (RMSE), mean absolute error (MAE), reliability index, vulnerability index and Nash–Sutcliffe model efficiency coefficient (NSE) are utilized to compare observed fish habitats and simulated fish habitats. Based on results, measurement indices demonstrate model is robust to assess physical habitats in rivers. RMSE and MAE are 0.09 and 0.08 respectively. Besides, NSE is 0.78 that indicates robustness of model. Moreover, it is necessary to apply developed habitat model in a practical habitat simulation. We utilize two-dimensional hydraulic model in steady state to simulate depth and velocity distribution. Based on qualitative comparison between results of model and observation, coupled invasive weed optimization-adaptive neuro fuzzy inference system method is robust and reliable to simulate physical habitats. We recommend utilizing proposed model for physical habitat simulation in streams for future studies.

scholarly journals A OPTIMIZATION TUNED ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR DAM DEFORMATION PREDICTION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1207-1213
Author(s):  
X. Y. Zhang ◽  
B. Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Deformation Prediction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Dam Deformation

Abstract. The performance and stability of Adaptive Neuro-Fuzzy Inference System (ANFIS) depend on its network structure and preset parameter selection, and Particle Swarm Optimization-ANFIS (PSO-ANFIS) easily falls into the local optimum and is imprecise. A novel ANFIS algorithm tuned by Chaotic Particle Swarm Optimization (CPSO-ANFIS) is proposed to solve these problems. A chaotic ergodic algorithm is first used to improve the PSO and obtain a CPSO algorithm, and then the CPSO is used to optimize the parameters of ANFIS to avoid falling into the local optimum and improve the performance of ANFIS. Based on the deformation data from the Xiaolangdi Dam in China, three neural network algorithms, ANFIS, PSO-ANFIS, and CPSO-ANFIS, are used to establish the dam deformation prediction models after data preparation and selection of influencing factors for the dam deformation. The results are compared using evaluation indicators that show that CPSO-ANFIS is more accurate and stable than ANFIS and PSO-ANFIS both in predictive ability and in predicted results.

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