Hybrid computational intelligence models for groundwater potential mapping

Pham, Binh Thai; Jaafari, Abolfazl; Prakash, Indra; Singh, Sushant K.; Quoc, Nguyen Kim; Bui, Dieu Tien

doi:10.1016/j.catena.2019.104101

Cited by 121 publications

(47 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are in line with previous works that demonstrated the advantages of ensemble modeling approaches over single simple modeling. For example, J48 decision tree integrated with Bagging [97] and Naïve Bayes tree integrated with Random Subspace [98] for landslide prediction, RF integrated with different ensemble techniques for gully erosion [31], and alternating decision tree integrated with AdaBoost [29], fisher's linear discriminant function integrated with Bagging [99], RF integrated with Random Subspace [14], and decision stump with different ensemble techniques for groundwater potential mapping [100]. (3), and RMSE (0.504) ( Table 2).…”

Section: Model Performancementioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

Self Cite

113

View full text Add to dashboard Cite

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

show abstract

Section: Model Performancementioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

Self Cite

113

View full text Add to dashboard Cite

show abstract

“…(ii) Data pre-processing: in this step, PCA and the Savitzky-Golay filter were used to reduce the dimensions data and reduce extreme values in the distribution of data. (iii) Data preparation: in this study, the holdout validation method was used for training and validating the models as it is a popular and effective method for generating the datasets for training and testing the models [24,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], and thus the collected data were divided into two parts. The first part included 70% data which was used to train the models, whereas the second part contained 30%, the remaining data and this was used to validate the models as the ratio 70/30 for dividing the training and testing dataset was a common ratio used in applying the ML models [29,[62][63][64][65][66][67][68][69][70][71].…”

Section: Methodology Frameworkmentioning

confidence: 99%

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

et al. 2019

Self Cite

View full text Add to dashboard Cite

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

show abstract

“…Slope is an essential factor for studying flash flood susceptibility because it controls the speed of water flow from high to low altitude [49]. In this study, five main classes are used for the slope map ( Figure 2a).…”

Section: Flash Flood Influencing Parametersmentioning

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

Self Cite

109

View full text Add to dashboard Cite

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.

show abstract

Hybrid computational intelligence models for groundwater potential mapping

Cited by 121 publications

References 51 publications

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

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

Contact Info

Product

Resources

About