Landslide susceptibility in Phuoc Son, Quang Nam: A deep learning approach
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https://doi.org/10.15625/2615-9783/21658Keywords:
Phuoc Son, Quang Nam, deep learning, LSM, machine learningAbstract
Advanced machine learning and deep Learning modeling applications for landslide susceptibility mapping are becoming increasingly popular. This study applied a deep learning model (DL) with a multilayer neural network to landslide research in the Phuoc Son district, Quang Nam province. Two methods for selecting conditioning factors, Correlation Attribute and OneR, were used to choose 12 condition parameters for landslides (Slope, Relief, Elevation, Distance to road, Rainfall, Land use, Weathering crust, Geology, Aspect, Soil, Distance to fault, and Curvature). Comparing the predicted results with two standard models, Naïve Bayes (NB) and Support Vector Machine (SVM), showed that the DL model has higher and better prediction performance. Accordingly, the prediction performance of the DL model on the training dataset was ACC = 92.12%, AUC = 0.970, and on the validation dataset was ACC = 87.52, AUC = 0.944. The LSM developed based on the DL model indicates that areas with high landslide susceptibility are primarily concentrated in the southern part of the study area. These findings could be highly beneficial for urban planning management, risk management, and efforts to prevent and mitigate the damage caused by landslides in Phuoc Son.
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References
Alcántara-Ayala I., 2002. Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology, 47, 107–124.
Artur M., 2021. Review the performance of the Bernoulli Naïve Bayes Classifier in Intrusion Detection Systems using Recursive Feature Elimination with Cross-validated selection of the best number of features. Procedia Computer Science, 190, 564–570.
Azarafza M., Azarafza M., Akgün H., Atkinson P.M., Derakhshani R., 2021. Deep learning-based landslide susceptibility mapping. Scientific Reports, 11, 24112.
Badola S., Parkash S., 2024. Landslide Susceptibility Mapping Using Machine Learning in Himalayan Region: A Review, In: Pandey P.C., Kumar R., Pandey M., Giuliani G., Sharma R.K., Srivastava P.K. (Eds.), Geo-information for Disaster Monitoring and Management. Springer International Publishing, Cham, 123–143.
Banerjee M., Capozzoli M., McSweeney L., Sinha D., 1999. Beyond kappa: A review of interrater agreement measures. Canadian Journal of Statistics, 27, 3–23.
Bien T.X., Truyen P.T., Phong T.V., Nguyen D.D., Amiri M., Costache R., Duc D.M., Le H.V., Nguyen H.B.T., Prakash I., Pham B.T., 2022. Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm. Geocarto International, 37, 17777–17798.
Capitani M., Ribolini A., Bini M., 2013. The slope aspect: A predisposing factor for landsliding? Comptes Rendus Geoscience, 345, 427–438.
Carrión-Mero P., Montalván-Burbano N., Morante-Carballo F., Quesada-Román A., Apolo-Masache B., 2021. Worldwide Research Trends in Landslide Science. International Journal of Environmental Research and Public Health, 18, 9445.
Chen Y., Li N., Zhao B., Xing F., Xiang H., 2024. Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation - a case study of Wenchuan County, China. Geocarto International, 39, 2361714.
Dao D.V., Jaafari A., Bayat M., Mafi-Gholami D., Qi C., Moayedi H., Phong T.V., Ly H.-B., Le T.-T., Trinh P.T., Luu C., Quoc N.K., Thanh B.N., Pham B.T., 2020. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA, 188, 104451.
Denis R., Antonio G., 2022. Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4. 2nd ed. Packt Publishing. E-ISBN:9781803243481, 602pp.
Doan V.L., Nguyen B.-Q.-V., Nguyen C.C., Nguyen C.T., 2024. Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam. Vietnam Journal of Earth Sciences, 46(2), 202–220. https://doi.org/10.15625/2615-9783/20065.
Fang-Fang G.U.O., Nong Y., Hui M., Yue-Qiao Z., Bao-Ying Y.E., 2008. Application of the relief amplitude and slope analysis to regional landslide hazard assessments. Geology in China, 35, 131–143.
