Abstract
This study aims to demarcate landslide susceptible zones using methods of analytical hierarchy process (AHP) and frequency ratio (FR) to find the most influencing factors and to compare their prediction capability. Ten causative factors (slope angle, elevation, lithology, land use/land cover types, normalized difference moisture index, road buffer, normalized difference built-up index, water ratio index, stream power index, and soil) are used in the study. The area of the landslide susceptibility was grouped into five classes. According to the landslide susceptibility maps prepared using the AHP and FR methods, 11.14% and 6.57% of the area are very highly susceptible to landslides. Finally, the receiver operating characteristic (ROC) curves for the landslide susceptibility maps prepared using both AHP and FR methods were plotted, and the area under the ROC curve (AUC) values were estimated to validate the results. AUC values of 0.69 and 0.81 were estimated for the landslide susceptible zone maps prepared using AHP and FR, respectively. From the AUC values, it is confirmed that the FR method is more effective in predicting the landslide susceptible zones in Idukki district. The landslide susceptibility maps are helpful for land use planners and policy makers in adopting suitable mitigation measures to minimize the impacts of landslides and thereby reduce loss of life and property.
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References
Abraham MT, Pothuraju D, Satyam N (2019) Rainfall thresholds for prediction of landslides in Idukki, India: an empirical approach. Water 11(10). https://doi.org/10.3390/w11102113
Abraham MT, Satyam N, Shreyas N, Pradhan B, Segoni S, Maulud KNA, Alamri AM (2021) Forecasting landslides using SIGMA model: a case study from Idukki, India. Geomat Nat Haz Risk 12(1):540–559. https://doi.org/10.1080/19475705.2021.1884610
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 Journal of Geosciences 10(194). https://doi.org/10.1007/s12517-017-2980-6
Achu AL, Joseph S, Aju CD, Mathai J (2021) Preliminary analysis of a catastrophic landslide event on 6 August 2020 at Pettimudi, Kerala State, India. Landslides 18:1459–1463. https://doi.org/10.1007/s10346-020-01598-x
Aghda SMF, Bagheri V, Razifard M (2018) Landslide susceptibility mapping using fuzzy logic system and its influences on mainlines in Lashgarak region, Tehran. Iran Geotechnical and Geological Engineering 36(2):915–937. https://doi.org/10.1007/s10706-017-0365-y
Aimaiti Y, Liu W, Yamazaki F, Maruyama Y (2019) Earthquake-induced landslide mapping for the 2018 Hokkaido Eastern Iburi earthquake using PALSAR-2 data. Remote Sensing 11(20). https://doi.org/10.3390/rs11202351
Ajin RS, Loghin AM, Vinod PG, Jacob MK, Krishnamurthy KK (2016) Landslide susceptible zone mapping using ARS and GIS techniques in selected taluks of Kottayam district, Kerala, India. International Journal of Applied Remote Sensing and GIS 3(1):16–25
Althuwaynee OF, Pradhan B, Park HJ, 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. https://doi.org/10.1016/j.catena.2013.10.011
Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277. https://doi.org/10.1016/0013-7952(92)90053-2
Cancela J, Fico G, Arredondo Waldmeyer MT (2015) Using the analytic hierarchy process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson’s disease. BMC Med Inform Decis Mak 15:S7. https://doi.org/10.1186/1472-6947-15-S3-S7
Cardinali M, Galli M, Guzzetti F, Ardizzone F, Reichenbach P, Bartoccini P (2006) Rainfall induced landslides in December 2004 in south-western Umbria, central Italy: types, extent, damage and risk assessment. Nat Hazard 6:237–260
Chawla A, Chawla S, Pasupuleti S, Rao ACS, Sarkar K, Dwivedi R (2018) Landslide susceptibility mapping in Darjeeling Himalayas. Advances in Civil Engineering, India. https://doi.org/10.1155/2018/6416492
Chen W, Han H, Huang B, Huang Q, Fu X (2018) A data-driven approach for landslide susceptibility mapping: a case study of Shennongjia Forestry District. China, Geomatics, Natural Hazards and Risk 9(1):720–736. https://doi.org/10.1080/19475705.2018.1472144
