Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping

Termeh, Seyed Vahid Razavi; Khosravi, Khabat; Sartaj, Majid; Keesstra, Saskia; Tsai, Frank T.-C.; Dijksma, R.; Pham, Binh Thai

doi:10.1007/s10040-019-02017-9

Cited by 86 publications

(28 citation statements)

References 71 publications

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“…The GAM is a semi-parametric extension version of the generalized linear model that can combine both linear and nonlinear relationships between the input predictors and the inventory dataset to determine the target data and the responses. Nonlinear problems apply smooth functions of covariance to transform the predictors [103]. The summation of smoothers in the predictors is considered the response [104].…”

Section: (4) Generalized Additive Model (Gam)mentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

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Section: (4) Generalized Additive Model (Gam)mentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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“…ROC curve is a popular measure to evaluate the accuracy of the model and can be used to determine the accuracy of natural hazard susceptibility mapping [63][64][65][66][67][68]. Two values are used to build the ROC curve: sensitivity and 100-specificity [69][70][71][72][73][74]. Performance of the models is analyzed quantitatively using the area under the curve (AUC) [75][76][77][78][79][80].…”

Section: Validation Methodsmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

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106

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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“…The ANFIS architecture consists of five principal layers such as fuzzification, rule, normalization, defuzzification and aggregation [37,[43][44][45][46]. Based on such construction, the neural network exhibits the ability to identify the parameters of FL algorithm [47][48][49]. In ANFIS, the Takagi-Sugeno if-then rules and appropriate membership function are employed for the fuzzy inference system [50,51].…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Pham

Dao

et al. 2019

Applied Sciences

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Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

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Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping

Cited by 86 publications

References 71 publications

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

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