Analyzing landslide susceptibility, health vulnerability and risk using multi criteria decision-making analysis in Arunachal Pradesh, India

Rehman, Sufa; Azhoni, Adani

doi:10.1007/s11600-022-00943-z

Artykuł - szczegóły

Tytuł artykułu

Analyzing landslide susceptibility, health vulnerability and risk using multi criteria decision-making analysis in Arunachal Pradesh, India

Autorzy

Rehman Sufa , Azhoni Adani

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00943-z

Warianty tytułu

Języki publikacji

Abstrakty

Landslides being a widespread disaster are associated with susceptibility, vulnerability and risk. The physical factors inducing landslides are relatively well-known. However, how landslide susceptibility will be exacerbated by climate change, impede the attainment of the sustainable development goals and increase health vulnerability is relatively less explored. We present an integrated assessment of landslide susceptibility, health vulnerability and overall risk to understand these interconnected dimensions using Arunachal Pradesh, India, as a case study, which is susceptible to landslides due to its topography and climate conditions. Landslide susceptibility was examined using twenty landslide conditioning parameters through the fuzzy analytical hierarchy process (FAHP). The susceptibility map was validated using the area under the ROC curve (AUC). National Family Health Survey (NFHS 4) data were used to analyze the health vulnerability, while the overall risk was computed through the integration of susceptibility and vulnerability. Landslide susceptibility analysis indicated that nearly 22% area of the state is characterized by moderate susceptibility followed by high (17%) and very high susceptibility (13%). High elevation, slope, rainfall, SPI, drainage density and complex geology were identified as the causative factors of landslides. In the case of health vulnerability, East Kameng and Lohit districts were found to be very highly vulnerable, while Papum Pare, Changlang and Tirap districts experience high health vulnerability due to high degree of exposure and sensitivity. Overall risk analysis revealed over 16.8% area of the state is under moderate risk followed by high (9.8%) and very high (4.2%) risk. Linking this analysis with the climate change projections and SDG goals attainment revealed that Papum Pare, Upper Subansiri, Tirap and West Kameng require priority for lessening susceptibility, vulnerability and risk for achieving sustainable development. A strong correlation (99%) between HVI and risk further demonstrates the need for lessening health vulnerability and risk in the study area. Furthermore, our study contributes additional insights into landslide susceptibility by considering heal vulnerability and risk which may help in planning sustainable development strategies in a changing climate.

Słowa kluczowe

landslide susceptibility FAHP risk sustainable development goals health vulnerability climate projections

podatność osuwiskowa FAHP ryzyko cel zrównoważonego rozwoju

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2023

Tom

Vol. 71, no. 1

Strony

101--128

Opis fizyczny

Bibliogr. 110 poz.

Twórcy

autor

Rehman Sufa

srfpinky@gmail.com

Department of Civil Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore. D.K, India

autor

Azhoni Adani

azhoni@nitk.edu.in

Department of Civil Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore. D.K, India