Chen W, Li W, Chai H, Hou E, Li X, Ding X (2016) GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji city, China. Environmental Earth Sciences 75(63). https://doi.org/10.1007/s12665-015-4795-7
Cruden DM (1991) A simple definition of a landslide. Bull Eng Geol Env 43(1):27–29
Dahoua L, Yakovitch SV, Hadji R, Farid Z (2018) Landslide susceptibility mapping using analytic hierarchy process method in BBA-Bouira Region, case study of East-West Highway, NE Algeria. In: Kallel A, Ksibi M, Ben Dhia H, Khélifi N (eds) Recent advances in environmental science from the Euro-Mediterranean and surrounding regions. EMCEI 2017. Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for Sustainable Development). Springer, Switzerland: pp 1837–1840. https://doi.org/10.1007/978-3-319-70548-4_532
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island. Hong Kong Geomorphology 42(3–4):213–228. https://doi.org/10.1016/S0169-555X(01)00087-3
Danumah JH, Odai SN, Saley BM, Szarzynski J, Thiel M, Kwaku A, Kouame FK, Akpa LY (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire). Geoenvironmental Disasters 3.https://doi.org/10.1186/s40677-016-0044-y
Demir G, Aytekin M, Akgün A, İkizler 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. https://doi.org/10.1007/s11069-012-0418-8
Devara M, Tiwari A, Dwivedi R (2021) Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis. Geomat Nat Haz Risk 12(1):675–693. https://doi.org/10.1080/19475705.2021.1887939
Ehret D, Rohn J, Dumperth C, Eckstein S, Ernstberger S, Otte K, Rudolph R, Wiedenmann J, Xiang W, Bi R (2010) Frequency ratio analysis of mass movements in the Xiangxi catchment, Three Gorges Reservoir area, China. Journal of Earth Science 21:824–834. https://doi.org/10.1007/s12583-010-0134-9
El Jazouli A, Barakat A, Khellouk R (2019) GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters 6(3). https://doi.org/10.1186/s40677-019-0119-7
Elmoulat M, Ait Brahim L (2018) Landslides susceptibility mapping using GIS and weights of evidence model in Tetouan-Ras-Mazari area (Northern Morocco). Geomat Nat Haz Risk 9(1):1306–1325. https://doi.org/10.1080/19475705.2018.1505666
Emrouznejad A, Marra M (2017) The state of the art development of AHP (1979–2017): a literature review with a social network analysis. Int J Prod Res 55(22):6653–6675. https://doi.org/10.1080/00207543.2017.1334976
Gao BC (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
García-Rodríguez MJ, Malpica JA, Benito B, Díaz M (2008) Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 95(3–4):172–191. https://doi.org/10.1016/j.geomorph.2007.06.001
Geertsema M, Highland L, Vaugeouis L (2009) Environmental impact of landslides. In: Sassa K, Canuti P (eds) Landslides – Disaster Risk Reduction. Springer, Berlin, Heidelberg, pp: 589–607. https://doi.org/10.1007/978-3-540-69970-5_31
Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang KT (2012) Landslide inventory maps: new tools for an old problem. Earth-Science Reviews 112: 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001
Hamza T, Raghuvanshi TK (2017) GIS based landslide hazard evaluation and zonation – a case from Jeldu district, Central Ethiopia. Journal of King Saud University – Science 29(2): 151–165. https://doi.org/10.1016/j.jksus.2016.05.002
Hemasinghe H, Rangali RSS, Deshapriya NL, Samarakoon L (2018) Landslide susceptibility mapping using logistic regression model (a case study in Badulla district, Sri Lanka). Procedia Engineering 212:1046–1053. https://doi.org/10.1016/j.proeng.2018.01.135
Hong Y, Adler RF, Huffman GJ (2007) Satellite remote sensing for global landslide monitoring. EOS Trans Am Geophys Union 88(37):357–368
Huang F, Yao C, Liu W, Li Y, Liu X (2018) Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine. Geomat Nat Haz Risk 9(1):919–938. https://doi.org/10.1080/19475705.2018.1482963
