09. Study on the application of machine learning technique and geographical information systems to build landslide susceptibility maps
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Submitted
12 October 2022
Published
30 September 2022
Keywords:
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Trắc địa - Bản đồ Surveying - Mapping
Abstract
Landslides have caused great damage to property, infrastructure and people in many mountainous areas of Vietnam. Studies on landslides have been addressed in the management and prevention of natural disasters. This study presents a machine learning method, which is a radial basis function classifier (RBFC) to create a landslide susceptibility map in Muong Cha district of Dien Bien province in the Northwest mountainous region, which is frequently affected by landslides. In the model, 12 influential factors were selected based on the topography and geographical conditions of the study area. To confirm the performance of the model, statistical indicators including ROC/AUC curves and various statistical indicators were used. The results showed that the RBFC models have high accuracy in building landslide spatial prediction maps, with AUC train = 0931, AUCtest = 0,857. This study is useful for building landslide susceptibility maps with the aim of identifying landslide prone areas for risk management.
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References
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[3]. Flentje, P. N, Miner, A, et al. (2007). Guidelines for landslide susceptibility, hazard and risk zoning for land use planning.
[4]. Darema, F (2004). Dynamic data driven applications systems: A new paradigm for application simulations and measurements. in International conference on computational science. Springer.
[5]. Agatonovic - Kustrin, S, Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical analysis, 22(5).
[6]. Wang, H, Hu, D. (2005). Comparison of SVM and LS - SVM for regression. International conference on neural networks and brain. IEEE.
[7]. Pham, B. T, Tien Bui, D. and Prakash, I. (2017). Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: A comparative study. Geotechnical and Geological Engineering, 35(6).
[8]. Karaboga, D, Kaya, E. (2019). Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review, 52(4).
[9]. Pham, B. T, Phong, T. V et al. (2020). A comparative study of kernel logistic regression, radial basis function classifier, multinomial naïve bayes and logistic model tree for flash flood susceptibility mapping. Water, 12(1).
[10]. Van Westen, C, Van Asch, T. W and Soeters, R. (2006). Landslide hazard and risk zonation - why is it still so difficult? Bulletin of Engineering geology and the Environment, 65(2).
[11]. Bui, K - T. T, Tien Bui, D et al. (2018). A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Computing and Applications, 29(12).
[12]. Luu, C, Nguyen, D. D et al. (2022). Using decision tree J48 based machine learning algorithm for flood susceptibility mapping: A case study in Quang Binh province, Vietnam. In CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. Springer. p. 1927 - 1935.
[13]. Tran, H. T, Campbell, J. B et al. (2017). Monitoring drought vulnerability using multispectral indices observed from sequential remote sensing (Case study: Tuy Phong, Binh Thuan, Vietnam). GIScience & Remote Sensing, 54(2).
[14]. Phong, T. V, Pham, B. T et al. (2021). Groundwater potential mapping using GIS ‐ based hybrid artificial intelligence methods. Groundwater, 59(5).
[15]. Bui, D.T, Lofman, O et al. (2011). Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural hazards, 59(3).
[16]. Chen, W, Fan, L et al. (2019). Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in nanzheng county, china. Applied Sciences, 10(1)
[17]. Chapi, K, Singh, V. P et al. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental modelling & software, 95.
[18]. Tien Bui, D, Shirzadi, A et al. (2019). A novel ensemble artificial intelligence approach for gully erosion mapping in a semi - arid watershed (Iran). Sensors, 19(11).
[19]. Danh, N.T, Ngọ, Đ.V, Dũng, T. Q. (2017). Ứng dụng phương pháp hồi quy logistic xác định tổ hợp tối ưu các yếu tố ảnh hưởng và xây dựng bản đồ tai biến trượt lở đất huyện Khánh Vĩnh, tỉnh Khánh Hòa. Science and Technology Development Journal, 20(K4 - 2017).
[20]. Pham, B.T, Nguyen, M. D et al. (2019). A novel artificial intelligence approach based on multi - layer perceptron neural network and biogeography - based optimization for predicting coefficient of consolidation of soil. Catena, 173.
Authors
Trần Xuân, B., & Lưu Thùy, D. (2022). 09. Study on the application of machine learning technique and geographical information systems to build landslide susceptibility maps. Science Journal of Natural Resources and Environment, (42), 90–101. Retrieved from https://tapchikhtnmt.hunre.edu.vn/index.php/tapchikhtnmt/article/view/433
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