Landslide hazard assessment for the Batxat area of Vietnam using GIS-based spatial prediction models

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Sergo Ordzhonikidzе Russian State Geological Prospecting University, Moscow, Russia
    3 Vietnam Association of Engineering Geology and Environment, Hanoi, Vietnam
    4 Lomonosov Moscow State University, Moscow, Russia
    5 Institute of Geological Sciences - Vietnam Academy of Science and Technology, Hanoi, Vietnam

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 25th-Apr-2024
  • Revised: 17th-Aug-2024
  • Accepted: 9th-Sept-2024
  • Online: 1st-Dec-2024
Pages: 70 - 81
Views: 33
Downloads: 0
Rating: , Total rating: 0
Yours rating

Abstract:

Located in the northwest of Laocai province, Batxat district has been frequently affected by natural disasters, including landslides and debris flows. Therefore, landslide hazard assessment (LHA) has been a significant task for planning, economic development, and minimizing human and property damage. For this purpose, landslide hazard maps were established in this study using the Analytic Hierarchy Process (AHP) and the combined Analytic Hierarchy Process - Frequency Ratio (AHP&FR) models. Ten landslide-related factors were selected, including elevation, slope, distance to road, distance to drainage, land use and land cover (LULC), average monthly rainfall, lithology, aspect, distance to fault, and relative relief. Afterwards, the weighted value of landslide-related factors and the landslide susceptibility index (LSI) were determined using the Analytic Hierarchy Process. The Frequency Ratio method was used to calculate the weighted value of factor classes. Two landslide hazard maps were established, and the study area was divided into five hazard zones: very low, low, moderate, high, and very high. The performance of the models was determined using the area under the curve (AUC) of the receiver operating characteristic (ROC), the seed cell area index (SCAI), and the precision of the predicted results (P). The AUC values for the success rate of these models were 0.72 and 0.75, and for the prediction rate were 0.67 and 0.70, respectively. The evaluation results of the models showed that, although both the AHP and combined AHP&FR models have good performance for landslide hazard mapping, the AHP&FR model produces more accurate outcomes than the AHP model.

How to Cite
Duong, B.Van, Fomenko, I.Konstantinovich, Nhu, H.Viet, Nguyen, P.Huy, Sirotkina, O.Nikolaevna, Nguyen, K.Trung and Pham, H.Ngoc Thi 2024. Landslide hazard assessment for the Batxat area of Vietnam using GIS-based spatial prediction models. Journal of Mining and Earth Sciences. 65, 6 (Dec, 2024), 70-81. DOI:https://doi.org/10.46326/JMES.2024.65(6).07.
References

ADDIN EN.REFLIST Akgun, A., and Türk, N. (2010). Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis. Environmental Earth Sciences, 61(3), 595-611. https://doi.org/10.1007/s12665-009-0373-1

Althuwaynee, O. F., Pradhan, B., and Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers and Geosciences, 44, 120-135. https://doi.org/10.1016/j.cageo.2012.03.003

Bui, T. D., Tran, A. T., Hoang, N.-D., Nguyen, Q. T., Nguyen, D. B., Ngo, V. L., and Pradhan, B. (2017). Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14(2), 447-458. https://doi.org/10.1007/s10346-016-0711-9

Bui, T. D., Tran, A. T., Klempe, H., Pradhan, B., and Revhaug, I. (2016). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13(2), 361-378. https://doi.org/10.1007/s10346-015-0557-6

Cantarino, I., Carrion, M. A., Goerlich, F., and Martinez Ibañez, V. (2019). A ROC analysis-based classification method for landslide susceptibility maps. Landslides, 16(2), 265-282. https://doi.org/10.1007/s10346-018-1063-4

Dang, Q. T., Nguyen, D. H., Prakash, I., Jaafari, A., Nguyen, V. T., Phong, T. V., and Pham, B. T.(2020). GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam. Vietnam Journal of Earth Sciences, 42(1), 55-66. https://doi.org/10.15625/0866-7187/42/1/14758

Dou, J., Yunus, A. P., Tien Bui, D., Sahana, M., Chen, C.-W., Zhu, Z., . . . Thai Pham, B. (2019). Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM. Remote Sensing, 11(6), 638. https://doi.org/10.3390/rs11060638

Gholami, M., Ghachkanlu, E. N., Khosravi, K., and Pirasteh, S. (2019). Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. Journal of Earth System Science, 128(2), 42. https://doi.org/10.1007/s12040-018-1047-8

Greenbaum, D., Bowker, M., Dau, I., Dropsy, H., Greally, K., McDonald, A. J. W., . . . Tragheim, D. (1995a). Rapid methods of landslide hazard mapping : Fiji case study. NERC. Nottingham, UK. http://core.ac.uk/download/pdf/58059.pdf

Greenbaum, D., Tutton, M., Bowker, M., Browne, T., Buleka, J., Greally, K., . . . Tragheim, D. (1995b). Rapid methods of landslide hazard mapping : Papua New Guinea case study. NERC. Nottingham, UK. https://core.ac.uk/download/pdf/57306.pdf

Le, T. T. T., Tran, T. V., Hoang, V. H., Bui, V. T., Bui, T. K. T., and Nguyen, H. P. (2021). Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam. Journal of Disaster Research, 16(4), 529-538. https://doi.org/10.20965/jdr.2021.p0529

Ma, Z., Mei, G., and Piccialli, F. (2021). Machine learning for landslides prevention: a survey. Neural Computing and Applications, 33(17), 10881-10907. https://doi.org/10.1007/s00521-020-05529-8

Mandal, S., and Mondal, S. (2019). Statistical Approaches for Landslide Susceptibility Assessment and Prediction. Springer International Publishing. Switzerland, 200 pages.

