Landslide susceptibility assessment for warning of dangerous areas in Tan Uyen district, Lai Chau province, Vietnam

References

• Abay A, Barbieri G. 2012. Landslide susceptibility and causative factors evaluation of the landslide area of Debresina, in the southwestern Afar escarpment, Ethiopia. J Earth Sci. 2:133–144.
   Google Scholar
• Ada M, San BT. 2018. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey. Nat Hazards. 90:237–263.
   Web of Science ®Google Scholar
• Aditian A, Kubota T, Shinohara Y. 2018. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology. 318:101–111. doi:10.1016/j.geomorph.2018.06.00.
   Web of Science ®Google Scholar
• Albano R, Sole A. 2018. Geospatial methods and tools for natural risk management and communications. ISPRS Int J Geoinf. 7(12):470. doi:10.3390/ijgi7120470.
   Web of Science ®Google Scholar
• Aleotti P, Chowdhury R. 1999. Landslide hazard assessment: summary, review and new perspectives. Bull Eng Geol Environ. 58:21–44.
   Google Scholar
• Ali S, Parvin A, Pham QB, Vojtek MJ, Costache R. 2020. Comparative GIS-based assessment of multi-user flood susceptibility criteria combining decision-making approaches, naive Bayesian trees, two-variable statistics and logistic regression: a case of the Topľa basin, Slovakia. Ecol India. 117. doi:10.1016/j.ecolind.2020.106620.
   Web of Science ®Google Scholar
• Al-Shabeeb AR. 2016. The use of AHP within GIS in selecting potential sites for water harvesting sites in the Azraq basin – Jordan. J Geogr Inform Syst. 8(1):73–88. doi:10.4236/jgis.2016.81008.
   Google Scholar
• Althuwaynee OF, Pradhan B, Park H-J, Lee JH. 2014. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. 114:21–36.
   Web of Science ®Google Scholar
• Asadzadeh S, Souza CR. 2016. A review on spectral processing methods for remote sensing. Int J Appl Earth Obs Geoinf. 47:69–90.
   Web of Science ®Google Scholar
• Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, Ahmad BB. 2019. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Sci Total Environ. 655:684–696.
   PubMed Web of Science ®Google Scholar
• Bai SB, Wang J, Lu GN, Zhou PG, Hou SS. 2010. GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three gorges area, China. Geomorphology. 115:23–31.
   Web of Science ®Google Scholar
• Blahut J, VanWesten C, Sterlacchini S. 2010. Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geophys J Roy Astron Soc. 119:36–51.
   Google Scholar
• Boroushaki S, Malczewski J. 2008. Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Comput Geosci. 34:399–410.
   Web of Science ®Google Scholar
• Bui DT, Pradhan B, Lofman O, Dick I, Revhaug OB. 2012. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena. 96:28–40.
   Web of Science ®Google Scholar
• Carver SJ. 1991. Integrating multi-criteria evaluation with geographical information systems. Int J Geogr Inf Syst. 5:321–339.
   Google Scholar
• Chen J. 2014. GIS-based multi-criteria analysis for land use suitability assessment in city of Regina. Environ Syst Res. 3(1):13. doi:10.1186/2193-2697-3-13.
   Google Scholar
• Chen X, Chen W. 2021. GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA. 196:104833. doi:10.1016/j.catena.2020.104833.
   Web of Science ®Google Scholar
• Choi J, Oh HJ, Lee HJ, Lee C, Lee S. 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol. 124:12–23.
   Web of Science ®Google Scholar
• Comber A, Zeng W. 2019. Spatial interpolation using areal features: a review of methods and opportunities using new forms of data with coded illustrations. Geogr Compass. 1–23. doi:10.1111/gec3.12465.
   Web of Science ®Google Scholar
• Cruden DM, Varnes DJ. 1996. Landslide types and processes, transportation research board, U.S. National Academy of Sciences. Special Rep. 247:36–75.
   Google Scholar
• Demir G, Aytekin M, Akgün A, Ikizler SB, Tatar O. 2013. A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards. 65:1481–1506.
   Web of Science ®Google Scholar
• Der Sarkissian R, Zaninetti RJM, Abdallah C. 2019. The use of geospatial information as support for Disaster Risk Reduction; contextualization to Baalbek-Hermel Governorate/Lebanon. Appl Geogr. 111:102075.
   Web of Science ®Google Scholar
• Ding Q, Chen W, Hong H. 2017. Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int. 32:619–639.
