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

Jaafari, Abolfazl; Panahi, Mahdi; Pham, Binh Thai; Shahabi, Himan; Bui, Dieu Tien; Rezaie, Fatemeh; Lee, Saro

doi:10.1016/j.catena.2018.12.033

Cited by 220 publications

(94 citation statements)

References 59 publications

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“…Regarding the set of influencing factors, we used correlation-based feature selection method [91] to measure the average merit of each factor for mapping the groundwater potential. We next overlaid the training and validations datasets with each one of the influencing factors to extract the factor values for generating the final training and validations datasets [92][93][94]. Using these datasets, groundwater potential mapping was formulated as a binary classification procedure, in which the goal was to distinguish between potential and non-potential groundwater classes.…”

Section: Modeling Methodologymentioning

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

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107

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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.

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Section: Modeling Methodologymentioning

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

107

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“…Over time, landslide susceptibility modeling has been considered using both qualitative (inventory-based analysis) and quantitative or data driven models [88,89]. Development of geographical information system (GIS) and machine learning algorithm has provided alternative decision tree (ADTree), support vector machine, artificial neural network and kernel logistic regression (KLR) advanced techniques with precise model building [90].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

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“…Furthermore, many studies have focused on the development of hybrid metaheuristic algorithms incorporated with typical models in order to achieve more powerful predictive tools [27][28][29]. Nguyen, et al [30] used particle swarm optimization (PSO) and artificial bee colony (ABC) metaheuristic techniques to optimize the performance of the ANN for landslide susceptibility mapping at northern Iran.…”

Section: Introductionmentioning

confidence: 99%

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

Mehrabi

Pradhan

Moayedi

et al. 2020

Sensors

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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

Cited by 220 publications

References 59 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

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

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

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