Mokarram, M., and Zarei, A. R. (2018). Landslide Susceptibility Mapping Using Fuzzy-AHP. Geotechnical and Geological Engineering, 36(6), 3931-3943. https://doi.org/10.1007/s10706-018-0583-y

Mokhtari, M., and Abedian, S. (2019). Spatial prediction of landslide susceptibility in Taleghan basin, Iran. Stochastic Environmental Research and Risk Assessment, 33(7), 1297-1325. https://doi.org/10.1007/s00477-019-01696-w

Nguyen, T. K., Tran, T. V., Vy, T. H. L., Pham, L. H. L., and Nguyen, Q. T. (2021). Landslide Susceptibility Mapping Based on the Combination of Bivariate Statistics and Modified Analytic Hierarchy Process Methods: A Case Study of Tinh Tuc Town, Nguyen Binh District, Cao Bang Province, Vietnam. Journal of Disaster Research, 16(4), 521-528. https://doi.org/10.20965/jdr.2021.p0521

Nguyen, V. C., and Dao, V. T. (2007). Investigation and research of landslide geohazard in north-western part of Vietnam for the sustainable development of the territory. In. Annual Report of FY 2006, The Core University Program between Japan Society for the Promotion of Science (JSPS) and Vietnamese Academy of Science and Technology (VAST). Osaka University. Osaka. 269-280.

Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., and Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349-369. https://doi.org/10.1007/s12040-013-0282-2

Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. https://doi.org/10.1016/0022-2496(77)90033-5

Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. https://doi.org/10.1016/0377-2217(90)90057-I

Saaty, T. L. (2002). Decision Making with the Analytic Hierarchy Process. Scientia Iranica, 9(3), 215-229. http://scientiairanica.sharif.edu/article_2696.html

Saaty, T. L. (2008). Decision making with the Analytic Hierarchy Process. Int. J. Services Sciences Int. J. Services Sciences, 1, 83-98. https://doi.org/10.1504/IJSSCI.2008.017590

Saaty, T. L., and Vargas, L. (2001). Models, Methods, Concepts and Applications of the Analytic Hierarchy Process. Springer US. New York, 333 pages.

Senouci, R., Taibi, N.-E., Teodoro, A. C., Duarte, L., Mansour, H., and Yahia Meddah, R. (2021). GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria. Sustainability, 13(2). https://doi.org/10.3390/su13020630

Shano, L., Raghuvanshi, T. K., and Meten, M. (2020). Landslide susceptibility evaluation and hazard zonation techniques – a review. Geoenvironmental Disasters, 7(1), 18. https://doi.org/10.1186/s40677-020-00152-0

Shano, L., Raghuvanshi, T. K., and Meten, M. (2021). Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia. Arabian Journal of Geosciences, 14(7), 623. https://doi.org/10.1007/s12517-021-06995-7

Šimundić, A.-M. (2009). Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC, 19(4), 203-211. https://pubmed.ncbi.nlm.nih.gov/27683318

Süzen, M. L., and Doyuran, V. (2004). A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology, 45(5), 665-679. https://doi.org/10.1007/s00254-003-0917-8

Tran, T. V., Alkema, D., and Hack, R. (2019). Weathering and deterioration of geotechnical properties in time of groundmasses in a tropical climate. Engineering Geology, 260, 105221. https://doi.org/10.1016/j.enggeo.2019.105221

Tran, T. V., Alvioli, M., and Hoang, V. H. (2021). Description of a complex, rainfall-induced landslide within a multi-stage three-dimensional model. Natural Hazards. https://doi.org/10.1007/s11069-021-05020-0

Vahidnia, M. H., Alesheikh, A. A., Alimohammadi, A., and Hosseinali, F. (2009). Landslide Hazard Zonation Using Quantitative Methods in GIS. IJCE, 7(3), 176-189. http://ijce.iust.ac.ir/article-1-289-en.html

Varnes, D. J. (1978). Slope Movement Types and Processes. In Robert L Schuster and Raymond J Krizek (Eds.). Landslides: Analysis and Control. Special Report 176. Transportation Research Board, National Academy of Sciences. Washington. 11-33. http://onlinepubs.trb.org/Onlinepubs/sr/sr176/176-002.pdf

Varnes, D. J., International Association of Engineering Geology, and Commission on Landslides and Other Mass Movements on Slopes, (1984). Landslide hazard zonation a review of principles and practice. Unesco. Paris, 63 pages.

Yalcin, A., Reis, S., Aydinoglu, A. C., and Yomralioglu, T. (2011). A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA, 85(3), 274-287. https://doi.org/10.1016/j.catena.2011.01.014

Zhang, G., Cai, Y., Zheng, Z., Zhen, J., Liu, Y., and Huang, K. (2016). Integration of the Statistical Index Method and the Analytic Hierarchy Process technique for the assessment of landslide susceptibility in Huizhou, China. CATENA, 142, 233-244. https://doi.org/10.1016/j.catena.2016.03.028.

Other articles