scholarly journals New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1210 ◽  
Author(s):  
Dieu Tien Bui ◽  
Khabat Khosravi ◽  
Shaojun Li ◽  
Himan Shahabi ◽  
Mahdi Panahi ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Model Performance ◽  
Optimization Models ◽  
Topographic Wetness Index ◽  
Invasive Weed ◽  
Inference System ◽  
Conditioning Factors ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Prediction Capability

This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.

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 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.

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling 

2021 ◽  
Author(s):  
Ehsan Shahiri Tabarestani ◽  
Hossein Afzalimehr
Keyword(s):  
Characteristic Curve ◽  
Drainage Density ◽  
Weight Of Evidence ◽  
Mazandaran Province ◽  
Conditioning Factors ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Susceptibility Maps ◽  
Reliable Model ◽  
First Time

Abstract Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

scholarly journals IDENTIFYING KEY FACTORS OF COMMUTER TRAIN SERVICE QUALITY:AN EMPIRICAL ANALYSIS FOR DHAKA CITY

2019 ◽  
Vol 31 (2) ◽  
Author(s):  
Anika Nowshin Mowrin ◽  
Md. Hadiuzzaman ◽  
Saurav Barua ◽  
Md. Mizanur Rahman
Keyword(s):  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Confusion Matrix ◽  
Model Development ◽  
Model Performance ◽  
Total Sample ◽  
Dhaka City ◽  
Anfis Model ◽  
Regular User ◽  
Inference System

Commuter train is a viable alternative to road transport to ease the traffic congestion which requires appropriate planning by concerned authorities. The research is aimed to assess passengers’ perception about commuter train service running in areas near Dhaka city. An Adaptive Neuro Fuzzy Inference System (ANFIS) model has been developed to evaluate service quality (SQ) of commuter train. Field survey data has been conducted among 802 respondents who were the regular user of commuter train and 12 attributes have been selected for model development. ANFIS was developed by the training and then tested by 80% and 20% of the total sample respectively. After that, model performance has been evaluated by (i) Confusion Matrix (ii) Root Mean Square Error (RMSE) and attributes are ranked based on their relative importance. The proposed ANFIS model has 61.50% accuracy in training and 47.80% accuracy in testing.  From the results, it is found that 'Bogie condition', 'Cleanliness', ‘Female harassment’, 'Behavior of staff' and 'Toilet facility' are the most significant attributes. This indicates that some necessary measures should be taken immediately to recover the effects of these attributes to improve the SQ of commuter train. 

scholarly journals Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3489
Author(s):  
Saeid Mehdizadeh ◽  
Babak Mohammadi ◽  
Quoc Bao Pham ◽  
Zheng Duan
Keyword(s):  
Reference Evapotranspiration ◽  
Model Performance ◽  
Irrigation Scheduling ◽  
Hybrid Models ◽  
Empirical Models ◽  
Model Complexity ◽  
Coefficient Of Determination ◽  
Coupled Models ◽  
Invasive Weed ◽  
Inference System

Proper irrigation scheduling and agricultural water management require a precise estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly estimated, and used further to calculate the evapotranspiration of each crop. In this study, two new coupled models were developed for estimating daily ETo. Two optimization algorithms, the shuffled frog-leaping algorithm (SFLA) and invasive weed optimization (IWO), were coupled on an adaptive neuro-fuzzy inference system (ANFIS) to develop and implement the two novel hybrid models (ANFIS-SFLA and ANFIS-IWO). Additionally, four empirical models with varying complexities, including Hargreaves–Samani, Romanenko, Priestley–Taylor, and Valiantzas, were used and compared with the developed hybrid models. The performance of all investigated models was evaluated using the ETo estimates with the FAO-56 recommended method as a benchmark, as well as multiple statistical indicators including root-mean-square error (RMSE), relative RMSE (RRMSE), mean absolute error (MAE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). All models were tested in Tabriz and Shiraz, Iran as the two studied sites. Evaluation results showed that the developed coupled models yielded better results than the classic ANFIS, with the ANFIS-SFLA outperforming the ANFIS-IWO. Among empirical models, generally the Valiantzas model in its original and calibrated versions presented the best performance. In terms of model complexity (the number of predictors), the model performance was obviously enhanced by an increasing number of predictors. The most accurate estimates of the daily ETo for the study sites were achieved via the hybrid ANFIS-SFLA models using full predictors, with RMSE within 0.15 mm day−1, RRMSE within 4%, MAE within 0.11 mm day−1, and both a high R2 and NSE of 0.99 in the test phase at the two studied sites.

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