https://orcid.org/0000-0001-9198-3273

Bibliografia

1. Abdı A, Bouamrane A, Karech T, Dahri N, Kaouachi A (2021) Landslide susceptibility mapping using GIS-based fuzzy logic and the analytical hierarchical processes approach: a case study in constantine (North-East Algeria). Geotech Geol Eng. https://doi.org/10.1007/s10706-021-01855-3
2. Abella EAC, Van Westen CJ (2008) Qualitative landslide susceptibility assessment by multicriteria analysis: a case study from San Antonio del Sur, Guantánamo. Cuba Geomorphology 94(3–4):453–466. https://doi.org/10.1016/j.geomorph.2006.10.038
3. Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arabian J Geosci 10(8):194. https://doi.org/10.1007/s12517-017-2980-6
4. Asadi M, Goli Mokhtari L, Shirzadi A, Shahabi H, Bahrami S (2022) A comparison study on the quantitative statistical methods for spatial prediction of shallow landslides (case study: Yozidar-Degaga Route in Kurdistan Province, Iran). Environmental Earth Sciences 81(2):1–21. https://doi.org/10.1007/s12665-021-10152-4
5. Ashley ST, Ashley WS (2008) Flood fatalities in the United States. J Appl Meteorol Climatol 47(3):805–818. https://doi.org/10.1175/2007JAMC1611.1
6. SDMA (2018) History of vulnerability of the state to the disasters of different types. State Disaster Management Authority (SDMA) Arunachal Pradesh. https://sdma-arunachal.in/history-of-vulnerability-of-the-state-to-the-disasters-of-different-types/. Accessed on 18 August, 2022.
7. Ballerine C (2017) Topographic wetness index urban flooding awareness act action support, will & DuPage Counties, Illinois. Illinois State Water Survey. https://www.isws.illinois.edu/pubdoc/CR/ISWSCR2017-02.pdf. Accessed on 13th September, 2021
8. Basu T, Pal S (2019) RS-GIS based morphometrical and geological multi-criteria approach to the landslide susceptibility mapping in Gish River Basin, West Bengal. India Adv Space Res 63(3):1253–1269. https://doi.org/10.1016/j.asr.2018.10.033
9. Basu T, Pal S (2020) A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India. Environ Dev Sustain 22(5):4787–4819. https://doi.org/10.1007/s10668-019-00406-4
10. Batar AK, Watanabe T (2021) Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the Indian Himalayan Region: recent developments, gaps, and future directions. ISPRS Int J Geo-Inf 10(3):114. https://doi.org/10.3390/ijgi10030114
11. Bernardie S, Vandromme R, Thiery Y, Houet T, Grémont M, Masson F, Grandjean G, Bouroullec I (2020) Modelling landslide hazard under global change: the case of a Pyrenean valley.https://doi.org/10.5194/nhess-2019-311
12. Biswas A, Rahman A, Mashreky S, Rahman F, Dalal K (2010) Unintentional injuries and parental violence against children during flood: a study in rural Bangladesh. Rural Remote Health 10:1199. https://doi.org/10.22605/RRH1199
13. Bora JK (2020) Factors explaining regional variation in under-five mortality in India: an evidence from NFHS-4. Health & Place 64:102363. https://doi.org/10.1016/j.healthplace.2020.102363
14. Brooks N (2003) Vulnerability, risk and adaptation: A conceptual framework. Tyndall Centre for climate change research working paper 38(38): 1–16.
15. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247. https://doi.org/10.1016/0165-0114(85)90090-9
16. Cao Y, Wei X, Fan W, Nan Y, Xiong W, Zhang S (2021) Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area. China Plos One 16(1):e0245668. https://doi.org/10.1371/journal.pone.0245668
17. Cardona OD (2011) Disaster risk and vulnerability: concepts and measurement of human and environmental insecurity. In: Coping with global environmental change, disasters and security, pp 107–121, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17776-7_3.
18. Çellek S (2020) Effect of the slope angle and its classification on landslide. Nat Hazards Earth Syst Sci Discuss. https://doi.org/10.5194/nhess-2020-87
19. Chan EYY, Huang Z, Lam HCY, Wong CKP, Zou Q (2019) Health vulnerability index for disaster risk reduction: application in belt and road initiative (BRI) region. Int J Environ Res Public Health 16(3):380. https://doi.org/10.3390/ijerph16030380
20. Chaudhry AK, Kumar K, Alam MA (2019) Mapping of groundwater potential zones using the fuzzy analytic hierarchy process and geospatial technique. Geocarto Int. https://doi.org/10.1080/10106049.2019.1695959
