Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

Yen, Hoang Phan Hải; Pham, Binh Thai; Phong, Tran Van; Ha, Duong Hai; Costache, Romulus; Le, Hiep Van; Nguyen, Huu Duy; Amiri, Mahdis; Tảo, Nguyễn Văn; Prakash, Indra

doi:10.1016/j.gsf.2021.101154

Cited by 29 publications

(7 citation statements)

References 83 publications

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“…ADB is a popular algorithm used to improve the predictive ability of weak classifiers (Tien Bui et al, 2016). Training subsets are taken sequentially in the adaptive reinforcement group (Yen et al, 2021). The ADB algorithm works in three steps: (1) a subset is trained from the training dataset and then builds a model based on the original dataset, where the classifiers are assigned weights that are equal numbers; (2) the original model is used to predict the samples in the training set, and the wrong classifiers are assigned a larger weight, while the correct classifiers keep the same weight; and (3) the weights of all classifiers in the training dataset are normalized and a new subset of the training dataset is randomly taken to build the next classification model.…”

Section: Methods and Techniquesmentioning

confidence: 99%

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Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

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Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.

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Section: Methods and Techniquesmentioning

confidence: 99%

Section: Adaboostmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

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“…In recent years, hybrid models have become a more popular way to resolve the problems of mapping flood susceptibility (Nguyen et al 2021a;Yen et al 2021). Hybrid models combine individual models with metaheuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Nguyen

2022

Journal of Water and Climate Change

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The objective of this study was the development of an approach based on machine learning and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based Optimizer (GBO), Chaos Game Optimization (CGO), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Differential Evolution (DE) to construct flood susceptibility maps in the Ha Tinh province of Vietnam. The database includes 13 conditioning factors and 1,843 flood locations, which were split by a ratio of 70/30 between those used to build and those used to validate the model, respectively. Various statistical indices, namely root mean square error (RMSE), area under curve (AUC), mean absolute error (MAE), accuracy, and R1 score, were applied to validate the models. The results show that all the proposed models performed well, with an AUC value of more than 0.95. Of the proposed models, ANFIS-GBO was the most accurate, with an AUC value of 0.96. Analysis of the flood susceptibility maps shows that approximately 32–38% of the study area is located in the high and very high flood susceptibility zone. The successful performance of the proposed models over a large-scale area can support local authorities and decision-makers develop policies and strategies to reduce the threats related to flooding in the future.

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“…Susceptibility, vulnerability, hazards, potentiality, and other issues can now be studied using a multi-model approach and ensemble modeling (Talukdar and Pal, 2019;Islam et al, 2021;Mahato et al 2021;Talukdar et al 2021a). The ensemble models include AdaBoost (Ha et al 2021), bagging (Yen et al 2021), Reptree-bagging (Chen et al 2019a), dagging (Talukdar et al 2021a, b), and rotation forest (Mallick et al 2021c). Therefore, to increase the model's resilience for GWP mapping, we utilized six ensemble machine learning techniques in the present study, including RF, RS, bagging, dagging, NBT, and stacking.…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid models to enhance the efficiency of groundwater potentiality model

et al. 2022

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The present study aimed to create novel hybrid models to produce groundwater potentiality models (GWP) in the Teesta River basin of Bangladesh. Six ensemble machine learning (EML) algorithms, such as random forest (RF), random subspace, dagging, bagging, naïve Bayes tree (NBT), and stacking, coupled with fuzzy logic (FL) models and a ROC-based weighting approach have been used for creating hybrid models integrated GWP. The GWP was then verified using both parametric and nonparametric receiver operating characteristic curves (ROC), such as the empirical ROC (eROC) and the binormal ROC curve (bROC). We conducted an RF-based sensitivity analysis to compute the relevancy of the conditioning variables for GWP modeling. The very high and high groundwater potential regions were predicted as 831–1200 km2 and 521–680 km2 areas based on six EML models. Based on the area under the curve of the ROC, the NBT (eROC: 0.892; bROC: 0.928) model outperforms rest of the models. Six GPMs were considered variables for the next step and turned into crisp fuzzy layers using the fuzzy membership function, and the ROC-based weighting approach. Subsequently four fuzzy logic operators were used to assimilate the crisp fuzzy layers, including AND, OR, GAMMA0.8, and GAMMA 0.9, as well as GAMMA0.9. Thus, we created four hybrid models using FL model. The results of the eROC and bROC curve showed that GAMMA 0.9 operator outperformed other fuzzy operators-based GPMs in terms of accuracy. According to the validation outcomes, four hybrid models outperformed six EML models in terms of performance. The present study will aid in enhancing the efficiency of GPMs in preparing viable planning for groundwater management.

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Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province, Vietnam

Cited by 29 publications

References 83 publications

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Spatial modeling of flood hazard using machine learning and GIS in Ha Tinh province, Vietnam

Novel hybrid models to enhance the efficiency of groundwater potentiality model

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