Prediction of coastal erosion susceptible areas of Quang Nam Province, Vietnam using machine learning models.
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| Title: | Prediction of coastal erosion susceptible areas of Quang Nam Province, Vietnam using machine learning models. |
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| Authors: | Thanh, Bui Nhi1,2 (AUTHOR), Van Phong, Tran2,3 (AUTHOR), Trinh, Phan Trong2,3 (AUTHOR), Costache, Romulus4,5 (AUTHOR), Amiri, Mahdis6 (AUTHOR), Nguyen, Dam Duc7 (AUTHOR), Le, Hiep Van7 (AUTHOR), Prakash, Indra8 (AUTHOR), Pham, Binh Thai7 (AUTHOR) binhpt@utt.edu.vn |
| Source: | Earth Science Informatics. Feb2024, Vol. 17 Issue 1, p401-419. 19p. |
| Subject Terms: | *Machine learning, *Coastal changes, *Erosion, *Standard deviations, *Advection |
| Geographic Terms: | Vietnam |
| Abstract: | Globally, coastal erosion significantly impacts the socio-economic conditions and infrastructure development of coastal regions, with Vietnam facing considerable challenges due to its extensive coastline. This study focuses on developing innovative hybrid machine learning models, namely BLWL and CGLWL, which combine Locally Weighted Learning (LWL) and two optimization techniques, namely Bagging and Cascade Generalization, respectively. Quang Nam Province in Vietnam consistently affected by coastal erosions, serves as the case study. For model development, a set of historical coastal erosions and the affecting factors, such as magnitude of horizontal flow (sea currents), wave height, wave direction, distance to fault, geology, river density, elevation, curvature, aspect, slope degree, and topographic wetness index were collected and used for generation of the database. For the selection and prioritization of affecting coastal erosion factors, Correlation Attribute Evaluation (CAE) method was used. Performance of the models was evaluated using standard statistical measures: Accuracy Assessment (ACC), Sensitivity (SST), Specificity (SPF), Root Mean Squared Errors (RMSE), Kappa (K), Positive Predictive Value (PPV), and Negative Predictive Value (NPV), and Area Under the ROC Curve (AUC). Results indicated that the BLWL model (AUC: 0.978) was the best, followed by CGLWL (AUC: 0.968) and LWL (AUC: 0.963) models in accurately predicting coastal erosion susceptible areas. Therefore, it can be concluded that BLWL is a promising tool for the development of coastal erosion susceptibility maps, facilitating effective planning and management to mitigate the impact of coastal erosion. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Globally, coastal erosion significantly impacts the socio-economic conditions and infrastructure development of coastal regions, with Vietnam facing considerable challenges due to its extensive coastline. This study focuses on developing innovative hybrid machine learning models, namely BLWL and CGLWL, which combine Locally Weighted Learning (LWL) and two optimization techniques, namely Bagging and Cascade Generalization, respectively. Quang Nam Province in Vietnam consistently affected by coastal erosions, serves as the case study. For model development, a set of historical coastal erosions and the affecting factors, such as magnitude of horizontal flow (sea currents), wave height, wave direction, distance to fault, geology, river density, elevation, curvature, aspect, slope degree, and topographic wetness index were collected and used for generation of the database. For the selection and prioritization of affecting coastal erosion factors, Correlation Attribute Evaluation (CAE) method was used. Performance of the models was evaluated using standard statistical measures: Accuracy Assessment (ACC), Sensitivity (SST), Specificity (SPF), Root Mean Squared Errors (RMSE), Kappa (K), Positive Predictive Value (PPV), and Negative Predictive Value (NPV), and Area Under the ROC Curve (AUC). Results indicated that the BLWL model (AUC: 0.978) was the best, followed by CGLWL (AUC: 0.968) and LWL (AUC: 0.963) models in accurately predicting coastal erosion susceptible areas. Therefore, it can be concluded that BLWL is a promising tool for the development of coastal erosion susceptibility maps, facilitating effective planning and management to mitigate the impact of coastal erosion. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 18650473 |
| DOI: | 10.1007/s12145-023-01182-6 |