21. Das B, Bordoloi R, Thungon LT, Paul A, Pandey PK, Mishra M, Tripathi OP (2020) An integrated approach of GIS, RUSLE and AHP to model soil erosion in West Kameng watershed, Arunachal Pradesh. J Earth Syst Sci 129(1):1–8. https://doi.org/10.1007/s12040-020-1356-6
22. Das R, Phukon P, Singh TN (2021) Understanding the cause and effect relationship of debris slides in Papum Pare district, Arunachal Himalaya. India Nat Hazard. https://doi.org/10.1007/s11069-021-05010-2
23. Dikshit A, Sarkar R, Pradhan B, Segoni S, Alamri AM (2020) Rainfall induced landslide studies in Indian Himalayan region: A critical review. Appl Sci 10(7):2466. https://doi.org/10.3390/app10072466
24. Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul. Turkey Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1
25. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343. https://doi.org/10.1016/j.geomorph.2004.09.025
26. Fan W, Wei XS, Cao YB, Zheng B (2017) Landslide susceptibility assessment using the certainty factor and analytic hierarchy process. J Mountain Sci 14(5):906–925. https://doi.org/10.1007/s11629-016-4068-2
27. FitzGerald G, Du W, Jamal A, Clark M, Hou XY (2010) Flood fatalities in contemporary Australia (1997–2008). Emerg Med Australas 22(2):180–186. https://doi.org/10.1111/j.1742-6723.2010.01284.x
28. Füssel HM (2007) Vulnerability: A generally applicable conceptual framework for climate change research. Global Environ Change 17(2):155–167. https://doi.org/10.1016/j.gloenvcha.2006.05.002
29. Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 162:227–252. https://doi.org/10.1016/j.earscirev.2016.08.011
30. Government of Arunachal Pradesh (2019) Arunachal Pradesh at a Glance. https://www.arunachalpradesh.gov.in/at-a-glance-2/. Accessed on 7th August, 2021.
31. Guo Z, Shi Y, Huang F, Fan X, Huang J (2021) Landslide susceptibility zonation method based on C5. 0 decision tree and K-means cluster algorithms to improve the efficiency of risk management. Geosci Front 101249. https://doi.org/10.1016/j.gsf.2021.101249
32. Gururajan NS, Choudhuri BK (2003) Geology and tectonic history of the Lohit valley, Eastern Arunachal Pradesh. India J Asian Earth Sci 21(7):731–741. https://doi.org/10.1016/S1367-9120(02)00040-8
33. Hasegawa S, Nonomura A, Nakai S, Dahal RK (2014) Drainage density as rainfall induced landslides susceptibility index in small catchment area. Int J Lsld Env 1(1):27–28
34. Hemasinghe H, Rangali RS, Deshapriya NL, Samarakoon L (2018) Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka). Procedia Eng 212:1046–1053. https://doi.org/10.1016/j.proeng.2018.01.135
35. Hepdeniz K (2020) Using the analytic hierarchy process and frequency ratio methods for landslide susceptibility mapping in Isparta-Antalya highway (D-685). Turkey Arabian J Geosci 13(16):1–16. https://doi.org/10.1007/s12517-020-05764-2
36. Holec J, Bednarik M, Šabo M, Minár J, Yilmaz I, Marschalko M (2013) A small-scale landslide susceptibility assessment for the territory of Western Carpathians. Nat Hazard 69(1):1081–1107. https://doi.org/10.1007/s11069-013-0751-6
37. Huggel C, Clague JJ, Korup O (2012) Is climate change responsible for changing landslide activity in high mountains? Earth Surf Processes Landforms 37(1):77–91. https://doi.org/10.1002/esp.2223
38. IPCC (2014) In: Field CB, Barros VR, Dokken DJ, Mach JJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds), Climate change 2014: Impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovermental panel on climate change, pp 11–32. Cambridge University Press, New York
39. Jeong S, Lee K, Kim J, Kim Y (2017) Analysis of rainfall-induced landslide on unsaturated soil slopes. Sustainability 9(7):1280. https://doi.org/10.3390/su9071280
40. Juang CS, Stanley TA, Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: introducing the cooperative open online landslide repository (COOLR). PLoS ONE 14(7):e0218657. https://doi.org/10.1371/journal.pone.0218657
41. Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ 653.801–814. https://doi.org/10.1016/j.scitotenv.2018.10.431.
42. Kadavi PR, Lee CW, Lee S (2018) Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens 10(8):1252. https://doi.org/10.3390/rs10081252
43. Kanwal S, Atif S, Shafiq M (2017) GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomatics Nat Hazards Risk 8(2):348–366. https://doi.org/10.1080/19475705.2016.1220023
44. Katzenberger A, Schewe J, Pongratz J, Levermann A (2021) Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models. Earth Syst Dyn 12(2):367–386. https://doi.org/10.5194/esd-12-367-2021
