Abstract
A comprehensive study of landslide susceptibility was carried out in Dejen district which have experienced repeated landslide activities. The purpose of this study was to investigate and identify the landslide susceptible areas using the most common, widely accepted, and ensemble models, analytical hierarchal process (AHP), frequency ratio (FR), and Shannon entropy (SE) and evaluate their performances. In this study, 87 landslides were identified, and a landslide inventory map has been generated. Twelve landslide conditioning factors like aspect, geology, elevation, slope, soil, rainfall, land use land cover (LULC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), curvature, distance from the road, and distance from the river were extracted and prepared from the spatial database. Based on the analysis, the high and very high percentage of landslide susceptibility classes cover about 54% of the study area. The receiver operating curves (ROC) and area under curve (AUC) used to validate the models. The results revealed that AHP model with a success rate of 86.5% and predictive rate 85.8% performs better than SE (success rate = 85.6%, and predictive rate = 85.3%) and FR model (success rate = 82.5% and predictive rate 81.8%). The results could be helpful for planners for general land use planning and management.
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References
Abebe B, Dramis F, Fubelli G, Umer M, Asrat A (2010) Landslides in the Ethiopian highlands and the Rift margins. J Afr Earth Sci 56(4–5):131–138. https://doi.org/10.1016/j.jafrearsci.2009.06.006
Abay A, Barbieri G, Woldearegay K (2019) Gis-based landslide susceptibility evaluation using analytical hierarchy process (ahp) approach: The case of Tarmaber district. Ethiopia Momona Ethiopian Journal of Science 11(1):14–36. https://doi.org/10.4314/mejs.v11i1.2
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
Agarwal E, Agarwal R, Garg RD, Garg PK (2013) Delineation of groundwater potential zone: an AHP/ANP approach. J Earth Syst Sci 122(3):887–898. https://doi.org/10.1007/s12040-013-0309-8
Akinci H, Kilicoglu C, Dogan S (2020) Random forest-based landslide susceptibility mapping in coastal regions of Artvin. Turkey Int J Geogr Inf Sci 9(9):553. https://doi.org/10.3390/ijgi9090553
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. B Eng Geol Environ 58(1):21–44. https://doi.org/10.1007/s100640050066
Althuwaynee OF, Pradhan B (2017) Semi-quantitative landslide risk assessment using GIS-based exposure analysis in Kuala Lumpur City. Geomat Nat Haz Risk 8(2):706–732. https://doi.org/10.1080/19475705.2016.1255670
Anderson J R (1976) A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
Arabameri A, Pradhan B, Rezaei K, Lee CW (2019) Assessment of landslide susceptibility using statistical-and artificial intelligence-based FR–RF integrated model and multiresolution DEMs. Remote Sens-Basel 11(9):999. https://doi.org/10.3390/rs11090999
Ardizzone F, Cardinali M, Galli M, Guzzetti F, Reichenbach P (2003, April) A comprehensive assessment of landslide hazard in the Staffora Basin, Northern Italian Apennines. In EGS-AGU-EUG Joint Assembly (p. 1728).
Arnone E, Francipane A, Noto LV, Scarbaci A, La Loggia G (2014) Strategies investigation in using artificial neural network for landslide susceptibility mapping: application to a Sicilian catchment. J Hydroinform 16(2):502–515. https://doi.org/10.2166/hydro.2013.191
G Assefa 1991 Lithostratigraphy and environment of deposition of the Late Jurassic-Early Cretaceous sequence of the central part of Northwestern Plateau, Ethiopia NeuesJahrbuch Für Geologie Und Paläontologie-Abhandlungen 255–284https://doi.org/10.1127/njgpa/182/1991/255
Ayalew L (1999) The effect of seasonal rainfall on landslides in the highlands of Ethiopia. B Eng Geol Environ 58(1):9–19. https://doi.org/10.1007/s100640050065
Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Eng Geol 81(4), 432–445.