Finlay P.J., Fell R., Maguire P.K., 1997. The relationship between the probability of landslide occurrence and rainfall. Canadian Geotechnical Journal, 34, 811–824.
Froude M.J., Petley D.N., 2018. Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci., 18, 2161–2181.
Glade T., 2003. Landslide occurrence as a response to land use change: a review of evidence from New Zealand. Catena, 51, 297–314.
Gorokhovich Y., Vustianiuk A., 2021. Implications of slope aspect for landslide risk assessment: A case study of Hurricane Maria in Puerto Rico in 2017. Geomorphology, 391, 107874.
He R., Zhang W., Dou J., Jiang N., Xiao H., Zhou J., 2024. Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review. Rock Mechanics Bulletin, 3, 100144.
Ho J.-Y., Lee K.T., Chang T.-C., Wang Z.-Y., Liao Y.-H., 2012. Influences of spatial distribution of soil thickness on shallow landslide prediction. Engineering Geology, 124, 38–46.
Hung P.V., 2011. Assessment of the Current Status and Delineation of Landslide Risk Zones in Quang Nam Province (In Vietnamese). Vietnam Journal of Earth Sciences, 33(3), 518–525.
Janiesch C., Zschech P., Heinrich K., 2021. Machine learning and deep Learning. Electronic Markets, 31, 685–695.
Jiang L., Wang S., Li C., Zhang L., 2016. Structure extended multinomial naive Bayes. Information Sciences, 329, 346–356.
Ke T., Ge X., Yin F., Zhang L., Zheng Y., Zhang C., Li J., Wang B., Wang W., 2024. A general maximal margin hyper-sphere SVM for multi-class classification. Expert Systems with Applications, 237, 121647.
Kshetrimayum A., H.R., Goyal A., 2024. Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models. Journal of Spatial Science, 1–30.
Lang S., Bravo-Marquez F., Beckham C., Hall M., Frank E., 2019. WekaDeeplearning4j: A deep learning package for Weka based on Deeplearning4j. Knowledge-Based Systems, 178, 48–50.
Le Minh N., Truyen P.T., Van Phong T., Jaafari A., Amiri M., Van Duong N., Van Bien N., Duc D.M., Prakash I., Pham B.T., 2023. Ensemble models based on radial basis function network for landslide susceptibility mapping. Environmental Science and Pollution Research, 30, 99380–99398.
Le Cun Y., Bengio Y., Hinton G., 2015. Deep Learning. Nature, 521, 436–444.
Liu J., Huang L.-W., Shao Y.-H., Chen W.-J., Li C.-N., 2024. A nonlinear kernel SVM classifier via L0/1 soft-margin loss with classification performance. Journal of Computational and Applied Mathematics, 437, 115471.
Lombardo L., Opitz T., Ardizzone F., Guzzetti F., Huser R., 2020. Space-time landslide predictive modelling. Earth-Science Reviews, 209, 103318.
Lucchese L.V., de Oliveira G.G., Pedrollo O.C., 2020. Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment. Environmental Monitoring and Assessment, 192, 129.
Ma Y., Chen S., Ermon S., Lobell D.B., 2024. Transfer learning in environmental remote sensing. Remote Sensing of Environment, 301, 113924.
Madhu D., Nithya G.K., Sreekala S., Ramesh M.V., 2024. Regional-scale landslide modeling using machine learning and GIS: a case study for Idukki district, Kerala, India. Natural Hazards, 120, 9935–9956.
Moayedi H., Mehrabi M., Mosallanezhad M., Rashid A.S.A., Pradhan B., 2019. Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering with Computers, 35, 967–984.
Nettleton D., 2014. Chapter 6 - Selection of Variables and Factor Derivation, In: Nettleton, D. (Ed.), Commercial Data Mining. Morgan Kaufmann, Boston, 79–104.