45. KC S (2013) Community vulnerability to floods and landslides in Nepal. Ecol Soc. https://doi.org/10.5751/ES-05095-180108
46. Kirschbaum D, Kapnick SB, Stanley T, Pascale S (2020) Changes in extreme precipitation and landslides over High Mountain Asia. Geophys Res Lett 47(4):p.e2019FL085347. https://doi.org/10.1029/2019GL085347
47. Krishnan R, Sanjay J, Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S (2020) Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MOES), government of India, p 226. Springer Nature
48. Kumpulainen S (2006) Vulnerability concepts in hazard and risk assessment. Special Paper-Geol Surv Finland 42:65
49. Lee DH, Yang YE, Lin HM (2007) Assessing slope protection methods for weak rock slopes in Southwestern Taiwan. Eng Geol 91(2–4):100–116. https://doi.org/10.1016/j.enggeo.2006.12.005
50. Mahad NF, Yusof N, Ismail NF (2019) The application of fuzzy analytic hierarchy process (FAHP) approach to solve multi-criteria decision making (MCDM) problems. J Phys Conf Ser 1358(1):012081
51. Mallick J, Singh RK, AlAwadh MA, Islam S, Khan RA, Qureshi MN (2018) GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed. Saudi Arabia Environ Earth Sci 77(7):1–25. https://doi.org/10.1007/s12665-018-7451-1
52. Mandal S, Mandal K (2018) Bivariate statistical index for landslide susceptibility mapping in the Rorachu river basin of eastern Sikkim Himalaya. India Spatial Inform Res 26(1):59–75. https://doi.org/10.1007/s41324-017-0156-9
53. Mandal S, Mondal S (2019) Concept on landslides and landslide susceptibility. In: Statistical approaches for landslide susceptibility assessment and prediction, pp 1–39. Springer, Cham. https://doi.org/10.1007/978-3-319-93897-4
54. Mandal RK, Alam MA, Baba U, Raiula T, Kumar S (2020) Status and velocity of urbanization in Arunachal Pradesh, India: a new direction. Elem Educ Online 19(4):3040–3054. https://doi.org/10.17051/ilkonline.2020.04.764679
55. IPCC (2021) Summary for policymakers. In: Climate change 2021: the physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change, Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthews JBR, Maycock TK, Waterfield T, Yelekçi O, Yu R, Zhou B (eds). Cambridge University Press. In Press
56. Medina V, Hürlimann M, Guo Z, Lloret A, Vaunat J (2021) Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale. CATENA 201:105213. https://doi.org/10.1016/j.catena.2021.105213
57. Naaz H, Akram M (2017) Nutritional status of children and adults in India: alarming revelations from NFHS-4. Man India. 97(23 Part 3):655–65
58. Naghadehi MZ, Mikaeil R, Ataei M (2009) The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine. Iran Expert Syst Appl 36(4):8218–8226. https://doi.org/10.3390/w11071402
59. National Portal of India (2012) Arunachal Pradesh Disaster Management Policy (APDMP). http://arunachalplan.gov.in/html/docs/AP_DisasterManagementPolicy.pdf. Accessed on 13th September, 2021.
60. NITI Aayog (2021a) NER District SDG Index and Dashboard 2021–22: An Introduction to the Baseline Report. https://sdgindiaindex.niti.gov.in/NER/dashboard/.
61. NITI Aayog (2021b) North Eastern Region District SDG Index Report & Dashboard 2021b–22. https://sdgindiaindex.niti.gov.in/NER/dashboard/#/.
62. Nohani E, Moharrami M, Sharafi S, Khosravi K, Pradhan B, Pham BT, Lee S, Melesse AM (2019) Landslide susceptibility mapping using different GIS-based bivariate models. Water 11(7):1402.https://doi.org/10.3390/w11071402
63. Nsengiyumva JB, Luo G, Hakorimana E, Mind’je R, Gasirabo A, Mukanyandwi V (2019) Comparative analysis of deterministic and semiquantitative approaches for shallow landslide risk modeling in Rwanda. Risk Anal 39(11):2576–2595. https://doi.org/10.1111/risa.13359
64. Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P, Dubash NK (2014) Climate change 2014: synthesis report. Contribution of working groups i, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (p. 151). Ipcc
65. Patnaik SK (2021) Urban housing in Itanagar: mountain geomorphology and hazard vulnerability vis-a-vis smart city framework. In: Geospatial technology and smart cities, pp 381–401. Springer, Cham
66. Pattanaik A, Singh TK, Saxena M, Prusty BG (2019) Landslide susceptibility mapping using AHP along Mechuka Valley, Arunachal Pradesh, India. In: Proceedings of international conference on remote sensing for disaster management, pp 635–651. Springer, Cham. https://doi.org/10.1007/978-3-319-77276-9
