Abstract
The present study attempts to assess the role of basin morphometric parameters in slope instability using morphometric diversity (MD) model. Also try to find out the role of drainage parameters and relief parameters in slope failure using drainage diversity (DD) and relief diversity (RD) models respectively. For that total 14 morphometric data layers were considered. The relationship of each data layers with landslide susceptibility was judge using frequency ratio (FR) approach. Parameters like drainage density, drainage frequency, relative relief, drainage texture, junction frequency, infiltration number, ruggedness index, dissection index, elevation, slope, relief ratio and hypsometric integral were positively related with landslide potentiality while bifurcation ratio and drainage intensity negatively correlated with slope failure. The principal component analysis (PCA) based weight assign to each data layers of each model which multiplied with unidirectional reclassified data layers for each model using weighted linear combination (WLC) approach to prepare landslide susceptibility maps. The receiver operating characteristics curve showed that, the landslides prediction accuracy of the DD, RD and MD models was 71.4, 73.9 and 76.3% respectively. The FR plots of the aforesaid three models suggested that, the chance of landslide increases from very low to very high susceptibility zones.
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References
Akgun A, Sezer EA, Nefesliogl HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38:23–34
Althuwaynee OF, Pradhan B, Park H, 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
Amani M, Safaviyan A (2015) Sub-basins prioritization using morphometric analysis- remote sensing technique and GIS-Golestan-Iran. Int Lett Nat Sci 38:56–65
Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277
Ardizzone F, Cardinali M, Carrara A, Guzzetti F, Reichenbach P (2002) Impact of mapping errors on the reliability of landslide hazard maps. Nat Hazards Earth Syst Sci 2:3–14
Avinash K, Deepika B, Jayappa KS (2014) Basin geomorphology and drainage morphometry parameters used as indicators for groundwater prospect: insight from geographical information system (GIS) technique. J Earth Sci 25(6):1018–1032. https://doi.org/10.1007/s12583-014-0505-8
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 81:432–445
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick 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
Chau KT, Sze YL, Fung MK, Wong WY, Fong EL, Chan LCP (2004) Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput Geosci 30:429–443
Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2011) 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
Chung C-JF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogram Eng Remote Sens 65(12):1389–1399
Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS Lantau Island Hong Kong. Geomorphology 42:213–228
Devkota KC, Regmi AD, Pourghasemi HR et al (2013a) 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
Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013b) 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 65:135–165
Dove N (1957) The ratio of relative and absolute altitude of Mt.Camel. Geog Rev 47:564–569
Faniran A (1968) The index of drainage intensity—a provisional new drainage factor. Aust J Sci 31:328–330
Farhan Y, Anbar A, Enaba O, Al-Shaikh N (2015) Quantitative analysis of geomorphometric parameters of Wadi Kerak, Jordan, using remote sensing and GIS. J Water Resour Protect 7:456–475. https://doi.org/10.4236/jwarp.2015.76037
Foumelis M, Lekkas E, Parcharidis I (2004) Landslide susceptibility mapping by GIS-based qualitative weighting procedure in Corinth area. Bull Geol Soc Greece XXXVI
Gajbhiye S, Mishra SK, Pandey A (2014) Prioritizing erosion-prone area through morphometric analysis: an RS and GIS perspective. Appl Water Sci 4(1):51–61
Ghosh D (2015) Landslide susceptibility analysis from morphometric parameter analysis of Riyong Khola basin, West Sikkim, India: a geospatial approach. Int J Geol 5(1):54–65
Ghosh KG, Saha S (2015) Identification of soil erosion susceptible areas in Hinglo river basin, Eastern India based on geo-statistics. Univers J Environ Res Technol 5(3)
Ghosh S, Carranza EJM, van Westen CJ, Jetten VG, Bhattacharya DN (2011) Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology 131(1–2):35–56. https://doi.org/10.1016/j.geomorph.2011.04.019
Gopal KG, Saha S (2015) Identification of soil erosion susceptible areas in Hinglo river basin, Eastern India based on geostatistics. Univ J Environ Res Technol 5(3):152–164
Gupta RP, Joshi BC (1990) Landslide Hazard Zonation using the GIS Approach—a case Study from the Ramganga Catchment Himalayas. Eng Geol 28:119–131
Horton RE (1932) Drainage basin characteristics. Am Geophys Union 13:350–361
Horton RE (1945) Erosional development of streams and their drainage basins, a hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56:275–370
Jeganathan C, Chauniyal DD (2000) An evidential weighted approach for landslide hazard zonation from geo-environmental characterization: a case study of Kelani area. Curr Sci 79(2):238–243
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 85:347–366
Karim S, Jalileddin S, Ali MT (2011) Zoning landslide by use of frequency ratio method (case study: Deylaman Region). Middle East J Sci Res 9(5):578–583
Khatun S, Pal S (2017) Categorization of morphometric surface through morphometric diversity analysis in Kushkarani River basin of Eastern India. Asian J Phys Chem Sci 2(1):1–19. https://doi.org/10.9734/ajopacs/2017/31098
Kienholz H (1978) Maps of geomorphology and natural hazards of Grindelwald, Switzerland: scale 1:10,000. Arct Alp Res 10:169–184
