Assessing the relationship between landslide susceptibility and land cover change using machine learning

Duy Nguyen Huu; Tung Vu Cong; Petre Bretcan; Alexandru-Ionut Petrisor

doi:10.15625/2615-9783/20706

Author affiliations

Authors

Huu Duy Nguyen Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, 334 Nguyen Trai, Thanh Xuan district, Hanoi, Vietnam
Tung Cong Vu Center for Educational Testing, Vietnam National University, Ha Noi, 5th floor, HT2 Building, Hoa Lac Campus, Thach That district, Hanoi, Vietnam
Petre Bretcan Valahia University of Targoviste, Faculty of Humanities, Department of Geography, 130004 Targoviste, Romania
Alexandru-Ionut Petrisor 1-Doctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, Bucharest, Romania, 010014; 2-Department of Architecture, Faculty of Architecture and Urban Planning, Technical University of Moldova, 2004 Chisinau, Moldova; 3-National Institute for Research and Development in Constructions, Urbanism and Sustainable Spatial Development URBAN-INCERC, 21652 Bucharest, Romania; 4-National Institute for Research and Development in Tourism, 50741 Bucharest, Romania

DOI:

https://doi.org/10.15625/2615-9783/20706

Keywords:

machine learning, landslide susceptibility, Da Lat city, Vietnam

Abstract

Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.

Downloads

Download data is not yet available.

References

Abu El-Magd S.A., Ali S.A., Pham Q.B., 2021. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Science Informatics, 14(3), 1227–1243.

Achu A., Aju C., Di Napoli M., Prakash P., Gopinath G., Shaji E., Chandra V., 2023. Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis. Geoscience Frontiers, 14(6), 101657.

Achu A.L., Aju C.D., Di Napoli M., Prakash P., Gopinath G., Shaji E., Chandra V., 2023. Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis. Geoscience Frontiers, 14(6), 101657. https://doi.org/10.1016/j.gsf.2023.101657.

Al-Najjar H.A., Pradhan B., Kalantar B., Sameen M.I., Santosh M., Alamri A., 2021. Landslide susceptibility modeling: an integrated novel method based on machine learning feature transformation. Remote Sensing, 13(16), 3281.

Breiman L., 1996. Bagging predictors. Machine learning, 24, 123–140.

Bui Q.D., Ha H., Khuc D.T., Nguyen D.Q., von Meding J., Nguyen L.P., Luu C., 2023. Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam. Natural Hazards, 116(2), 2283–2309.

Cao W.G., Fu Y., Dong Q.Y., Wang H.G., Ren Y., Li Z.Y., Du Y.Y., 2023. Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning. China Geology, 6(3), 409–419. https://doi.org/10.31035/cg2023013.

Chang Z., Catani F., Huang F., Liu G., Meena S.R., Huang J., Zhou C., 2023. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors. Journal of Rock Mechanics and Geotechnical Engineering, 15(5), 1127–1143.

Chang Z., Huang J., Huang F., Bhuyan K., Meena S.R., Catani F., 2023. Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models. Gondwana Research, 117, 307–320.

Dahal R.K., Hasegawa S., Nonomura A., Yamanaka M., Masuda T., Nishino K., 2008. GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology, 54, 311–324.

Fatemi Aghda S., Bagheri V., Razifard M., 2018. Landslide susceptibility mapping using fuzzy logic system and its influences on mainlines in lashgarak region, Tehran, Iran. Geotechnical and Geological Engineering, 36, 915–937.

Ganesh B., Vincent S., Pathan S., Benitez S.R.G., 2023. Machine learning based landslide susceptibility mapping models and GB-SAR based landslide deformation monitoring systems: Growth and evolution. Remote Sensing Applications: Society and Environment, 29, 100905.

Ghasemain B., Asl D.T., Pham B.T., Avand M., Nguyen H.D., Janizadeh S., 2020. Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam J. Earth Sci., 42(3), 208–227. https://doi.org/10.15625/0866-7187/42/3/14952.

Goetz J., Brenning A., Petschko H., Leopold P., 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & Geosciences, 81, 1–11.

Hajek P., Abedin M.Z., Sivarajah U., 2023. Fraud detection in mobile payment systems using an XGBoost-based framework. Information Systems Frontiers, 25(5), 1985–2003.

Hong H., 2024. Landslide susceptibility assessment using locally weighted learning integrated with machine learning algorithms. Expert systems with Applications, 237, 121678.

Hong H., Liu J., Bui D.T, Pradhan B., Acharya T.D., Pham B.T., Zhu X.Z., Chen W., Ahmad B.B., 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena, 163, 399–413. https://doi.org/10.1016/j.catena.2018.01.005.

Huang F., Xiong H., Yao C., Catani F., Zhou C., Huang J., 2023. Uncertainties of landslide susceptibility prediction considering different landslide types. Journal of Rock Mechanics and Geotechnical Engineering, 15(3), 2954-2972. https://doi.org/10.1016/j.jrmge.2023.03.001.

