Integrating remote sensing and artificial intelligence for landslide detection and susceptibility analysis along tourism routes in  Da Bac district, Hoa Binh province, Vietnam

Bac Dang Kinh; Thi Thu Hoang Huong; Hieu Nguyen; Kim Chi Vu; Tuan Linh Giang; Closson Damien; Thi Dieu Linh Nguyen; Thi Ngan Do

doi:10.15625/2615-9783/23462

Author affiliations

Authors

Kinh Bac Dang VNU University of Science, Vietnam National University, Thanh Xuan, Hanoi, Vietnam
Thi Thu Huong Hoang VNU University of Science, Vietnam National University, Thanh Xuan, Hanoi, Vietnam
Hieu Nguyen VNU University of Science, Vietnam National University, Thanh Xuan, Hanoi, Vietnam
Kim Chi Vu VNU Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, Thanh Xuan, Hanoi, Vietnam
Tuan Linh Giang VNU Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, Thanh Xuan, Hanoi, Vietnam
Closson Damien Ministry of Defense, Belgium
Thi Dieu Linh Nguyen VNU University of Science, Vietnam National University, Thanh Xuan, Hanoi, Vietnam
Thi Ngan Do VNU University of Science, Vietnam National University, Thanh Xuan, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/23462

Keywords:

Landslide warning system, machine learning, deep learning, mountainous tourism area, Vietnam

Abstract

The occurrence of natural disasters, especially with landslides, threatens mountainous districts and has serious consequences on local tourism development. Future disaster management must develop efficient innovative tools to control the rising frequency and intensity of landslides due to the impacts of economic development and climate change. Minimizing the risk and effects of these occurrences relies on the establishment of an optimal early warning system. This study focuses on the integration of artificial intelligence approaches to identify landslides and evaluate their susceptibility, with an emphasis on early warning systems on tourist routes in Da Bac district. As the first tool in the system, advanced deep learning models using satellite data at high resolution assist in identifying landslides. As a result, a developed DeepLab-v3 model demonstrated high performance by reaching 0.213 dice coefficient and 96.8% accuracy for landslide detection without restrictions from specific input resolution sizes. As the second tool, various machine learning tools, such as Random Forest and Support Vector Machine, utilize the identified landslide locations from the first tool to assess and map their susceptibility based on environmental and human-made factors. Accordingly, the study proposed an early warning system for landslide disaster management using real-time ecological factors and historical data. The proposed integrated system helps tourists and local communities take preventive actions that reduce landslide impacts, thus achieving safety goals in tourism activities, particularly in the Da Bac district of Hoa Binh province, Vietnam. It enhances strategies to minimize risk, increases the ability to predict landslide-prone tourist areas, and aids in implementing sustainable tourism in the future.

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References

Abdo H.G., Richi S.M., 2024. Application of machine learning in the assessment of landslide susceptibility: A case study of mountainous eastern Mediterranean region, Syria. J. King Saud Univ. Sci., 36, 103174. https://doi.org/10.1016/j.jksus.2024.103174.

Agboola G., Beni L.H., Elbayoumi T., Thompson G., 2024. Optimizing landslide susceptibility mapping using machine learning and geospatial techniques. Ecol. Inform., 81, 102583. https://doi.org/10.1016/j.ecoinf.2024.102583.

Aji R.R., Faniza V., Tarlani Damayanti V., 2021. Landslide Disaster Engineering in Tourism Potential Area. IOP Conf. Ser. Earth Environ. Sci., 830. https://doi.org/10.1088/1755-1315/830/1/012036.

Alom M.Z., Taha T.M., Yakopcic C., Westberg S., Sidike P., Nasrin M.S., Hasan M., Van Essen B.C., Awwal A.A.S., Asari V.K., 2019. A state-of-the-art survey on deep learning theory and architectures. Electron., 8, 1–67. https://doi.org/10.3390/electronics8030292.

Aprina P.U., Santoso D., Alawiyah S., Prasetyo N., Ibrahim K., 2024. Delineating geological structure utilizing integration of remote sensing and gravity data: a study from Halmahera, North Molucca, Indonesia. Vietnam Journal of Earth Sciences, 46(2), 147–168. https://doi.org/10.15625/2615-9783/20010.