67. Pehlivan NY, Paksoy T, Çalik A (2017) Comparison of methods in FAHP with application in supplier Selection. In: Fuzzy analytic hierarchy process, pp 45–76. Chapman and Hall/CRC. DOI: https://doi.org/10.1201/9781315369884
68. Pham NT, Nong D, Sathyan AR, Garschagen M (2020) Vulnerability assessment of households to flash floods and landslides in the poor upland regions of Vietnam. Clim Risk Manage 28(100215):1–4. https://doi.org/10.1016/j.crm.2020.100215
69. Pham QB, Achour Y, Ali SA, Parvin F, Vojtek M, Vojteková J, Al-Ansari N, Achu AL, Costache R, Khedher KM, Anh DT (2021) A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomat Nat Haz Risk 12(1):1741–1777. https://doi.org/10.1080/19475705.2021.1944330
70. Phukon P, Chetia D, Das P (2012) Landslide susceptibility assessment in the Guwahati city, Assam using analytic hierarchy process (AHP) and geographic information system (GIS). Int J Comput Appl Eng Sci 2:1–6
71. Pollock W, Wartman J (2020) Human vulnerability to landslides. GeoHealth 4(10):p.e2020GH000287. https://doi.org/10.1029/2020GH000287
72. Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2012) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Terrigenous mass movements, pp 23–49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25495-6_2
73. Pourghasemi HR, Yansari ZT, Panagos P, Pradhan B (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arabian J Geosci 11(9):1–12. https://doi.org/10.1007/s12517-018-3531-5
74. Raja NB, Çiçek I, Türkoğlu N, Aydin O, Kawasaki A (2017) Landslide susceptibility mapping of the Sera River Basin using logistic regression model. Nat Hazard 85(3):1323–1346. https://doi.org/10.1007/s11069-016-2591-7
75. Ramli MF, Yusof N, Yusoff MK, Juahir H, Shafri HZM (2010) Lineament mapping and its application in landslide hazard assessment: a review. Bull Eng Geol Environ 69(2):215–233. https://doi.org/10.1007/s10064-009-0255-5
76. Rawat JS, Joshi RC (2012) Remote-sensing and GIS-based landslide-susceptibility zonation using the landslide index method in Igo River Basin, Eastern Himalaya. India Intern J Remote Sens 33(12):3751–3767. https://doi.org/10.1080/01431161.2011.633121
77. Rehman S, Sahana M, Kumar P, Ahmed R, Sajjad H (2021) Assessing hazards induced vulnerability in coastal districts of India using site-specific indicators: An integrated approach. GeoJournal 86(5):2245–2266. https://doi.org/10.1007/s10708-020-10187-3
78. Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
79. Rostamy AAA, Shaverdi M, Amiri B, Takanlou FB (2012) Using fuzzy analytical hierarchy process to evaluate main dimensions of business process reengineering. J Appl Oper Res 4(2):69–77
80. Roy J, Saha S (2019) Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal. India Geoenviron Disasters 6(1):1–18. https://doi.org/10.1186/s40677-019-0126-8
81. Saha A, Saha S (2021) Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach. Environ Dev Sustain. https://doi.org/10.1007/s10668-020-00783-1
82. Saleem N, Huq M, Twumasi NYD, Javed A, Sajjad A (2019) Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility mapping and landslide risk assessment: a review. ISPRS Int J Geo-Inf 8(12):545. https://doi.org/10.3390/ijgi8120545
83. Sartohadi J, Harlin Jennie Pulungan NA, Nurudin M, Wahyudi W (2018) The ecological perspective of landslides at soils with high clay content in the middle Bogowonto Watershed, Central Java, Indonesia. Appl Environ Soil Sci. https://doi.org/10.1155/2018/2648185
84. Segoni S, Pappafico G, Luti T, Catani F (2020) Landslide susceptibility assessment in complex geological settings: Sensitivity to geological information and insights on its parameterization. Landslides 17(10):2443–2453. https://doi.org/10.1007/s10346-019-01340-2
85. Sekhose K, Das G (2021) A note on post-disaster preliminary assessment of landslide around Donyi colony near Jollang road, Papumpare, Arunachal Pradesh. Geological Survey of India: Landslide Incidences for the period 2020. https://employee.gsi.gov.in/cs/groups/public/documents/document/b3zp/odi0/~edisp/dcport1gsigovi824845.pdf. Accessed on 7th August, 2021.
86. Seneviratne S, Nicholls N, Easterling D, Goodess C, Kanae S, Kossin J, Luo Y, Marengo J, McInnes K, Rahimi M, Reichstein M (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, pp 109–230
87. Sevgen E, Kocaman S, Nefeslioglu HA, Gokceoglu C (2019) A novel performance assessment approach using photogrammetric techniques for landslide susceptibility mapping with logistic regression. ANN Random Forest Sens 19(18):3940. https://doi.org/10.3390/s19183940
88. Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T (2016) Fuzzy shannon entropy: A hybrid gis-based landslide susceptibility mapping method. Entropy 18(10):343. https://doi.org/10.3390/e18100343
89. Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5(1):1–15. https://doi.org/10.1038/srep09899
90. Shano L, Raghuvanshi TK, Meten M (2020) Landslide susceptibility evaluation and hazard zonation techniques–a review. Geoenviron Disasters 7:1–19. https://doi.org/10.1186/s40677-020-00152-0
91. Sharma S, Mahajan AK (2019) A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed. India Bull Eng Geol Environ 78(4):2431–2448. https://doi.org/10.1007/s10064-018-1259-9
92. Sharma J, Ravindranath NH (2019) Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change. Environ Res Commun 1(5):051004. https://doi.org/10.1088/2515-7620/ab24ed
93. Sharma H, Singh SK, Srivastava S (2020) Socio-economic inequality and spatial heterogeneity in anaemia among children in India: Evidence from NFHS-4 (2015–16). Clinical Epidemiol Global Health 8(4):1158–1171. https://doi.org/10.1016/j.cegh.2020.04.009
94. Sidle RC, Ochiai H (2006) Introduction and overview of landslide problems. Landslides: Processes. Prediction, and Land Use 18:1–22. https://doi.org/10.1002/9781118665954.ch1
95. Singh CD, Kumar P (2010) Highly damaging small landslides of north east India. Jour Engg Geol 36:1–4
96. Singh Y, Singh T, Kaushal PD (2008) GIS based landslide inventory of Itanagar-the capital of Arunachal Pradesh. Indian Landslide 1(2):19–26
97. Singh CD, Kohli A, Kumar P (2014) Comparison of results of BIS and GSI guidelines on macrolevel landslide hazard zonation—A case study along highway from Bhalukpong to Bomdila, West Kameng district, Arunachal Pradesh. J Geol Soc India 83(6):688–96. 0016-7622/2014-83-6-688
98. Singh P, Sharma A, Sur U, Rai PK (2021) Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh. India Environ Develop Sustain 23(4):5233–5250. https://doi.org/10.1007/s10668-020-00811-0
99. Stanley T, Kirschbaum DB (2017) A heuristic approach to global landslide susceptibility mapping. Nat Hazard 87(1):145–164. https://doi.org/10.1007/s11069-017-2757-y
100. Sujakhu NM, Ranjitkar S, He J, Schmidt-Vogt D, Su Y, Xu J (2019) Assessing the livelihood vulnerability of rural indigenous households to climate changes in Central Nepal. Himalaya Sustain 11(10):2977
101. Sur U, Singh P, Meena SR (2020) Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomatics Nat Hazards Risk 11(1):2176–2209. https://doi.org/10.1080/19475705.2020.1836038
102. Sur U, Singh P, Meena SR (2022) Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Geomat Nat Haz Risk 11(1):2176–2209. https://doi.org/10.1080/19475705.2020.1836038
103. Tang C, Zhu J, Ding J, Cui XF, Chen L, Zhang JS (2011) Catastrophic debris flows triggered by a 14 August 2010 rainfall at the epicenter of the Wenchuan earthquake. Landslides 8(4):485–497. https://doi.org/10.1007/s10346-011-0269-5
104. The Economic Times (2022). Landslides Wreak Havoc in Arunachal Pradesh. https://economictimes.indiatimes.com/news/elections/lok-sabha/india/landslides-wreak-havoc-in-arunachal-pradesh/articleshow/91618880.cms. Accessed 16 August, 2022.
105. UNISDR (2017) Economic losses, poverty & disasters 1998–2017. Centre for research on the Epidemiology of Disasters CRED. https://www.preventionweb.net/files/61119_credeconomiclosses.pdf. Accessed on 2nd September, 2021.
106. Vojteková J, Vojtek M (2020) Assessment of landslide susceptibility at a local spatial scale applying the multi-criteria analysis and GIS: a case study from Slovakia. Geomatics Nat Hazards Risk 11(1):131–148. https://doi.org/10.1080/19475705.2020.1713233
107. Wubalem A, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2(5):1–19. https://doi.org/10.1007/s42452-020-2563-0
108. Yalcin A, Reis S, Aydinoglu AC, 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
109. Yanar T, Kocaman S, Gokceoglu C (2020) Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS Int J Geo-Inf 9(2):114. https://doi.org/10.3390/ijgi9020114
110. Zhao S, Zhao Z (2021) A comparative study of landslide susceptibility mapping using SVM and PSO-SVM models based on Grid and Slope Units. Math Probl Eng. https://doi.org/10.1155/2021/8854606

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-d631a42d-5438-4f3b-ae2e-15641b767503