Ayalew L, Yamagishi H (2004) Slope failures in the Blue Nile basin, as seen from landscape evolution perspective. Geomorphology 57(1–2):95–116. https://doi.org/10.1016/S0169-555X(03)00085-0
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Central Japan Geomorphology 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010
Ayenew T, Barbieri G (2005) Inventory of landslides and susceptibility mapping in the Dessie area, northern Ethiopia. Eng Geol 77(1–2):1–15. https://doi.org/10.1016/j.enggeo.2004.07.002
Berhane G, Kebede M, Alfarah N, Hagos E, Grum B, Giday A, Abera T (2020) Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia. J Afr Earth Sci J 164:103795. https://doi.org/10.1016/j.jafrearsci.2020.103795
Bragagnolo L, da Silva RV, Grzybowski JMV (2020) Artificial neural network ensembles applied to the mapping of landslide susceptibility. CATENA 184:104240. https://doi.org/10.1016/j.catena.2019.104240
Chen W, Peng J, Hong H, Shahabi H, Pradhan B, Liu J, Duan Z (2018) Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Sci Total Environ 626:1121–1135. https://doi.org/10.1016/j.scitotenv.2018.01.124
Chen W, Zhang S, Li R, Shahabi H (2018) Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ 644:1006–1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
Cheng YS, Yu TT, Son NT (2021) Random forests for landslide prediction in Tsengwen River Watershed. Central Taiwan Remote Sens-Basel 13(2):199. https://doi.org/10.3390/rs13020199
Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b
Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). CATENA 113:236–250. https://doi.org/10.1016/j.catena.2013.08.006
Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. B Eng Geol Environ 73(2):209–263. https://doi.org/10.1007/s10064-013-0538-8
Cuny X, Lejeune M (2003) Statistical modelling and risk assessment. Safety Sci 41(1):29–51. https://doi.org/10.1016/S0925-7535(01)00056-X
Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64(1):65–87. https://doi.org/10.1016/S0013-7952(01)00093-X
Deif A, El-Hussain I, Al-Jabri K, Toksoz N, El-Hady S, Al-Hashmi S, Al-Saifi M (2013) Deterministic seismic hazard assessment for Sultanate of Oman. Arab J Geosci 6(12):4947–4960. https://doi.org/10.1007/s12517-012-0790-4
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards 65(1):135–165. https://doi.org/10.1007/s11069-012-0347-6
Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Pham B T (2020) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed. Japan Landslides 17(3):641–658. https://doi.org/10.1007/s10346-019-01286-5
Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modelling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221
El Jazouli A, Barakat A, Khellouk R (2019) GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geo Environmental Disasters 6(1):1–12. https://doi.org/10.1186/s40677-019-0119-7
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
Fang Z, Wang Y, Duan G, Peng L (2021) Landslide susceptibility mapping using rotation forest ensemble technique with different decision trees in the Three Gorges Reservoir Area. China Remote Sens-Basel 13(2):238. https://doi.org/10.3390/rs13020238
Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10(5):1–13. https://doi.org/10.1007/s12517-017-2918-z
Gonai Y, Tsukamoto S, Enokida M, Ichikawa K, Nakagawa A, Takeuchi T (2013) Case example of GIS utilization on Abay Gorge’s landslide survey in Ethiopia. In Earthquake-Induced Landslides (pp. 699–706). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32238-9_76
Grilli ST, Taylor ODS, Baxter CD, Maretzki S (2009) A probabilistic approach for determining submarine landslide tsunami hazard along the upper east coast of the United States. Mar Geol 264(1–2):74–97. https://doi.org/10.1016/j.margeo.2009.02.010
Guettouche M S (2013) Modelling and risk assessment of landslides using fuzzy logic. Application on the slopes of the Algerian Tell (Algeria). Arab J Geosci 6(9), 3163–3173. https://doi.org/10.1007/s12517-012-0607-5
Hall LW Jr, Anderson RD (1999) A deterministic ecological risk assessment for copper in European saltwater environments. Mar Pollut Bull 38(3):207–218. https://doi.org/10.1016/S0025-326X(98)00164-7
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
Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529. https://doi.org/10.1016/j.catena.2018.03.003
Ishizaka A, Labib A (2014) A hybrid and integrated approach to evaluate and prevent disasters. Journal of the Operational Research Society 65(10):1475–1489. https://doi.org/10.1057/jors.2013.59
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 Te 11(4):909–926. https://doi.org/10.1007/s13762-013-0464-0
Kanungom D P, Arora M K, Sarkar S, Gupta R P (2012) Landslide susceptibility zonation (LSZ) mapping–a review.