Bui T.D., Hoang N.D., Nguyen H., Tran X.L., 2019. Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam. Adv. Eng. Informatics, 42, 100978. https://doi.org/10.1016/j.aei.2019.100978.

Bui T.D., Pradhan B., Lofman O., Revhaug I., 2012a. Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and naive bayes models. Mathematical Problems in Engineering, 974638, 1–26. https://doi.org/10.1155/2012/974638.

Bui T.D., Pradhan B., Lofman O., Revhaug I., Dick O.B., 2012b. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology, 171–172, 12–29. https://doi.org/10.1016/j.geomorph.2012.04.023.

Casagli N., Intrieri E., Tofani V., Gigli G., Raspini F., 2023. Landslide detection, monitoring and prediction with remote-sensing techniques. Nat. Rev. Earth Environ., 4, 51–64. https://doi.org/10.1038/s43017-022-00373-x.

Catani, F., 2021. Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides, 18, 1025–1044. https://doi.org/10.1007/s10346-020-01513-4.

Chen L.C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L., 2018. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell., 40, 834–848. https://doi.org/10.1109/TPAMI.2017.2699184.

Cheng G., Guo L., Zhao T., Han J., Li H., Fang J., 2013. Automatic landslide detection from remote-sensing imagery using a scene classification method based on boVW and pLSA. Int. J. Remote Sens., 34, 45–59. https://doi.org/10.1080/01431161.2012.705443.

Dang K.B., Burkhard B., Müller F., Dang V.B., 2018. Modelling and mapping natural hazard regulating ecosystem services in Sapa, Lao Cai province, Vietnam. Paddy Water Environ., 16, 767–781. https://doi.org/10.1007/s10333-018-0667-6.

Dang K.B., Giang T.L., Dang V.B., Phan T.T., Truong Q.H., Ngo V.L., Do T.H., Dang N.V., Forino G., 2024a. Deep learning models integrating multi-sensor and -temporal remote sensing to monitor landslide traces in Vietnam. Int. J. Disaster Risk Reduct., 105, 104391. https://doi.org/10.1016/j.ijdrr.2024.104391.

Dang K.B., Ha T., Nguyen T., Nguyen H.D., Truong Q.H., Vu T.P., 2022. U-shaped deep-learning models for island ecosystem type classification , a case study in Con Dao Island of Vietnam. One Ecosyst., 7, 23. https://doi.org/10.3897/oneeco.7.e79160.

Dang K.B., Nguyen C.Q., Tran Q.C., Nguyen H., Nguyen T.T., Nguyen D.A., Tran T.H., Bui P.T., Giang T.L., Nguyen D.A., Lenh T.A., Ngo V.L., Yasir M., Nguyen T.T., Ngo H.H., 2024b. Comparison between U-shaped structural deep learning models to detect landslide traces. Sci. Total Environ., 912, 169113. https://doi.org/10.1016/j.scitotenv.2023.169113.

Doan V.L., Nguyen B.Q.V., Nguyen C.C., Nguyen C.T., 2024. Research assessment landslide and sedimentation of Hoa Binh hydropower reservoir. Vietnam J. Earth Sci., 46(2), 203–221. https://doi.org/10.15625/2615-9783/20065.

Duc Dao Minh, Minh V.C., Yen H.H., Loc N.T., Duc Do Minh, 2023. Analysis of landslide kinematics integrating weather and geotechnical monitoring data at Tan Son slow moving landslide in Ha Giang province. Vietnam J. Earth Sci., 45(2), 131–146.

Froude M.J., Petley D.N., 2018. Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci., 18, 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018.

Gallo F., Lavé J., 2014. Evolution of a large landslide in the High Himalaya of central Nepal during the last half-century. Geomorphology, 223, 20–32. https://doi.org/10.1016/j.geomorph.2014.06.021.

Geertsema M., Highland L., Vaugeouis L., 2009. Environmental Impact of Landslides, in: Sassa, K., Canuti, P. (Eds.), Landslides - Disaster Risk Reduction. Springer Berlin Heidelberg, Berlin, Heidelberg, 589–607. https://doi.org/10.1007/978-3-540-69970-5_31.

