Skip to main content

Advertisement

Log in

A hybrid integration of analytical hierarchy process (AHP) and the multiobjective optimization on the basis of ratio analysis (MOORA) for landslide susceptibility zonation of Aizawl, India

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Landslide susceptibility zonation mapping is important to identify the places susceptible to landslides according to the failure probability of slopes. Recently, several techniques for assessing the landslide susceptibility zone (LSZ) have been documented with a unique evaluation and validation strategy. Functionally, in LSZ mapping, susceptibility predictions are typically generated in terms of probabilities and likelihoods. The novelty of the current study is a hybrid integration of analytical hierarchy process (AHP) and multi objective optimization on the basis of ratio analysis (MOORA) multi-criteria decision-making to prepare landslide susceptibility zones of Aizawl district of the state of Mizoram, India. The region falls under the tectonically active belt of the Himalayas which makes it landslide prone. Eight morphometric indices, including terrain surface texture, topographic wetness index, slope steepness and length (LS) factor, terrain surface convexity, topographic openness, topographic ruggedness index, morphometric protection index, and stream power index, have been chosen for preparing LSZ after being examined by a multi-collinearity test using variance inflation factors and tolerances. As a result, AHP subjective weighting with an acceptable consistency ratio was used to solve the criteria weight selection problem, and MOORA for weighted overlay with inverse distance weighting interpolation was used. The landslide susceptibility raster was categorised into five landslide susceptibility zones by the natural breaking classification system. As a result, the district comprises a very high landslide susceptibility zone about 8.51%, a high landslide susceptibility zone about 28.30%, a moderate landslide susceptibility zone about 35.75%, a low landslide susceptibility zone about 23.29%, and a very low landslide susceptibility zone about 4.15%. The significance of the study is that integration of AHP and MOORA has shown very high accuracy at 0.981 and the same was evaluated using the ROC (receiver operating characteristics) curve. Overall, the use of the AHP approach in combination with certain other MCDM models, like a hybrid model to develop LSZs, is suggested as the fundamentals of participatory decision-making in AHP for land-use design and landslide hazard management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig.11
Fig. 12

Similar content being viewed by others

Data Availability

Data will be provided upon request by the corresponding author.

References

  • ADDM (2019) Department of Disaster Management & Rehabilitation, Government of Mizoram

  • Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area Bangladesh. Landslides 12(6):1077–1095

    Article  Google Scholar 

  • Ahmed F, Rao K (2016) Morphotectonic studies of the Tuirini drainage basin; a remote sensing and geographic information system perspective. Int J Geol Earth Environ Sci 6(1):54–65

    Google Scholar 

  • Ahmed AD, Abdulah EK, Abdulwahhab BI, Abed NO (2020) Solving multicollinearity problem of gross domestic product using ridge regression method. Period Eng Nat Sci 8(2):668–672

    Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K (2019) Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J Environ Manage 232:928–942

    Article  Google Scholar 

  • Arabameri A, Saha S, Roy J, Chen W, Blaschke T, Tien Bui D (2020) Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed. Iran Remote Sens 12(3):475

    Article  Google Scholar 

  • Arabameri A, Chandra Pal S, Rezaie F, Chakrabortty R, Saha A, Blaschke T et al (2022) Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto Int 37(16):4594–4627

    Article  Google Scholar 

  • Awathankar RV, Rukmini MSS, Raut RD (2021) To mitigate with trusted channel selection using MOORA algorithm in cognitive radio network. Iran J Sci Technol Trans Electr Eng 45(2):381–390

    Article  Google Scholar 

  • Barman J, Biswas B (2022) Application of e-TOPSIS for ground water potentiality zonation using morphometric parameters and geospatial technology of Vanvate Lui Basin, Mizoram, NE India. J Geol Soc India 98(10):1385–1394

    Article  Google Scholar 

  • Barman J, Biswas B, Das J (2022a) Mizoram, the capital of landslide: a review of articles published on landslides in Mizoram, India. In: Das J, Bhattacharya SK (eds) Monitoring and managing multi-hazards. GIScience and geo-environmental modelling. Springer, Cham. https://doi.org/10.1007/978-3-031-15377-8_6

  • Barman J, Soren DDL, Biswas B (2022b) Landslide susceptibility evaluation and analysis: a review on articles published during 2000 to 2020. In: Das J, Bhattacharya SK (eds) Monitoring and managing multi-hazards. GIScience and geo-environmental modelling. Springer, Cham. https://doi.org/10.1007/978-3-031-15377-8_14

  • Barman J, Ali SS, Biswas B, Das J (2023) Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India. Nat Haz Res 3(3):508–521

    Google Scholar 

  • Bednarik M, Magulová B, Matys M, Marschalko M (2010) Landslide susceptibility assessment of the Kraľovany-Liptovský Mikuláš railway case study. Phys Chem Earth Parts a/b/c 35(3–5):162–171

    Article  Google Scholar 

  • Bhaskar AS, Khan A (2022) Comparative analysis of hybrid MCDM methods in material selection for dental applications. Expert Syst Appl 209:118268

    Article  Google Scholar 

  • Bhattacharya P, Mukhopadhyay A, Saha J, Samanta B, Mondal M, Bhattacharya S, Paul S (2023) Perception-satisfaction based quality assessment of tourism and hospitality services in the Himalayan region: an application of AHP-SERVQUAL approach on Sandakphu Trail, West Bengal, India. Int J Geoherit Parks 11(2):259–275