Kaur L, Rishi MS, Siddiqui AU (2020) Deterministic and probabilistic health risk assessment techniques to evaluate non-carcinogenic human health risk (NHHR) due to fluoride and nitrate in groundwater of Panipat, Haryana. India Environ Pollut 259:113711. https://doi.org/10.1016/j.envpol.2019.113711
Kavzoglu T, Sahin EK, Colkesen I (2015) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Nat Hazards 76(1):471–496. https://doi.org/10.1007/s11069-014-1506-8
Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28. https://doi.org/10.1016/j.geomorph.2005.07.005
Krkač M, Gazibara SB, Arbanas Ž, Sečanj M, Arbanas SM (2020) A comparative study of random forests and multiple linear regression in the prediction of landslide velocity. Landslides 17(11):2515–2531. https://doi.org/10.1007/s10346-020-01476-6
Krkač M, Špoljarić D, Bernat S, Arbanas SM (2017) Method for prediction of landslide movements based on random forests. Landslides 14(3):947–960. https://doi.org/10.1007/s10346-016-0761-z
Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin. Korea Environ Geol 40(9):1095–1113. https://doi.org/10.1007/s002540100310
Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun. Korea Environ Geol 44(7):820–833. https://doi.org/10.1007/s00254-003-0825-y
Lee S, Ryu JH, Lee MJ, Won JS (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung. Korea Math Geol 38(2):199–220. https://doi.org/10.1007/s11004-005-9012-x
Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms: British Geomor 28(12):1361–1376. https://doi.org/10.1002/esp.593
Magliulo P, Di Lisio A, Russo F, Zelano A (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47(3):411–435. https://doi.org/10.1007/s11069-008-9230-x
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
Mandal S, Mondal S (2019) Statistical approaches for landslide susceptibility assessment and prediction. Springer International Publishing. https://doi.org/10.1007/978-3-319-93897-4_1
Mehrabi M, Pradhan B, Moayedi H, Alamri A (2020) Optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art meta heuristic techniques. Sensors 20(6):1723. https://doi.org/10.3390/s20061723
Mittal SK, Dhingra S, Sardana HK (2011) Analysis of data using neuro fuzzy approach recorded by instrumentation network installed at Mansa Devi (Haridwar) landslide site. J Sci Ind Res India 70(1):25–31
Mondal S, Maiti R (2013) Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya. Int J Disast Risk Sc 4(4):200–212. https://doi.org/10.1007/s13753-013-0021-y
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
Ngadisih Yatabe R, Bhandary N P, Dahal R K (2014) Integration of statistical and heuristic approaches for landslide risk analysis: a case of volcanic mountains in West Java Province, Indonesia. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 8(1), 29–47. https://doi.org/10.1080/17499518.2013.826030
Nnorom IC, Ewuzie U, Eze SO (2019) Multivariate statistical approach and water quality assessment of natural prings and other drinking water sources in South-eastern Nigeria. Heliyon 5(1):e01123. https://doi.org/10.1016/j.heliyon.2019.e01123
Nohani E, Moharrami M, Sharafi S, Khosravi K, Pradhan B, Pham BT, Melesse M, A (2019) Landslide susceptibility mapping using different GIS-based bivariate models. Water-Sui 11(7):1402. https://doi.org/10.3390/w11071402
Nourani V, Pradhan B, Ghaffari H, Sharifi SS (2014) Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models. Nat Hazards 71(1):523–547. https://doi.org/10.1007/s11069-013-0932-3
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
Orozova IM, Suhadolc P (1999) A deterministic–probabilistic approach for seismic hazard assessment. Tectonophysics 312(2–4):191–202. https://doi.org/10.1016/S0040-1951(99)00162-6
Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197. https://doi.org/10.1016/j.jseaes.2012.12.014
Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. Springerplus 2(1):1–11. https://doi.org/10.1186/2193-1801-2-523
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area. Korea Environ Earth Sci 68(5):1443–1464. https://doi.org/10.1007/s12665-012-1842-5
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-Basel 10(10):1545. https://doi.org/10.3390/rs10101545