   Web of Science ®Google Scholar
• Do MN, Dang TT, Do MD. 2016. Application of GIS and analytic hierarchy process (AHP) method to establish map of landslide susceptibility in Xin Man district, Ha Giang province, Vietnam. Sci J Hanoi Nat Univ. 32(2S):206–216.
   Google Scholar
• Eastman JR. 2009. IDRISI taiga: guide to GIS and image processing volume – manual version 16.02: 325. Worcester (MA): Clark University. Clark Labs.
   Google Scholar
• Eastman JR, Jiang H. 1996. Fuzzy measures in multi-criteria evaluation. Proceedings, Second International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Studies; Fort Collins, GIS World Inc. 3(1): 527–534.
   Google Scholar
• Fayez L, Pham BT, Solanki HA, Pazhman D, Dholakia MB, Khalid M, Prakash I. 2018. Application of frequency ratio model for the development of landslide susceptibility mapping at part of Uttarakhand state, India. Int J Appl Eng Res. 13:6846–6854.
   Google Scholar
• Feizizadeh B, Shadman RM, Jankowski P, Blaschke T. 2014. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Comput Geosci. 73:208–221. doi:10.1016/j.cageo.2014.08.001.
   PubMed Web of Science ®Google Scholar
• Gholami M, Ghachkanlu EN, Khosravi K, Pirasteh S. 2019. Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index methods. J Earth Syst Sci. 128:42.
   Web of Science ®Google Scholar
• Goodchild MF, Haining RP. 2003. GIS and spatial data analysis: converging perspectives. Pap Reg Sci. 83(1):363–385.
   Web of Science ®Google Scholar
• Gorsevski PV., Brown MK, Panter K, Onasch CM, Simic A, Snyder J. 2016. Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio. Landslides. 13:467–484.
   Web of Science ®Google Scholar
• Hong H, Xu C, Bui DT. 2015. Landslide susceptibility assessment at the Xiushui area (China) using frequency ratio model. Proc Earth Planet Sci. 15:513–517.
   Google Scholar
• Jia P, Cheng X, Xue H, Wang Y. 2017. Applications of geographic information systems (GIS) data and methods in obesity-related research. Obes Rev. 18(4):400–411.
   Web of Science ®Google Scholar
• Kanungo D, Arora M, Sarkar S, Gupta R. 2009. Landslide susceptibility zonation (LSZ) mapping – a review. J South Asia Disaster Stud. 2:81–105.
   Google Scholar
• Kawasaki A, Berman ML, Guan W. 2012. The growing role of web-based geospatial technology in disaster response and support. Disasters. 37(2):201–221.
   Web of Science ®Google Scholar
• Khan H, Shafique M, Bacha M, Shah S, Calligaris C. 2020. Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egyptian J Remote Sensing Space Sci. 22(1):11–24. doi:10.1016/j.ejrs.2018.03.004.
   Web of Science ®Google Scholar
• Kieu QL. 2021. Flash flood hazard mapping using satellite images and GIS integration method: a case study of Lai Chau province, Vietnam. Geogr Tech. 16(2):105–115.
   Web of Science ®Google Scholar
• Kieu QL, Do TVH, Van HT. 2020. Constructing a spatial analysis model in assessing the sensitivity of landscape erosion in mountainous regions of Vietnam. SN Appl Sci. 2:43–56.
   Web of Science ®Google Scholar
• Kumar R, Chatterjee C, Singh RD, Lohani AK, Kumar S. 2007. Runoff estimation for an ungauged catchment using geomorphological instantaneous unit hydrograph (GIUH) models. Hydrol Process. 21(14):1829–1840.
   Web of Science ®Google Scholar
• Kumar S, Srivastava PK, Snehmani. 2017. GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya. Geocarto Int. 32(11):1254–1267. doi:10.1080/10106049.2016.1206626.
   Web of Science ®Google Scholar
• Lai TA, Nguyen NT, Pham XC, Le NN. 2018. Building an early warning system for flash floods and landslide in mountainous areas, testing it in Thuan Chau district, Son La province. VNJ Sci Technol. 60(8):28–35.
   Google Scholar
• Lam MH. 2011. Research and develop landslide risk zoning maps to serve the prevention of landslide for Yen Bai province. J Meteorol Hydrol. 211(2):11–15.
   Google Scholar
• Le QH. 2018. Investigation, assessment and zoning of landslide risk in Lai Chau province. J Earth Sci. 6(3):23–56.
   Google Scholar
• Lei X, Chen W, Pham BT. 2020. Performance evaluation of GIS-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int J Geo-Inf. 9:443.