Ibrahim GRF (2017) Urban land use land cover changes and their effect on land surface temperature: case study using Dohuk City in the Kurdistan Region of Iraq. Climate 5(1). https://doi.org/10.3390/cli5010013
Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926. https://doi.org/10.1007/s13762-013-0464-0
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3–4):347–366. https://doi.org/10.1016/j.enggeo.2006.03.004
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2009) Landslide susceptibility zonation (LSZ) mapping - a review. Journal of South Asia Disaster Studies 2(1):81–105
Kanungo DP, Singh R, Dash RK (2020) Field observations and lessons learnt from the 2018 landslide disasters in Idukki district, Kerala. India Current Science 119(11):1797–1806
Karsli F, Atasoy M, Yalcin A, Reis S, Demir O, Gokceoglu C (2009) Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey). Environ Monit Assess 156:241–255. https://doi.org/10.1007/s10661-008-0481-5
Kaur H, Gupta S, Parkash S (2017) Comparative evaluation of various approaches for landslide hazard zoning: a critical review in Indian perspectives. Spat Inf Res 25(3):389–398. https://doi.org/10.1007/s41324-017-0105-7
Kayastha P, Dhital MR, De Smedt F (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed. Nepal Natural Hazards 63(2):479–498. https://doi.org/10.1007/s11069-012-0163-z
Khan H, Shafique M, Khan MA, Bacha MA, Shah SU, Calligaris C (2019) Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. The Egyptian Journal of Remote Sensing and Space Science 22(1):11–24. https://doi.org/10.1016/j.ejrs.2018.03.004
Kouhpeima A, Feiznia S, Ahmadi H, Moghadamnia AR (2017) Landslide susceptibility mapping using logistic regression analysis in Latyan catchment. Desert 22(1): 85–95. https://doi.org/10.22059/jdesert.2017.62181
Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286. https://doi.org/10.1007/s12594-016-0395-8
Kumar MK, Annadurai R (2015) Comparison of frequency ratio model and analytic hierarchy process methods upon landslide susceptibility mapping using geospatial techniques. Disaster Advances 8(5):46–55
Kumar D, Thakur M, Dubey CS, Shukla DP (2017) Landslide susceptibility mapping & prediction using support vector machine for Mandakini river basin, Garhwal Himalaya, India. Geomorphology 295:115–125. https://doi.org/10.1016/j.geomorph.2017.06.013
Lee S (2007) Landslide susceptibility mapping using an artificial neural network in the Gangneung area. Korea International Journal of Remote Sensing 28(21):4763–4783. https://doi.org/10.1080/01431160701264227
Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672. https://doi.org/10.1007/s12040-006-0004-0
Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology 47:982–990. https://doi.org/10.1007/s00254-005-1228-z
Lee S, Choi J, Min K (2004) Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun. Korea International Journal of Remote Sensing 25(11):2037–2052. https://doi.org/10.1080/01431160310001618734
Lee ML, Ng KY, Huang YF, Li WC (2014) Rainfall-induced landslides in Hulu Kelang area. Malaysia Natural Hazards 70(1):353–375. https://doi.org/10.1007/s11069-013-0814-8
Li F, He H (2018) Assessing the accuracy of diagnostic tests. Shanghai Archives of Psychiatry 30(3): 207–212. https://doi.org/10.11919/j.issn.1002-0829.218052
Melo F (2013) Area under the ROC curve. In: Dubitzky W, Wolkenhauer O, Cho KH, Yokota H (eds) Encyclopedia of Systems Biology. Springer, New York. https://doi.org/10.1007/978-1-4419-9863-7_209
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30. https://doi.org/10.1002/hyp.3360050103
Myronidis D, Papageorgiou C, Theophanous S (2016) Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP). Nat Hazards 81(1):245–263. https://doi.org/10.1007/s11069-015-2075-1
Nakamura S, Wakai A, Umemura J, Sugimoto H, Takeshi T (2014) Earthquake-induced landslides: distribution, motion and mechanisms. Soils Found 54(4):544–559. https://doi.org/10.1016/j.sandf.2014.06.001
Nakileza BR, Nedala S (2020) Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda. Geoenvironmental Disasters 7.https://doi.org/10.1186/s40677-020-00160-0
Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276. https://doi.org/10.1016/j.cageo.2010.10.012
Oh HJ, Lee S, Hong SM (2017) Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. Journal of Sensors. https://doi.org/10.1155/2017/3730913
Oh HJ, Kadavi PR, Lee CW, Lee S (2018) Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models. Geomat Nat Haz Risk 9(1):1053–1070. https://doi.org/10.1080/19475705.2018.1481147
Ortiz JAV, Martínez-Graña AM (2018) A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia). Geomat Nat Haz Risk 9(1):1106–1128. https://doi.org/10.1080/19475705.2018.1513083
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.https://doi.org/10.3390/rs10101545
Pham VD, Nguyen Q, Nguyen H, Pham V, Vu VM, Bui Q (2020) Convolutional neural network—optimized moth flame algorithm for shallow landslide susceptible analysis. IEEE Access 8:32727–32736. https://doi.org/10.1109/ACCESS.2020.2973415
Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin. Iran CATENA 97:71–84. https://doi.org/10.1016/j.catena.2012.05.005
Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed. Iran Natural Hazards 63(2):965–996. https://doi.org/10.1007/s11069-012-0217-2
Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, 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
Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759. https://doi.org/10.1016/j.envsoft.2009.10.016
Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48(12):4164–4177. https://doi.org/10.1109/TGRS.2010.2050328
Raghuvanshi TK, Ibrahim J, Ayalew D (2014) Slope stability susceptibility evaluation parameter (SSEP) rating scheme-an approach for landslide hazard zonation. J Afr Earth Sc 99(2):595–612. https://doi.org/10.1016/j.jafrearsci.2014.05.004
Raghuvanshi TK, Negassa L, Kala PM (2015) GIS based grid overlay method versus modeling approach – a comparative study for landslide hazard zonation (LHZ) in Meta Robi district of West Showa Zone in Ethiopia. The Egyptian Journal of Remote Sensing and Space Sciences 18:235–250. https://doi.org/10.1016/j.ejrs.2015.08.001
Ramachandran RM, Reddy CS (2017) Monitoring of deforestation and land use changes (1925–2012) in Idukki district, Kerala, India using remote sensing and GIS. Journal of the Indian Society of Remote Sensing 45:163–170. https://doi.org/10.1007/s12524-015-0521-x
Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenvironmental Disasters 3.https://doi.org/10.1186/s40677-016-0053-x
Ray RL, Jacobs JM (2007) Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat Hazards 43:211–222. https://doi.org/10.1007/s11069-006-9095-9
Resmi TR, Sudharma KV, Hameed AS (2016) Stable isotope characteristics of precipitation of Pamba river basin, Kerala, India. J Earth Syst Sci 125:1481–1493. https://doi.org/10.1007/s12040-016-0747-1
Rostami ZA, Al-modaresi SA, Fathizad H, Faramarzi M (2016) Landslide susceptibility mapping by using fuzzy logic: a case study of Cham-gardalan catchment, Ilam, Iran. Arabian Journal of Geosciences 9(685). https://doi.org/10.1007/s12517-016-2720-3
Roy J, Saha S (2019) Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenvironmental Disasters 6.https://doi.org/10.1186/s40677-019-0126-8
Russo RFSM, Camanho R (2015) Criteria in AHP: a systematic review of literature. Procedia Computer Science 55:1123–1132. https://doi.org/10.1016/j.procs.2015.07.081
Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation (decision making series). McGraw Hill, New York, United States of America
Sar N, Chatterjee S, Adhikari MD (2015) Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai river basin, India. Model Earth Syst Environ 1.https://doi.org/10.1007/s40808-015-0039-9
Sarkar S, Kanungo DP, Mehrotra GS (1995) Landslide hazard zonation: a case study of Gharwal Himalaya. India Mountain Research and Development 15(4):301–309
Sartohadi J, Pulungan NAHJ, Nurudin M, Wahyudi W (2018) The ecological perspective of landslides at soils with high clay content in the Middle Bogowonto Watershed, Central Java, Indonesia. Applied and Environmental Soil Science. https://doi.org/10.1155/2018/2648185