Pasang S, Kubíček P (2020) Landslide susceptibility mapping using statistical methods along the Asian Highway. Bhutan Geosciences 10(11):430. https://doi.org/10.3390/geosciences10110430
Peres DJ, Cancelliere A (2018) Modelling impacts of climate change on return period of landslide triggering. J Hydrol 567:420–434. https://doi.org/10.1016/j.jhydrol.2018.10.036
Pham BT, Prakash I, Khosravi K, Chapi K, Trinh PT, Ngo TQ, Bui DT (2019a) A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto Int 34(13):1385–1407. https://doi.org/10.1080/10106049.2018.1489422
Pham BT, Jaafari A, Prakash I, Bui DT (2019b) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modelling. B Eng Geol Environ 78(4):2865–2886. https://doi.org/10.1007/s10064-018-1281-y
Pham BT, Bui DT, Prakash I (2018) Bagging based support vector machines for spatial prediction of landslides. Environ Earth Sci 77(4):1–17. https://doi.org/10.1007/s12665-018-7268-y
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province. Iran Environ Earth Sci 75(3):185
Pourghasemi HR, Mohammady M, Pradhan B (2012) 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
Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4(1):1–15
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, 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 Science 18(2):235–250. https://doi.org/10.1016/j.ejrs.2015.08.001
Raman R, Punia M (2012) The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley Western Himalaya, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 6(3), 145–161. https://doi.org/10.1080/17499518.2011.637504
Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Akgun KT, A (2014) 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. https://doi.org/10.1007/s12517-012-0807-z
Reis S, Yalcin A, Atasoy M, Nisanci RECEP, Bayrak T, Ekercin EMURAT, S (2012) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environ Earth Sci 66(7):2063–2073. https://doi.org/10.1007/s12665-011-1432-y
Remondo J, Bonachea J, Cendrero A (2005) A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment. Landslides 2(4):321–328. https://doi.org/10.1007/s10346-005-0016-x
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
Roy J, Saha S (2019) Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal. India Geoenvironmental Disasters 6(1):1–18. https://doi.org/10.1186/s40677-019-0126-8
TL Saaty 1980 The Analytical Hierarchy Process McGraw-Hill New York
Safa M, Sari PA, Shariati M, Suhatril M, Trung NT, Wakil K, Khorami M (2020) Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes. Physica A 550:124046. https://doi.org/10.1016/j.physa.2019.124046
Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides 2(1):61–69. https://doi.org/10.1007/s10346-004-0039-8
Sahin EK, Colkesen I, Kavzoglu T (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 35(4):341–363. https://doi.org/10.1080/10106049.2018.1516248
Saito H, Nakayama D, Matsuyama H (2009) Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains. Japan Geomorphology 109(3–4):108–121. https://doi.org/10.1016/j.geomorph.2009.02.026
Salciarini D, Godt JW, Savage WZ, Baum RL, Conversini P (2008) Modeling landslide recurrence in Seattle, Washington, USA. Eng Geol 102(3–4):227–237. https://doi.org/10.1016/j.enggeo.2008.03.013
Sameen MI, Sarkar R, Pradhan B, Drukpa D, Alamri AM, Park HJ (2020) Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests. Comput Geosci 134:104336. https://doi.org/10.1016/j.cageo.2019.104336
Sara F, Silvia B, Sandro M (2015) Landslide inventory updating by means of persistent scatterer interferometry (PSI): the Setta basin (Italy) case study. Geomat Nat Haz Risk 6(5–7):419–438. https://doi.org/10.1080/19475705.2013.866985
Sdao F, Lioi DS, Pascale S, Caniani D, Mancini IM (2013) Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat Hazard Earth Sys 13(2):395–407. https://doi.org/10.5194/nhess-13-395-2013
Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep-Uk 5(1):1–15. https://doi.org/10.1038/srep09899
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(1):1–16. https://doi.org/10.1186/s40677-018-0097-1
Shihabudheen KV, Peethambaran B (2017) Landslide displacement prediction technique using improved neuro-fuzzy system. Arab J Geosci 10(22):1–14. https://doi.org/10.1007/s12517-017-3278-4