   Web of Science ®Google Scholar
• Majumder M. 2015. Multi criteria decision making. Springer Briefs in Water Science and Technology; p. 35–47. doi:10.1007/978-981-4560-73-32.
   Google Scholar
• Malczewski J, Rinner C. 2015. Multicriteria decision analysis in geographic information science. New York: Springer. doi:10.1007/978-3-540-74757-4.
   Google Scholar
• Manfré LA, Hirata E, Silva JB, Shinohara EJ, Giannotti MA, Quintanilha JA. 2012. An analysis of geospatial technologies for risk and natural Disaster management. ISPRS Int J Geoinf. 1(2):166–185.
   Web of Science ®Google Scholar
• Miyazaki H, Nagai M, Shibasaki R. 2015. Reviews of geospatial information technology and collaborative data delivery for disaster risk management. ISPRS Int J Geoinf. 4(4):1936–1964.
   Web of Science ®Google Scholar
• Mohammady M, Pourghasemi HR, Pradhan B. 2012. Landslide susceptibility mapping at Golestan province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci. 61:221–236.
   Web of Science ®Google Scholar
• Mondal S, Maiti R. 2013. Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shivkhola watershed, Darjeeling Himalaya. Int J Disaster Risk Sci. 4:200–212.
   Web of Science ®Google Scholar
• Nefeslioglu HA, Sezer EA, Gokceoglu C, Ayas Z. 2013. A modified analytical hierarchy process (M-AHP) approach for decision support systems in natural hazard assessments. Comput Geosci. 59:1–8.
   Web of Science ®Google Scholar
• Nguyen DT, Kieu QL. 2022. Application of 3S technology in disaster risk research in the northern mountainous region of Vietnam. Geogr Tech. 17(1):117–129.
   Web of Science ®Google Scholar
• Nguyen NT. 2003. Research and forecast of natural disasters in Hoa Binh province. Vietnam: VNU. Special scientific project report, code QG 0017.
   Google Scholar
• Nguyen T, Nguyen DD, Uong DK. 2012. Building landslide risk map of Quang Tri province by integrating analytical hierarchical model (AHP) into GIS. Hue Univ Sci J. 74:25–32.
   Google Scholar
• Nguyen TD, Tran AT, Saro L. 2008. Application of GIS technology to establish landslide susceptibility maps of northwestern border provinces of Vietnam. J Earth Sci. 30(2):12–20.
   Google Scholar
• Nguyen TL, Nguyen T, Vu DD, Nguyen TH. 2017. Research on establishing inundation maps from radar remote sensing images applied to downstream Tra Khuc and Ve river basins. Quang Ngai province. J Irrig Sci Technol. 39:21–28.
   Google Scholar
• Nguyen VH. 2015. Study on landslide warning in Hoa Binh and Son La hydroelectric reservoirs by analysis of geographic information systems. J Earth Sci. 37(3):193–203.
   Google Scholar
• Pamela M, Sadisun IA, Arifianti Y. 2018. Weights of evidence method for landslide susceptibility mapping in Takengon, Central Aceh, Indonesia. IOP Conf Ser Earth Environ Sci. 118:012037. doi:10.1088/1755-1315/118/1/012037.
   Google Scholar
• Park SJ, Lee CW, Lee S, Lee MJ. 2018. Landslide susceptibility mapping and comparison using decision tree models: a case study of Jumunjin area, Korea. Remote Sens. 10:1545–1560.
   Web of Science ®Google Scholar
• Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R. 2014. Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the three gorges area, China. Geomorphology. 204:287–301.
   Web of Science ®Google Scholar
• People’s Committee of Tan Uyen district. 2021. An overview report on natural and socio-economic conditions of Tan Uyen district, Lai Chau province of Vietnam.
   Google Scholar
• Pham BT, Bui TD, Prakash I, Dholakia M. 2015. Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach frequency ratio method. Int J Eng Res Technol. 4:338–344.
   Google Scholar
• Pham HG. 2021. Research on natural characteristics of Tan Uyen district. J Sci Technol Thai Nguyen Univ. 156(3):88–102.
   Google Scholar
• Pirasteh S, Li J. 2017. Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs. Pirasteh Li Geoenviron Dis. 4:19–35. doi:10.1186/s40677-017-0083-z.
   Google Scholar
• Pourghasemi HR, Kerle N. 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci. 75:174–185.
   Web of Science ®Google Scholar
• Pu R, Gong P, Tian Y, Miao X, Carruthers RI, Anderson GL. 2008. Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA. Int J Remote Sens. 29(14):3987–4011.
   Web of Science ®Google Scholar
• Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F. 2016. Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards. 82(2):1231–1258.
