Predict the Riverbank Erosion Susceptibility for the Ham Luong River Using the Logistic Regression Model
Main Article Content
Abstract
Riverbank erosion, once considered an inevitable process, has emerged as a severe and unpredictable issue, exacerbated by climate change and human activities. The Vietnamese Mekong Delta (VMD) has faced significant erosion, leading to considerable infrastructural damage and economic losses. This study aims to predict riverbank erosion susceptibility along the Ham Luong River using a logistic regression (LR) model for 100 riverbank locations, classified as eroded or stable, with twelve conditioning variables. The LR model achieved an overall accuracy (ACC) of 0.83 and an area under the receiver operating characteristic curve (AUC) of 0.86 through 15-fold cross-validation. Sensitivity analysis identified the bank slope, soil moisture, and bank height as key factors influencing erosion susceptibility. Probability analysis revealed that an increased bank slope may cause greater riverbank instability, whereas higher soil moisture levels may reduce erosion susceptibility. These findings highlight the importance of stabilizing bank slopes and maintaining soil moisture to mitigate the riverbank erosion susceptibility effectively, emphasizing the need for managing water levels in rivers and canals during the dry season as part of disaster risk management in the VMD.
References
[2] J. P. Bravard, M. Goichot, S. Gaillot, Geography of Sand and Gravel Mining in the Lower Mekong River, EchoGéo, No. 26, 2013, https://doi.org/10.4000/echogeo.13659.
[3] L. M. Hung, H. Tanaka, N. T. Tu, N. T. Viet, Prediction of Riverbank Erosion in the Lower Mekong River Delta, Vietnam-Japan Estuary Workshop, 2006, pp. 169-178.
[4] D. N. Khoi, T. D. Dang, L. T. H. Pham, P. T. Loi, N. T. D. Thuy, N. K. Phung, N. T. Bay, Morphological Change Assessment from Intertidal to River-Dominated Zones Using Multiple-Satellite Imagery: A Case Study of the Vietnamese Mekong Delta, Regional Studies in Marine Science, Vol. 34, 2020, https://doi.org/10.1016/j.rsma.2020.101087.
[5] D. V. Binh, B. Wietlisbach, S. Kantoush, H. H. Loc, E. Park, G. D. Cesare, D. H. Cuong, N. X. Tung, and T. Sumi, A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta, Remote Sensing, Vol. 12, No. 20, 2020, https://doi.org/10.3390/rs12203298.
[6] Y. Garosi, M. Sheklabadi, C. Conoscenti, H. R. Pourghasemi, K. V. Oost, Assessing the Performance of GIS-Based Machine Learning Models with Different Accuracy Measures for Determining Susceptibility to Gully Erosion, Science of The Total Environment, Vol. 664, 2019, pp. 1117-1132, https://doi.org/10.1016/j.scitotenv.2019.02.093.
[7] H. Sahour, V. Gholami, M. Vazifedan, S. Saeedi, Machine Learning Applications for Water-Induced Soil Erosion Modeling and Mapping, Soil and Tillage Research, Vol. 211, 2021, https://doi.org/10.1016/j.still.2021.105032.
[8] W. Chen, Y. Li, W. Xue, H. Shahabi, S. Li, H. Hong, X. Wang, H. Bian, S. Zhang, B. Pradhan,
B. B. Ahmad, Modeling Flood Susceptibility using Data-Driven Approaches of Naive Bayes Tree, Alternating Decision Tree, and Random Forest Methods, Science of The Total Environment, Vol. 701, 2020, pp. 134979, https://doi.org/10.1016/j.scitotenv.2019.134979.
[9] A. Saadon, J. Abdullah, N. S. Muhammad, J. Ariffin, Development of Riverbank Erosion Rate Predictor for Natural Channels using NARX-QR Factorization Model: a Case Study of Sg. Bernam, Selangor, Malaysia, Neural Computing and Applications, Vol. 32, No. 18, 2020, pp. 14839-14849,
https://doi.org/10.1007/s00521-020-04835-5.
[10] A. Saadon, J. Abdullah, N. S. Muhammad, J. Ariffin, P. Y. Julien, Predictive models for the estimation of riverbank erosion rates, Catena, Vol. 196, 2021, https://doi.org/10.1016/j.catena.2020.104917.
[11] L. V. Quyen, D. V. Binh, Assessing Riverbank Erosion in the Ham Luong River by Integrating Remote Sensing with Machine Learning and Digital Shoreline Analysis System, Journal of Hydro-meteorology, Vol. 8, No. 764, 2024, pp. 38-52, https://doi.org/10.36335/vnjhm.2024%28764%29.38-52, (in Vietnamese).
[12] D. Jurafsky, J. H. Martin, Logistic Regression, In: Speech and Language Processing, 3rd Edition, 2023, pp. 75-93, https://web.stanford.edu/~jurafsky/slp3/ed3book_dec302020.pdf (accessed on: June 1st, 2024).
[13] A. Merghadi, A. P. Yunus, J. Dou, J. Whiteley, B. ThaiPham, D. T. Bui, R. Avtar, B. Abderrahmane, Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance, Earth-Science Reviews, Vol. 207, 2020, pp. 103225, https://doi.org/10.1016/j.earscirev.2020.103225.
[14] D. W. Hosmer, J. S. Lemeshow, R. X. Sturdivant, Applied Logistic Regression, John Wiley & Sons, 2013.
[15] J. C. Lana, P. D. T. A. Castro, C. E. Lana, Assessing Gully Erosion Susceptibility and Its Conditioning Factors in Southeastern Brazil using Machine Learning Algorithms and Bivariate Statistical Methods: A Regional Approach, Geomorphology, Vol. 402, 2022, https://doi.org/10.1016/j.geomorph.2022.108159.
[16] A. Alin, Multicollinearity, Wiley Interdisciplinary Reviews Computational Stastistics, Vol. 2, 2010, pp. 370-374, https://doi.org/10.1002/wics.84.
[17] A. J. Henshaw, C. R. Thorne, and N. J. Clifford, Identifying Causes and Controls of River Bank Erosion in a British Upland Catchment, Catena, Vol. 100, 2013, pp. 107-119, https://doi.org/10.1016/j.catena.2012.07.015.