Nguyen Huu D., Vu Cong T., Bretcan P., Petrisor A.-I., 2024. Assessing the relationship between landslide susceptibility and land cover change using machine learning. Vietnam Journal of Earth Sciences, 46(3), 339–359. https://doi.org/10.15625/2615-9783/20706.
Nie F., Hao Z., Wang R., 2024. Multi-Class Support Vector Machine with Maximizing Minimum Margin. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 14466–14473.
Novellino A., Pennington C., Leeming K., Taylor S., Alvarez I.G., McAllister E., Arnhardt C., Winson A., 2024. Mapping landslides from space: A review. Landslides, 21, 1041–1052.
Ohlmacher G.C., 2000. The Relationship Between Geology and Landslide Hazards of Atchison, Kansas, and Vicinity. Current Research in Earth Sciences, 1–16.
Ontivero-Ortega M., Lage-Castellanos A., Valente G., Goebel R., Valdes-Sosa M., 2017. Fast Gaussian Naïve Bayes for searchlight classification analysis. NeuroImage, 163, 471–479.
Pham Van T., Le Hong L., Tran Thanh N., Nguyen Quoc P., Phan Trong T., Dinh Thi Q., Dao Minh D., Nguyen Chau L., Nguyen Hai C., 2023. Mechanism and numerical simulation of a rapid deep-seated landslide in Van Hoi reservoir, Vietnam. Vietnam Journal of Earth Sciences, 45(3), 357–373. https://doi.org/10.15625/2615-9783/18539.
Pradhyumna P., Mohana, 2022. A Survey of Modern Deep Learning based Generative Adversarial Networks (GANs), 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 1146–1152.
Regmi A.D., Yoshida K., Dhital M.R., Devkota K., 2013. Effect of rock weathering, clay mineralogy, and geological structures in the formation of large landslide, a case study from Dumre Besei landslide, Lesser Himalaya Nepal. Landslides, 10, 1–13.
Shao X., Ma S., Xu C., Zhou Q., 2020. Effects of sampling intensity and non-slide/slide sample ratio on the occurrence probability of coseismic landslides. Geomorphology, 363, 107222.
Sharma N., Saharia M., Ramana G.V., 2024. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data. Catena, 235, 107653.
Soga K., Alonso E., Yerro A., Kumar K., Bandara S., 2016. Trends in large-deformation analysis of landslide mass movements with particular emphasis on the material point method. Géotechnique, 66, 248–273.
Song J., Gao S., Zhu Y., Ma C., 2019. A survey of remote sensing image classification based on CNNs. Big Earth Data, 3, 232–254.
Tharwat A., 2021. Classification assessment methods. Applied Computing and Informatics, 17, 168–192.
Thuc T., Thanh Thuy T., Huong H.T.L., 2023. Multi-hazard risk assessment of typhoon, typhoon-rainfall and post-typhoon-rainfall in the Mid-Central Coastal region of Vietnam. International Journal of Disaster Resilience in the Built Environment, 14, 402–419.
Wang H., Wang L., Zhang L., 2023. Transfer learning improves landslide susceptibility assessment. Gondwana Research, 123, 238–254.
Wickramasinghe I., Kalutarage H., 2021. Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Computing, 25, 2277–2293.
Witten I.H., Frank E., 2005. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems). 2nd ed. Morgan Kaufmann Publishers Inc, ISBN: 978-0120884070, 560pp,
Yang C., Liu L.-L., Huang F., Huang L., Wang X.-M., 2023. Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples. Gondwana Research, 123, 198–216.
Yang Z.-Q., Qi W.-W., Xu C., Shao X.-Y., 2024. Exploring deep Learning for landslide mapping: A comprehensive review. China Geology, 7, 330–350.
Yu Y., Si X., Hu C., Zhang J., 2019. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31, 1235–1270.
Zhong C., Liu Y., Gao P., Chen W., Li H., Hou Y., Nuremanguli T., Ma H., 2020. Landslide mapping with remote sensing: challenges and opportunities. International Journal of Remote Sensing, 41, 1555–1581.
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