   Web of Science ®Google Scholar
• Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A. 2013. Application of frequency ratio, statistical index, and weights of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci. 7(2):725–742. doi:10.1007/s12517-012-0807-z.
   Web of Science ®Google Scholar
• Regmi NR, Giardino JR, Vitek JD. 2010. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology. 115(1-2):172–187. doi:10.1016/j.geomorph.2009.10.002.
   Web of Science ®Google Scholar
• Reznik T, Lukas, Charvat K, Charvát K, Kepka M. 2017. Disaster risk reduction in agriculture through geospatial (Big) data processing. ISPRS Int J Geoinf. 6(8):238–251.
   Web of Science ®Google Scholar
• Saaty TL. 1987. The analytic hierarchy process – what it is and how it is used. Mathl Modelling. 3:161–176.
   Google Scholar
• Selçuk L. 2013. An avalanche hazard model for Bitlis Province, Turkey, using GIS based multi-criteria decision analysis. Turkish J Earth Sci. 22:523–535.
   Web of Science ®Google Scholar
• Shahabi H, Hashim M. 2015. Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Nat Sci Rep. 5:1–15. doi:10.1038/srep09899.
   Web of Science ®Google Scholar
• Shirowzhan S, Sepasgozar S. 2019. Spatial analysis using temporal point clouds in advanced GIS: methods for ground elevation extraction in slant areas and building classifications. ISPRS Int J Geoinf. 8(3):110–120.
   Web of Science ®Google Scholar
• Snehmani A, Bhardwaj A, Pandit A, Ganju A. 2014. Demarcation of potential avalanche sites using remote sensing and ground observations: a case study of Gangotri Glacier. Geocarto Int. 29:520–535. doi:10.1080/10.106049.2013.807304.
   Web of Science ®Google Scholar
• Statistical Office of Lai Chau Province. 2022. Statistics of natural disasters for the period 2001–2021. Statistical Publishing House; p. 248–269.
   Google Scholar
• Thomas L, Saaty TL. 2008. Decision making with the analytic hierarchy process. Int J Serv Sci. 1(1):167–175.
   Google Scholar
• Tran AT, Nguyen TD. 2012. Sensitization study and landslide risk zoning in Son La hydropower reservoir area by hierarchical analysis method. J Earth Sci. 34:15–27.
   Google Scholar
• Tran VK. 2020. Studying the current situation and establishing landslides zoning maps for some mountainous areas of Vietnam. J Sci Technol Thai Nguyen Univ. 225(11):38–47.
   Google Scholar
• Triantaphyllou E. 2000. Multi-criteria decision making methods: a comparative study. Dordrecht: Kluwer Academic Publishers.
   Google Scholar
• Vakhshoori V, Zare M. 2016. Landslide susceptibility mapping by comparing the weight of evidence, fuzzy logic, and frequency ratio methods. Geomat Nat Hazards Risk. 7(5):1731–1752.
   Web of Science ®Google Scholar
• Vietnam’s Disaster Prevention and Control Office. 2021. Report on flash floods and landslides in mountainous areas in the period 2001–2021.
   Google Scholar
• Vietnam’s Ministry of Natural Resources and Environment. 2020. Project ‘Investigation, assessment and zoning for landslide hazard warning in mountainous areas of Vietnam’, Hanoi, Vietnam.
   Google Scholar
• Wang LJ, Guo M, Sawada K, Lin J, Zhang J. 2016. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J. 20:117–136.
   Web of Science ®Google Scholar
• Wang Q, Li W. 2017. A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys Geogr. 38(4):318–337.
   Web of Science ®Google Scholar
• Wessel B, Huber M, Wohlfart C, Marschalk U, Kosmann D. 2018. Accuracy assessment of the global Tan DEM-X digital elevation model with GPS data. ISPRS J Photogramm Remote Sens. 139:171–182.
   Web of Science ®Google Scholar
• Yadav SK. 2013. GIS based approach for site selection in waste management. J Environ Eng Manag. 4(5):507–514.
   Google Scholar
• Ye C, Li Y, Cui P, Liang L, Pirasteh S, Marcato J. 2019. Landslide detection of hyperspectral remote sensing data based on deep learning with constrains. IEEE J Select Top Appl Earth Observ Remote Sensing. 12(12):5047–5060. doi:10.1109/jstars.2019.2951725.
   Web of Science ®Google Scholar
• Zhang J, Su Y, Wu J, Liang H. 2015. GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China. Comput Electron Agri. 114:202–211.
   Web of Science ®Google Scholar