Kumar K, Garbyal Y (2016) Analysis of morphometric parameters for the identification of probable landslide occurrences. In: Conference: Geo-Chicago, 14–16 Aug 2016, ASCE library, subject heading- Himalayas, Chicago, Illinois At. https://doi.org/10.1061/9780784480120.035
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41
Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area Cambodia using frequency ratio and logistic regression models. Environ Geol 50:847–855
Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990
Magesh NS, Jitheshlal K, Chandrasekar N, Jini K (2012) GIS based morphometric evaluation of Chimini and Mupily watersheds, parts of Western Ghats, Thrissur district, Kerala, India. Earth Sci Inform 5:111–121
Mahalingam R, Olsen MJ, O’Banion MS (2016) Evaluation of landslide susceptibility mapping techniques using lidar–derived conditioning factors (Oregon case study). Geomat Nat Hazard Risk 7:1884–1907
Majtan S, Omura H, Morita K (2002) Fractal dimension as an indicator of probability for landslides in North Matsuura Japan. Geograficky Casopis 54:5–19
Mandal B, Mandal S (2016) Assessment of mountain slope instability in the Lish river basin of Eastern Darjeeling Himalaya using frequency ratio model (FRM). Earth Syst Environ 2:121. https://doi.org/10.1007/s40808-016-0169-8
Mandal S, Mandal K (2017) Bivariate statistical index for landslide susceptibility mapping in the Rorachu River basin of Eastern Sikkim Himalaya, India. Spat Inf Res. https://doi.org/10.1007/s41324-017-0156-9
Mandal S, Mandal K (2018) Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India. Model Earth Syst Environ. https://doi.org/10.1007/s40808-018-0426-0
Miller CT, Poirier-McNeill MM, Mayer AS (1990) Dissolution of trapped nonaqueous phase liquids: mass transfer characteristics. Water Resour Res 26:2783–2796
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
Mondal S, Mandal S (2017a) RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model. Georisk Assess Manag Risk Eng Syst Geohazards. https://doi.org/10.1080/17499518.2017.1347949
Mondal S, Mandal S (2017b) Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya. Spat Inf Res. https://doi.org/10.1007/s41324-017-0101-y
Nag SK (1998) Morphometric analysis using remote sensing techniques in the Chaka sub-basin, Purulia district, West Bengal. J Indian Soc Remote Sens 26(1):69–76
Nautiyal MD (1994) Morphometric analysis of drainage basin, district Dehradun, Uttar Pradesh. J Indian Soc Remote Sens 22(1994):252–262
Nefeslioglu HA, Sezer E, Go¨kc¸eog˘lu C, Bozkır AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul Turkey. Math Prob Eng (Article ID: 901095)
Nidhi K, Chowdary VM, Tiwari KN, Shinde V, Dadhwal VK (2016) Assessment of surface water potential using morphometry and curve number-based approaches. Geocarto Int 32(11):1206–1228. https://doi.org/10.1080/10106049.2016.1195889
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
Poli S, Sterlacchini S (2007) Landslide representation strategies in susceptibility studies using weights-of-evidence modeling technique. Nat Res Res 16(2). https://doi.org/10.1007/s11053-007-9043-8
Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. https://doi.org/10.1007/s12517-012-0532-7
Pradhan B (2010) Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environ Earth Sci 63:329–349
Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054
Pradhan B, Lee S (2010b) 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
Rai PK, Mishra S, Ahmad A, Mohan K (2014) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci. https://doi.org/10.1007/s13201-014-0238-y
Rastogi RA, Sharma TC (1976) Quantitative analysis of drainage basin characteristics. J Soil Water Conserv India 26(1&4):18–25
Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogram Eng Remote Sens 70(5):617–625
Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Bull Geol Soc Am 67:597
Shrestha S, Kang T, Suwal MS (2017) An ensemble model for co-seismic landslide susceptibility using GIS and random forest method. Int J Geo-Inf 6:365. https://doi.org/10.3390/ijgi6110365
Smith GH (1935) The relative relief of Ohio. Geogr Rev India 25:272–284
Song Y, Gong J, Gao S, Wang D, Cui T, Li Y, Wei B (2012) Susceptibility assessment of earthquake induced landslides using Bayesian network: a case study in Beichuan China. Comput Geosci 42:189–199
Spiker EC, Gori PL (2000) National landslide hazards mitigation strategy: a framework for loss reduction. Department of the interior, U.S. Geol Surv 59
Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Geol Soc Am Bull 63:117–142
Strahler AN (1964) Quantitative geomorphology of drainage basins and channel networks. In: Chow VT (ed) Handbook of applied hydrology, pp 439–476
Thakkar AK, Dhiman SD (2007) Morphometric analysis and prioritization of mini watershed s in Mohr watersheds. Gujarat using remote sensing and GIS techniques. J Indian Soc Remote Sens 33(1):25–38
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Native Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena 145:164–179
Umar Z, Pradhan B, Ahmad A et al (2014) Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118:124–135. https://doi.org/10.1016/j.catena.2014.02.005
Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed China. Geomorphology 145–146:70–80
Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey; Ph.D thesis, Department of Geomatics, University of Melbourne, 423 p
Yin KJ, Yin TZ (1988) Statistical prediction model for slope instability of metamorphosed rock. In: Proceedings of 5th international symposium on landslides Lausanne, Switzerland 2, pp 1269–1272
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Mandal, S., Mondal, S. (2019). Geomorphic Diversity and Landslide Susceptibility: A Multi-criteria Evaluation Approach. In: Geoinformatics and Modelling of Landslide Susceptibility and Risk. Environmental Science and Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-10495-5_4
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