Ghorbanzadeh O., Shahabi H., Crivellari A., Homayouni S., Blaschke T., Ghamisi P., 2022a. Landslide detection using deep learning and object-based image analysis. Landslides, 19, 929–939. https://doi.org/10.1007/s10346-021-01843-x.

Ghorbanzadeh O., Xu Y., Zhao H., Wang J., Zhong Y., Zhao D., Zang Q., Wang S., Zhang F., Shi Y., Zhu X.X., Bai L., Li W., Peng W., Ghamisi P., 2022b. The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 15, 9927–9942. https://doi.org/10.1109/JSTARS.2022.3220845.

Hang H.T., Hoa P.D., Tru V.N., Phuong N.V., 2021. Landslide Susceptibility Mapping Along National Highway-6, Hoa Binh Province, Vietnam Using Frequency Ratio Model And Gis. Int. J. Geomate. 21, 84–90. https://doi.org/10.21660/2021.85.j2222.

Hoang T.T.H., Nguyen Q.A., 2022. Assessing climate and hydrological conditions for tourism development in the Hoa Binh hydropower reservoir area, Hoa Binh province, in: Proceedings of the 13th National Geography Conference, p.11.

Hung P. Van, Son P.Q., Dung N. Van, 2015. The study evaluated arming of risk of lanslide in Hoa Binh and Son La reservoir hydropower area on the basis of analyzing high-resolution remote sensing and geographic information systems. Vietnam J. Earth Sci., 37(2), 193–203.

Khan R., Yousaf S., Haseeb A., Uddin M.I., 2021. Exploring a Design of Landslide Monitoring System. Complexity, 2021, 5552417, 13. https://doi.org/10.1155/2021/5552417.

Kubalíková L., Kirchner K., Bajer A., 2021. Geomorphological Resources for Geoeducation and Geotourism, in: Advances in Geographical and Environmental Sciences, 343–358. https://doi.org/10.1007/978-981-15-4956-4_18.

Lee Y.J., Jayakumar R., 2021. Economic impact of UNESCO Global Geoparks on local communities: Comparative analysis of three UNESCO Global Geoparks in Asia. Int. J. Geoheritage Park., 9, 189–198. https://doi.org/10.1016/j.ijgeop.2021.02.002.

Hoang, H.T.T., 2014. Multi-scale Analysis of Human-Environment Interactions. A Case-study in the Northern Vietnamese Mountains. Katholieke Universiteit Leuven, 12, 237.

Luu C., Ha H., Bui Q.D., Luong N.D., Khuc D.T., Vu H., Nguyen D.Q., 2023. Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling. Quat. Sci. Adv., 11, 100083. https://doi.org/10.1016/j.qsa.2023.100083.

Mohan A., Singh A.K., Kumar B., Dwivedi R., 2021. Review on remote sensing methods for landslide detection using machine and deep learning. Trans. Emerg. Telecommun. Technol., 32, 1–23. https://doi.org/10.1002/ett.3998.

Naveen D., Nirmala M., Roopesh D., Reddy J.K., Raju P.K., 2022. Landslide Detection Using Machine Learning Algorithms. J. Algebr. Stat., 13, 2822–2828.

Newsome D., Dowling R., 2018. Geoheritage and geotourism, Geoheritage: Assessment, Protection, and Management. Elsevier Inc., 305–321 https://doi.org/10.1016/B978-0-12-809531-7.00017-4.

Ngo V.L., Nguyen H., Dang K.B., Giang T.L., Dang V.B., Do T.H., Nguyen M.H., Dang N.V., Dao M.D., 2025. Advancing debris flow detection based on deep learning model and high-resolution images. Vietnam J. Earth Sci., 47(2), 290–214.

Nguyen B.D., Dang T.H., Vu D.M., Le T.T.H., 2011. Studying to determine causes of landslide in the area of the Mong Sen bridge, Lao Cai province. J. Earth Sci., 33(2), 164–174.

Nguyen H.D., Vu C.T., Bretcan P., Petrisor A.-I., 2024. Assessing the relationship between landslide susceptibility and land cover change using machine learning. Vietnam J. Earth Sci., 46(3), 339–359.