Silalahi FES, Arifianti Y, Hidayat F (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java. Indonesia Geoscience Letters 6(1):1–17. https://doi.org/10.1186/s40562-019-0140-4
Sujatha ER (2012) Geoinformatics based landslide susceptibility mapping using probabilistic analysis and entropy index of Tevankarai stream sub-watershed. India Disaster Adv 5(3):26–33
Sun D, Wen H, Wang D, Xu J (2020) A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology 362:107201. https://doi.org/10.1016/j.geomorph.2020.107201
Tian Y, Xu C, Hong H, Zhou Q, Wang D (2019) Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event. Geomat Nat Haz Risk 10(1), 1–25. https://doi.org/10.1080/19475705.2018.1487471
Tsangaratos P, Ilia I (2016) Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection. Greece Landslides 13(2):305–320. https://doi.org/10.1007/s10346-015-0565-6
Vakhshoori V, Zare M (2016) Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomat Nat Haz Risk 7(5):1731–1752. https://doi.org/10.1080/19475705.2016.1144655
Van Westen CJ (2000) The modelling of landslide hazards using GIS. Surv Geophys 21(2):241–255. https://doi.org/10.1023/A:1006794127521
Van Westen CJ, Van Asch TW, Soeters R (2006) Landslide hazard and risk zonation—why is it still so difficult? B Eng Geol Environ 65(2):167–184. https://doi.org/10.1007/s10064-005-0023-0
Wang Y, Sun D, Wen H, Zhang H, Zhang F (2020) Comparison of random forest model and frequency ratio model for landslide susceptibility mapping (LSM) in Yunyang County (Chongqing, China). Int J Env Res Pub He 17(12):4206. https://doi.org/10.3390/ijerph17124206
Wronna M, Omira R, Baptista MA (2015) Deterministic approach for multiple-source tsunami hazard assessment for Sines. Portugal Nat Hazard Earth Sys 15(11):2557–2568. https://doi.org/10.5194/nhess-15-2557-2015
Wu T H, Tang W H, Einstein H H (1996) Landslides: investigation and mitigation. Chapter 6-landslide hazard and risk assessment. Trans Res B (247).
Wu Y, Ke Y, Chen Z, Liang S, Zhao H, Hong H (2020) Application of alternating decision tree with Ada Boost and bagging ensembles for landslide susceptibility mapping. CATENA 187:104396. https://doi.org/10.1016/j.catena.2019.104396
Wubalem A (2020) Modeling of Landslide susceptibility in a part of Abay Basin, northwestern Ethiopia. Open Geosci 12(1):1440–1467. https://doi.org/10.1515/geo-2020-0206
Xie M, Esaki T, Qiu C, Wang C, Wang Z (2009) Deterministic landslide risk assessment at a past landslide site. Geotech Geol Eng 27(3):355–364. https://doi.org/10.1007/s10706-008-9232-1
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
Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. CATENA 72(1):1–12. https://doi.org/10.1016/j.catena.2007.01.003
Yesilnacar, E., & Topal, T. (2005). Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology. https://doi.org/10.1016/j.enggeo.2005.02.002
Yang Z, Qiao J (2010, August) Regional landslide zonation based on entropy method in Three Gorges area, China. In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 3, pp. 1336–1339). IEEE. https://doi.org/10.1109/FSKD.2010.5569097
Youssef AM, Pradhan B, Jebur MN, El-Harbi HM (2015) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area. Saudi Arabia Environ Earth Sci 73(7):3745–3761. https://doi.org/10.1007/s12665-014-3661-3
Zêzere JL, Garcia RAC, Oliveira SC, Reis E (2008) Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon (Portugal). Geomorphology 94(3–4):467–495. https://doi.org/10.1016/j.geomorph.2006.10.040
Zhao L, Wu X, Niu R, Wang Y, Zhang K (2020) Using the rotation and random forest models of ensemble learning to predict landslide susceptibility. Geomat Nat Haz Risk 11(1):1542–1564. https://doi.org/10.1080/19475705.2020.1803421
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Melese, T., Belay, T. & Andemo, A. Application of analytical hierarchal process, frequency ratio, and Shannon entropy approaches for landslide susceptibility mapping using geospatial technology: The case of Dejen district, Ethiopia. Arab J Geosci 15, 424 (2022). https://doi.org/10.1007/s12517-022-09672-5
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DOI: https://doi.org/10.1007/s12517-022-09672-5