Huang Z., Peng L., Li S., Liu Y., Zhou S., 2023. GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison. Environmental Science and Pollution Research, 30(38), 88612–88626.

Iban M.C., Bilgilioglu S.S., 2023. Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach. Stochastic Environmental Research and Risk Assessment, 37(6), 2243–2270.

Kavzoglu T., Colkesen I., Sahin E.K., 2019. Machine learning techniques in landslide susceptibility mapping: a survey and a case study. Landslides: Theory, practice and modelling, 283–301.

Khaliq A.H., Basharat M., Riaz M.T., Wani S., Al-Ansari N., Long B.L., Linh N.T.T., 2023. Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan. Ain Shams Engineering Journal, 14(3), 101907.

Kim J.-C., Lee S., Jung H.-S., Lee S., 2018. Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto International, 33(9), 1000–1015.

Le Minh N., Truyen P.T., Van Phong T., Jaafari A., Amiri M., Van Duong N., Nguyen V.B., Dao M.D., Prakash I., Pham B.T., 2023. Ensemble models based on radial basis function network for landslide susceptibility mapping. Environmental Science and Pollution Research, 30(44), 99380–99398.

Lee S., Min K., 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology, 40, 1095–1113.

Liu C., 2023. Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm. Earth Science Informatics, 16(4), 4131–4152.

Luu C., Nguyen D., Amiri M., Van Phong T., Bui Q., Prakash I., Pham B., 2022. Flood susceptibility modeling using Radial Basis Function Classifier and Fisher's linear discriminant function. Vietnam J. Earth Sci., 44(1), 55–75. https://doi. org/10.15625/2615-9783/16626.

Mandal B., Mondal S., Mandal S., 2023. GIS-based landslide susceptibility zonation (LSZ) mapping of Darjeeling Himalaya, India using weights of evidence (WoE) model. Arabian Journal of Geosciences, 16(7), 1–20.

Mandal I., Pal S., 2020. Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India. Advances in Space Research, 66(6), 1351–1371.

Merghadi A., Yunus A.P., Dou J., Whiteley J., Thai Pham B., Bui D.T., Ram A., Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225.

Nguyen H.D., Dang D.K., Bui Q.T., Petrisor A.I., 2023. Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam. Transactions in GIS, 27(5), 1614–1640.

Nguyen H.D., Nguyen Q.H., Du Q.V.V., Pham V.T., Pham L.T., Van Hoang T., Quang-Hai T., Quang-Thanh B., Petrisor A.I., 2023. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam. Geological Journal, 59(2), 636–658.

Nguyen H.D., Nguyen V.H., Du Q.V.V., Nguyen C.T., Dang D.K., Truong Q.H., Ngo D.B.T., Tran Q.T., Nguyen Q.H., Bui Q.-T., 2024. Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam. Earth Science Informatics, 1–21.

Nguyen H.D., Van C.P., Nguyen T.G., Dang D.K., Pham T.T.N., Nguyen Q.-H., Bui Q.T., 2023. Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam's Mekong River Delta. Environmental Science and Pollution Research, 1–18.

Nhu V.-H., Bui T.T., My L.N., Vuong H., Duc H.N., 2022. A new approach based on integration of random subspace and C4. 5 decision tree learning method for spatial prediction of shallow landslides. Vietnam J. Earth Sci., 44(3), 327–342. https://doi.org/10.15625/2615-9783/16929.

Nhu V.H., Thai B.P., Tien D.B., 2023. A novel swarm intelligence optimized extreme learning machine for predicting soil shear strength: A case study at Hoa Vuong new urban project (Vietnam). Vietnam J. Earth Sci., 45(2), 219–237. https://doi.org/10.15625/2615-9783/18338.

Niraj K., Singh A., Shukla D.P., 2023. Effect of the normalized difference vegetation index (NDVI) on GIS-enabled bivariate and multivariate statistical models for landslide susceptibility mapping. Journal of the Indian Society of Remote Sensing, 51(8), 1739–1756.

Nwazelibe V.E., Egbueri J.C., Unigwe C.O., Agbasi J.C., Ayejoto D.A., Abba S.I., 2023. GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach. Environmental Earth Sciences, 82(19), 439.

Petrişor A.I., 2015. Using CORINE data to look at deforestation in Romania: Distribution & possible consequences. Urbanism. Arhitectură. Construcţii, 6(1), 83–90.

Pham B.T., Tien Bui D., Prakash I., 2018. Bagging based support vector machines for spatial prediction of landslides. Environmental Earth Sciences, 77, 1–17.

Poddar I., Roy R., 2024. Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin. Quaternary Science Advances, 13, 100150.