Sassa K, Fukuoka H, Scarascia-Mugnozza G, Evans S (1996) Earthquake-induced-landslides: distribution, motion and mechanisms. Soils Found 36:53–64. https://doi.org/10.3208/sandf.36.Special_53
Semlali I, Ouadif L, Bahi L (2019) Landslide susceptibility mapping using the analytical hierarchy process and GIS. Curr Sci 116(5):773–779
Senthilkumar V, Chandrasekaran SS, Maji VB (2018) Rainfall-induced landslides: case study of the Marappalam landslide, Nilgiris district, Tamil Nadu, India. International Journal of Geomechanics 18(9). https://doi.org/10.1061/(ASCE)GM.1943-5622.0001218
Shahfahad, Kumari B, Tayyab M, Ahmed IA, Baig MRI, Khan MF, Rahman (2020) A longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arab J Geosci 13.https://doi.org/10.1007/s12517-020-06068-1
Shahri AA, Spross J, Johansson F, Larsson S (2019) Landslide susceptibility hazard map in southwest Sweden using artificial neural network. CATENA 183.https://doi.org/10.1016/j.catena.2019.104225
Shano L, Raghuvanshi TK, Meten M (2021) Landslide susceptibility mapping using frequency ratio model: the case of Gamo highland, South Ethiopia. Arab J Geosci 14.https://doi.org/10.1007/s12517-021-06995-7
Sharma S, Mahajan AK (2018) Comparative evaluation of GIS-based landslide susceptibility mapping using statistical and heuristic approach for Dharamshala region of Kangra Valley, India. Geoenvironmental Disasters 5(4). https://doi.org/10.1186/s40677-018-0097-1
Shen L, Li C (2010) Water body extraction from Landsat ETM+ imagery using adaboost algorithm. 2010 18th International Conference on Geoinformatics: 1–4. https://doi.org/10.1109/GEOINFORMATICS.2010.5567762
Sifa SF, Mahmud T, Tarin MA, Haque DME (2019) Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: a case study of Rangamati district in Bangladesh. Geology, Ecology, and Landscapes. https://doi.org/10.1080/24749508.2019.1619222
Silalahi FES, Pamela, Arifianti Y, Hidayat F (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geosci Lett 6.https://doi.org/10.1186/s40562-019-0140-4
Sujatha ER, Sridhar V (2021) Landslide susceptibility analysis: a logistic regression model case study in Coonoor, India. Hydrology 8(1). https://doi.org/10.3390/hydrology8010041
Turrini MC, Visintainer P (1998) Proposal of a method to define areas of landslide hazard and application to an area of the Dolomites. Italy Engineering Geology 50(3–4):255–265. https://doi.org/10.1016/S0013-7952(98)00022-2
United States Geological Survey (2004) Landslide types and processes. Fact Sheet 2004–3072. U.S. Department of the Interior.
Wu Y, Li W, Liu P, Bai H, Wang Q, He J, Liu Y, Sun S (2016) Application of analytic hierarchy process model for landslide susceptibility mapping in the Gangu County, Gansu Province, China. Environmental Earth Sciences 75(422). https://doi.org/10.1007/s12665-015-5194-9
Xie P, Wen H, Ma C, Baise LG, Zhang J (2018) Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China. Geomat Nat Haz Risk 9(1):501–523. https://doi.org/10.1080/19475705.2018.1451399
Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24(3):583–594. https://doi.org/10.1080/01431160304987
Zhang K, Thapa B, Ross M, Gann D (2016) Remote sensing of seasonal changes and disturbances in mangrove forest: a case study from South Florida. Ecosphere 7(6). https://doi.org/10.1002/ecs2.1366
Zhang K, Wu X, Niu R, Yang K, Zhao L (2017) The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China. Environ Earth Sci 76.https://doi.org/10.1007/s12665-017-6731-5
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Thomas, A.V., Saha, S., Danumah, J.H. et al. Landslide Susceptibility Zonation of Idukki District Using GIS in the Aftermath of 2018 Kerala Floods and Landslides: a Comparison of AHP and Frequency Ratio Methods. J geovis spat anal 5, 21 (2021). https://doi.org/10.1007/s41651-021-00090-x
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DOI: https://doi.org/10.1007/s41651-021-00090-x