10. APPLICATION OF THE GIS AND R PROGRAM FOR LANDSLIDE SUSCEPTIBILITY MAPPING: A CASE STUDY IN VAN YEN, YEN BAI, VIETNAM
Thanh bên bài viết
Đã nộp
26 Tháng mười hai 2022
Đã xuất bản
30 Tháng mười một 2022
Giới thiệu
This study presents the r.landslide tool, an open source add-on to the open source Geographic Information System (GIS) GRASS software for landslide susceptibility mapping. The tool was written in Python language and works on the top of an Artificial Neural Network (ANN) fed with environmental parameters and landslide databases, such as: DTM, NDVI, Aspect, Geology, Faults, Plan Curvature, Profile Curvature, Rivers, Roads, Slope, No Landslide Zones (NLZ). In order to illustrate the application and effectiveness of the developed tool, a case study is presented for the Van Yen district, Yen Bai province, Vietnam. The resulted map with four landslide susceptibility classes: Low, moderate, high and very high susceptibility for landslide, which are derived based on the correspondence with landslide inventory. The map indicates that about 42 % of the area is very high and highly susceptible for landslide. The landslide susceptibility map can be useful for the decision - makers and planners in choosing suitable locations for the long - term development.
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Trích dẫn
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[17]. Thinnukool O., Kongchouy, N., Choonpradub C. (2014). Detection of land use change using R program (A case study of Phuket island, Thailand). Research Journal of Applied Sciences, 9:228-237.
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[19]. Cascini, L. (2008). Applicability of landslide susceptibility and hazard zoning at different scales. Eng. Geol. 102(3-4), 164 - 177.
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[21]. Nguyen, T. V. (2009). Building methodology for estimating geo-hazard risk in the northwest mountainous cities of Vietnam using RS&GIS: Case study in Yen Bai city. Ministry of Sciences and Technology.
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[2]. T. Trinh, D. M. Wu & J. Z. Huang (2016). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study in Yen Bai province, Vietnam. Conference: ICETI, Volume: 1, ISBN 978-1-138-02996-5.
[3]. Bui, T. D., B. Pradhan, O. Lofman, I. Revhaug & O. B. Dick (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput. Geosci. 45, 199 - 211.
[4]. Duc, D. M. (2013). Rainfall - triggered large landslides on December 15th 2005 in Van Canh district, Binh Dinh province, Vietnam. Landslides 10(2), 219 - 230.
[5]. Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., Guzzetti, F. (2018). A review of statistically based landslide susceptibility models. Earth - Science Reviews, 180, 60 - 91.
[6]. Rossi, M., Reichenbach, P. (2016). LAND-SE: A software for statistically based landslide. Geoscientific Model Development, p. 9533 - 9543.
[7]. Fausto, G., Alberto C., Mauro C., Paola R. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi - scale study, Central Italy. Geomorphology, p. 181 - 216.
[8]. Anis, Z., Wissem, G., Vali, V., Smida, H., Essghaie, G. M. (2019). GIS - based landslide susceptibility mapping using bivariate statistical methods in North - western Tunisia. Open Geosciences, 11:708-726.
[9]. Pamela, Sadisun, I. A., Arifianti, Y. (2018). Weights of evidence method for Landslide susceptibility mapping in Takengon, Central Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 118, 012037.
[10]. Silalahi, F.E.S., Pamela, Arifianti, Y., Hidayat, F. (2019). Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters, 6(1).
[11]. Singh Pradhan, A. M., Dawadi, A., Kim, Y. T. (2012). Use of different bivariate statistical landslide susceptibility methods: A case study of Khulekhani watershed, Nepal. Journal of Nepal Geological Society, 44, 1 - 12.
[12]. Sumaryono, Muslim D., Sulaksana N., Triana Y. D. (2015). Weights of evidence method for Landslide susceptibility mapping in Tandikek and Damar Bancah, West Sumatra, Indonesia. International Journal of Science and Research (IJSR), Vol. 4, Issue 10, October 2015, 1283 - 1290.
[13]. Christos P., Christos C. (2018). Comparison and evaluation of landslide susceptibility maps obtained from the weight of evidence, logistic regression, and artificial neural network models. Natural Hazards, vol. 93, no. August 2018, p. 249 - 274.
[14]. Roger, S. B., Edzer, P. and Virgilio G. R. (2013). Applied spatial data analysis with R. New York: Springer.
[15]. Hadley, W. (2014). Tidy data. Journal of Statistical Software, vol. 59, no. 10, p. 1 - 23.
[16]. Althuwaynee, O. F., Musakwa, W., Gumbo, T., Reis, S. (2017). Applicability of R statistics in analyzing landslides spatial patterns in Northern Turkey. 2nd International Conference on Knowledge Engineering and Applications (ICKEA).
[17]. Thinnukool O., Kongchouy, N., Choonpradub C. (2014). Detection of land use change using R program (A case study of Phuket island, Thailand). Research Journal of Applied Sciences, 9:228-237.
[18]. Tonini, Marj Abellan, Antonio (2013). Rockfall detection from terrestrial LiDAR point clouds: A clustering approach using R. Journal of Spatial Information Science. 8. 10.5311/JOSIS.2014.8.123.
[19]. Cascini, L. (2008). Applicability of landslide susceptibility and hazard zoning at different scales. Eng. Geol. 102(3-4), 164 - 177.
[20]. Soeters, R. & C. J. V. Van Westen (1996). Slope instability recognition analysis and zonation.
[21]. Nguyen, T. V. (2009). Building methodology for estimating geo-hazard risk in the northwest mountainous cities of Vietnam using RS&GIS: Case study in Yen Bai city. Ministry of Sciences and Technology.
[22]. Nguyen, X. K. (2012). Assessment on present situation of geo-hazards in provinces of Ha Giang, Cao Bang, Tuyen Quang and Bac Kan - Causes, forecast zoning and recommendation for risk prevention and reduction.
[23]. Tien Bui, D.; Tuan, T. A.; Klempe, H.; Pradhan, B.; 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 2016, 13, 361 - 378.
[24]. Ciaburro, G.; Venkateswaran, B. (2017). Neural Network with R: Smart models using CNN, RNN, Deep Learning and Artificial Intelligence Principles. Packt Publishing Ltd: Birmingham, UK, 2017; Volume 91.
[25]. Chen, H.; Zeng, Z.; Tang, H. (2015). Landslide deformation prediction based on Recurrent Neural Network. Neural Process. Lett. 2015, 41,169 - 178.
Các tác giả
Pham Thi Thanh, T., Le Thi Thu, H., Vu Ngoc, P., & Vu Ngoc, P. (2022). 10. APPLICATION OF THE GIS AND R PROGRAM FOR LANDSLIDE SUSCEPTIBILITY MAPPING: A CASE STUDY IN VAN YEN, YEN BAI, VIETNAM. Tạp Chí Khoa học Tài Nguyên Và Môi trường, (43), 104–113. Truy vấn từ https://tapchikhtnmt.hunre.edu.vn/index.php/tapchikhtnmt/article/view/450
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