Qasimi A.B., Isazade V., Enayat E., Nadry Z., Majidi A.H., 2023. Landslide susceptibility mapping in Badakhshan province, Afghanistan: A comparative study of machine learning algorithms. Geocarto International, 38(1), 2248082.

Qazi A., Singh K., Vishwakarma D.K., Abdo H.G., 2023. GIS based landslide susceptibility zonation mapping using frequency ratio, information value and weight of evidence: a case study in Kinnaur District HP India. Bulletin of Engineering Geology and the Environment, 82(8), 332.

Qiu Y., Wang J., Li Z., 2023. Personalized HRTF prediction based on LightGBM using anthropometric data. China Communications, 20(6), 166–177.

Rai S.C., Pandey V.K., Sharma K.K., Sharma S., 2024. Landslide susceptibility analysis in the Bhilangana Basin (India) using GIS-based machine learning methods. Geosystems and Geoenvironment, 100253. https://doi.org/10.1016/j.geogeo.2024.100253.

Rezapour H., Jamali S., Bahmanyar A., 2023. Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks. Energies, 16(12), 4636.

Mohamed S., Tayeb B., Icon, Mawloud G., Karim I.A., Sameh A.K., Tetsuya S., Hamouda B., Daisuke N., Emad M., 2022. Examining LightGBM

and CatBoost models for wadi flash flood susceptibility prediction. Geocarto International, 37(25), 7462–7487.

Sahin E.K., 2020. Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Sciences, 2(7), 1308.

Saito H., Nakayama D., Matsuyama H., 2009. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology, 109(3–4), 108–121.

Sharma A., Prakash C., 2023. Impact assessment of road construction on landslide susceptibility in mountainous region using GIS-based statistical modelling. Journal of the Geological Society of India, 99(8), 1131–1140.

Sharma N., Saharia M., Ramana G., 2024. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data. Catena, 235, 107653.

Taalab K., Cheng T., Zhang Y., 2018. Mapping landslide susceptibility and types using Random Forest. Big Earth Data, 2(2), 159–178.

Tien Bui D., Ho T.C., Revhaug I., Pradhan B., Nguyen D.B., 2014. Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. Paper presented at the Cartography from pole to pole: selected contributions to the XXVIth international conference of the ICA, Dresden 2013.

Tyagi A., Tiwari R.K., James N., 2023. Mapping the landslide susceptibility considering future land-use land-cover scenario. Landslides, 20(1), 65–76.

Ukey N., Yang Z., Li B., Zhang G., Hu Y., Zhang W., 2023. Survey on exact knn queries over high-dimensional data space. Sensors, 23(2), 629.

Vakhshoori V., Zare M., 2016. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards and Risk, 7(5), 1731–1752.

Van Phong T., Ly H.-B., Trinh P.T., Prakash I., Dao T.H., 2020. Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam J. Earth Sci., 42(3), 237–246. https://doi.org/10.15625/0866-7187/42/3/15047.

Viet Du Q.V., Nguyen H.D., Pham V.T., Nguyen C.H., Nguyen Q.-H., Bui Q.-T., Doan T.T., Tran A.T., Petrisor A.-I., 2023. Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam. Geocarto International, 2172218.

Wu Y., Ke Y., Chen Z., Liang S., Zhao H., Hong H., 2020. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena, 187, 104396.

Xuan B.T., Pham The T., Luu Thuy D., Tran Van P., Vuong Hong N., Van Le H., Duc Nguyen D., Prakash I., Pham Thanh T., Binh Thai B., 2024. Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model. Vietnam J. Earth Sci., 46(2), 272–281. https://doi.org/10.15625/2615-9783/20316.

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.

Ye P., Yu B., Chen W., Liu K., Ye L., 2022. Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China. Natural Hazards, 113(2), 965–995.

Yeon Y.-K., Han J.-G., Ryu K.H., 2010. Landslide susceptibility mapping in Injae, Korea, using a decision tree. Engineering Geology, 116(3-4),

–283.

Zeng T., Jin B., Glade T., Xie Y., Li Y., Zhu Y., Yin K., 2024. Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry. Catena, 236, 107732.

Zhang J., Ma X., Zhang J., Sun D., Zhou X., Mi C., Wen H., 2023. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model. Journal of Environmental Management, 332, 117357.

Zhang T., Fu Q., Wang H., Liu F., Wang H., Han L., 2022. Bagging-based machine learning algorithms for landslide susceptibility modeling. Natural Hazards, 110(2), 823–846.

Zhang T., Li S., Jing X., Song J., Shi L., He X., 2023. Negative comment recognition model based on lightGBM. Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973Z (28 March 2023). https://doi.org/10.1117/12.2672779.

Zhang W., He Y., Wang L., Liu S., Meng X., 2023. Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing. Geological Journal, 58(6), 2372-2387.

Zhou C., Yin K., Cao Y., Ahmed B., Li Y., Catani F., Pourghasemi H.R., 2018. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers & Geosciences, 112, 23–37.