Nhu V.H., Hoang N.D., Nguyen H., Ngo P.T.T., Thanh Bui T., Hoa P.V., Samui P., Tien Bui D., 2020. Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area. Catena, 188, 104458. https://doi.org/10.1016/j.catena.2020.104458.

Pham B.T., Jaafari A., Nguyen D.D., Bayat M., Nguyen H.B.T., 2022. Development of multiclass alternating decision trees based models for landslide susceptibility mapping. Phys. Chem. Earth, 128, 103235. https://doi.org/10.1016/j.pce.2022.103235.

Pham V.T., Le H.L., Tran T.N., Nguyen Q.P., Phan T.T., Dinh T.Q., Dao M.D., Nguyen C.L., Nguyen H.C., 2023. Mechanism and numerical simulation of a rapid deep-seated landslide in Van Hoi reservoir, Vietnam. Vietnam J. Earth Sci., 45(3), 357–373.

Phong T.V., Ly H.-B., Trinh P.T., Prakash I., Hoan D.T., 2020. Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach. Vietnam J. Earth Sci., 42(3), 237–246.

Prakash N., Manconi A., Loew S., 2021. A new strategy to map landslides with a generalized convolutional neural network. Sci. Rep., 11, 1–15. https://doi.org/10.1038/s41598-021-89015-8.

Roa-Lobo J.G., 2007. Identifying Landslide Hazards in a Tropical Mountain Environment, using Geomorphologic and Probabilistic Approaches. University of Maryland, 172.

Schweigl J., Hervas J., 2009. Landslide mapping in Austria, JRC Scientific and Technical Reports EUR 23785 EN. https://doi.org/10.2788/85150.

Sharma N., Saharia M., Ramana G. V., 2024. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data. Catena, 235, 107653. https://doi.org/10.1016/j.catena.2023.107653.

Sun D., Ding Y., Wen H., Zhang F., Zhang Junyi, Gu Q., Zhang Jialan, 2024. SHAP-PDP hybrid interpretation of decision-making mechanism of machine learning-based landslide susceptibility mapping: A case study at Wushan District, China. Egypt. J. Remote Sens. Sp. Sci., 27, 508–523. https://doi.org/10.1016/j.ejrs.2024.06.005.

Tofani V., Segoni S., Agostini A., Catani F., Casagli N., 2013. Technical note: Use of remote sensing for landslide studies in Europe. Nat. Hazards Earth Syst. Sci., 13, 299–309. https://doi.org/10.5194/nhess-13-299-2013.

Tran A.T., Pham V.H., Tran T.T., Nguyen T.A.N., Nguyen V.D., Pham T.H., Tran V.P., 2024. Landslide susceptibility in Phuoc Son, Quang Nam: A deep learning approach. Vietnam J. Earth Sci., 47(1), 39–57.

Tricia R.S., Marian L., Burns W.J., Justin M., 2019. Preparing for Landslide Hazards a Land Use Guide for Oregon Communities, Oregon City GIS, 257.

Valchev N., Eftimova P., Andreeva N., Prodanov B., 2017. Application of Bayesian Network As a Tool for Coastal Flooding Impact Prediction At Varna Bay (Bulgaria, Western Black Sea). Coast. Eng. Proc., 1, 14. https://doi.org/10.9753/icce.v35.management.14.

World Bank Group, 2021. Economics for Disaster Prevention and Preparedness SUMMARY REPORT Investment in Disaster Risk Management in Europe Makes Economic Sense. World Bank, Washington, DC, USA.

World Bank Group, 2020. Seismic Resilience and Energy Efficiency in Public Buildings Project Information Document. Washington, DC.

Yang X., Chen L., 2010. Using multi-temporal remote sensor imagery to detect earthquake-triggered landslides. Int. J. Appl. Earth Obs. Geoinf., 12, 487–495. https://doi.org/10.1016/j.jag.2010.05.006.

Yao X., Yang H., Wu Y., Wu P., Wang B., Zhou X., Wang S., 2019. Land use classification of the deep convolutional neural network method reducing the loss of spatial features. Sensors (Switzerland), 19. https://doi.org/10.3390/s19122792.

Zhao C., Lu Z., 2018. Remote sensing of landslides-A review. Remote Sens., 10, 8–13. https://doi.org/10.3390/rs10020279.