Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning

Bien, Tran Xuan; Jaafari, Abolfazl; Phong, Tran Van; Trinh, Phan Trong; Pham, Binh Thai

doi:10.1007/s12145-022-00925-1

Cited by 15 publications

(16 citation statements)

References 63 publications

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“…A false positive rate or probability of false alarm is defined as the rate of wrongly determined soil erosion locations. The ROC curve quantitatively measures model performance through the Area Under the ROC Curve (AUC) (Bien et al 2022(Bien et al , 2023. The AUC value ranges from 0.5 to 1.…”

Section: Validation Methodsmentioning

confidence: 99%

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Quyet Nguyen,

Thi Tran,

Thanh Thi Nguyen

et al. 2023

Journal of Hydroinformatics

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Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors.

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Section: Validation Methodsmentioning

confidence: 99%

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Quyet Nguyen,

Thi Tran,

Thanh Thi Nguyen

et al. 2023

Journal of Hydroinformatics

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“…Higher rainfall usually means higher groundwater recharge, but this also depends on other factors such as soil type, slope, and evapotranspiration (Lin et al, 2023;Lu et al, 2024). In semi-arid regions, where rainfall is scarce and erratic, groundwater recharge is often low and variable (Bien et al, 2023;Guo et al, 2023). Land use/cover affects the infiltration capacity of the soil, which is the ability of the soil to absorb water.…”

Section: Groundwater Conditioning Factorsmentioning

confidence: 99%

“…Higher rainfall usually leads to higher groundwater potential, as it increases the amount of water available for infiltration and recharge. However, this also depends on other factors that influence infiltration, such as soil type, slope, and evapotranspiration (Bien et al, 2023;Guo et al, 2023;Kayhomayoon, Ghordoyee-Milan, et al, 2022). Geology was the second most important factor influencing the groundwater potential.…”

Section: Factor Importancementioning

confidence: 99%

“…Avand et al (2020) applied the Dagging, AdaBoost, and Multi-Boosting ensemble learning to the Best-First tree (BFTree) method and demonstrated an improved GPM for the Yasuj-Dena region, Iran. Bien et al (2023) showed that a combination of the Forest by Penalizing Attributes method and the Decorate ensemble learning technique performed better than the single Multilayer Perception Network, Partial Decision Tree, and Fuzzy Unordered Rule Induction Algorithm methods for GPM in Vietnam's Central Highlands.…”

Section: Introductionmentioning

confidence: 99%

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Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

Wang,

Le,

Tian

et al. 2024

Earth and Space Science

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Groundwater potential maps are crucial tools for effectively managing water resources, particularly in agriculturally focused countries such as Vietnam. However, creating these maps is a challenging task that requires reliable data and methods. In this study, we integrated the Split‐Point Sampling and Node Attribute Subsampling Classifier (SPAARC) with the Bagging (B), MultiBoostAB (MBAB), and Random Subspace (RSS) ensemble learning techniques and developed three ensemble models: B‐SPAARC, MBAB‐SPAARC, and RSS‐SPAARC. We selected 13 geoenvironmental factors based on their availability, relevance, and association with groundwater potential in the Sesan River basin of Vietnam. We assessed the models' performance using various metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and RMSE. The findings indicated that the ensemble models performed better than the single SPAARC model in mapping groundwater potential. The MBAB‐SPAARC model demonstrated the highest accuracy with an AUC value of 0.891, followed by B‐SPAARC (AUC = 0.844), RSS‐SPAARC (AUC = 0.871), and the single SPAARC (AUC = 0.853) models. The results also highlighted that elevation, rainfall, land use/cover, and altitude were the most significant factors for mapping groundwater potential in the Sesan River basin. The innovative ensemble models and reliable potential maps developed in this study assist water resource managers in planning water usage based on the benefits and costs for various users and in devising sustainable strategies for using, protecting, and managing groundwater.

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“…In the last decade, machine learning-based approaches have been increasingly applied for mapping potential groundwater areas (Bien, Jaafari, Van Phong, Trinh, & Pham, 2023). These methods offer several advantages, including the ability to address complex and nonlinear relationships between groundwater occurrence and the associated predictors, resulting in more accurate and reliable outcomes.…”

Section: Introductionmentioning

confidence: 99%

A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential

Nhu,

Phan,

Hoa

et al. 2024

IEEE Access

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Groundwater spring plays a crucial role in human life, including water resource management and planning; therefore, developing accurate prediction models for groundwater spring potential mapping is essential. The objective of this research is to introduce and confirm a new modeling approach based on TensorFlow Deep Neural Networks (TF-DNN) and multisource geospatial data for spatial prediction of groundwater spring potential, with a case study in the tropical province in the central highland of Vietnam. For this task, the TF-DNN model structure with three hidden layers with 32 neurons each was established; therein, the Adaptive Moment Estimation (ADAM) algorithm was used as an optimizer, whereas the Rectified Linear Unit (ReLU) was used as the activation function, and the sigmoid was used as the transfer function. A geospatial database for the study area, consisting of 733 groundwater spring locations and 12 influencing factors, was prepared in ArcGIS Pro. Then, it was used to develop and verify the TF-DNN model. Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, and Classification and Regression Trees were used as a benchmark for the model comparison. The results demonstrate that the proposed TF-DNN model (Accuracy = 80.5%, F-score = 0.797, and AUC = 0.864) achieves a high global prediction performance, outperforming the benchmark models. Thus, the TF-DNN represents a novel and effective tool for spatially predicting groundwater spring potential mapping. The groundwater spring potential map generated in this study has the potential to assist provincial authorities in formulating strategies concerning water management and socio-economic development.

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Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning

Cited by 15 publications

References 63 publications

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Novel Ensemble Models Based on the Split‐Point Sampling and Node Attribute Subsampling Classifier for Groundwater Potential Mapping

A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential

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