    Article  Google Scholar 

  • Biswas B, Rahaman A, Barman J (2023) Comparative assessment of FR and AHP models for landslide susceptibility mapping for Sikkim, India and preparation of suitable mitigation techniques. J Geolog Soc India. doi https://doi.org/10.1007/s12594-023-xxxx-x

  • Biswas B, Vignesh KS, Ranjan R (2021) Landslide susceptibility mapping using integrated approach of multi-criteria and geospatial techniques at Nilgiris district of India. Arab J Geosci 14(11):980

    Article  Google Scholar 

  • Bragagnolo L, da Silva RV, Grzybowski JMV (2020) Landslide susceptibility mapping with R. landslide: a free open-source GIS-integrated tool based on artificial neural networks. Environ Model Softw 123:104565

  • Brauers WKM (2004) Optimization methods for a stakeholder society. A revolution in economic thinking by multiobjective optimization. Kluwer Academic Publishers, Boston

  • Census of India (2011) Registrar General and Census Commissioner, India

  • Chakrabortty R, Chandra Pal S, Rezaie F, Arabameri A, Lee S, Roy P et al. (2022) Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India. Geocarto Int 37(23):6713–6735

  • Chang Z, Du Z, Zhang F, Huang F, Chen J, Li W, Guo Z (2020) Landslide susceptibility prediction based on remote sensing images and GIS: comparisons of supervised and unsupervised machine learning models. Remote Sens 12(3):502

    Article  Google Scholar 

  • Conforti M, Ietto F (2020) Influence of tectonics and morphometric features on the landslide distribution: a case study from the Mesima Basin (Calabria, South Italy). J Earth Sci 31(2):393–409

    Article  Google Scholar 

  • Daxer C (2020) Topographic openness maps and red relief image maps in OGIS. Tech Rep Inst Geol 17:1–15

    Google Scholar 

  • Dornik A, Drăguţ L, Oguchi T, Hayakawa Y, Micu M (2022) Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas. Sci Rep 12(1):2106

    Article  CAS  Google Scholar 

  • Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y, Zhu Z (2015) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776

    Article  Google Scholar 

  • Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landslide susceptibility assessment. Geomorphology 66(1–4):327–343

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2021) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geogr Inf Sci 35(2):321–347

    Article  Google Scholar 

  • Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189

    Article  Google Scholar 

  • Fernández T, Pérez JL, Cardenal J, Gómez JM, Colomo C, Delgado J (2016) Analysis of landslide evolution affecting olive groves using UAV and photogrammetric techniques. Remote Sens 8(10):837

    Article  Google Scholar 

  • Gadakh VS (2010) Application of MOORA method for parametric optimization of milling process. Int J Appl Eng Res 1(4):743

    Google Scholar 

  • Ghunowa K, MacVicar BJ, Ashmore P (2021) Stream power index for networks (SPIN) toolbox for decision support in urbanizing watersheds. Environ Model Softw 144:105185

    Article  Google Scholar 

  • Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11

    Article  Google Scholar 

  • Haque U, Blum P, Da Silva PF, Andersen P, Pilz J, Chalov SR, Keellings D (2016) Fatal landslides in Europe. Landslides 13:1545–1554

    Article  Google Scholar 

  • Huang F, Zhang J, Zhou C, Wang Y, Huang J, Zhu L (2019) A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides. https://doi.org/10.1007/s10346-019-01274-9

    Article  Google Scholar 

  • Huang F, Cao Z, Guo J, Jiang SH, Li S, Guo Z (2020a) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580

    Article  Google Scholar 

  • Huang F, Cao Z, Jiang SH, Zhou C, Huang J, Guo Z (2020b) Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides 17:2919–2930

    Article  Google Scholar 

  • Iwahashi J, Kamiya I, Yamagishi H (2012) High-resolution DEMs in the study of rainfall-and earthquake-induced landslides: use of a variable window size method in digital terrain analysis. Geomorphology 153:29–38

    Article  Google Scholar 

  • Kadavi PR, Lee CW, Lee S (2018) Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens 10(8):1252

    Article  Google Scholar 

  • Kadavi PR, Lee CW, Lee S (2019) Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models. Environ Earth Sci 78:1–17

    Article  Google Scholar 

  • Karande P, Chakraborty S (2012) Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Mater Des 37:317–324

    Article  Google Scholar 

  • Kayastha P, Dhital MR, De Smedt F (2012) Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed Nepal. Nat Hazards 63(2):479–498

    Article  Google Scholar 

  • Kebede YS, Endalamaw NT, Sinshaw BG, Atinkut HB (2021) Modeling soil erosion using RUSLE and GIS at watershed level in the upper beles Ethiopia. Environ Challenges 2:100009

    Article  Google Scholar 

  • Khan MI (2023) Correlations between factor of safety with distributed load and crest length—Zariwam landslide as case study. Geol Ecol Landsc 1–14

  • Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Prakash I (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323

    Article  Google Scholar 

  • Kim JH, Ahn BS (2019) Extended VIKOR method using incomplete criteria weights. Expert Syst Appl 126:124–132

    Article  Google Scholar 

  • Klar A, Aharonov E, Kalderon‐Asael B, Katz O (2011) Analytical and observational relations between landslide volume and surface area. J Geophys Res Earth Surf 116(F2):1–10. https://doi.org/10.1029/2009JF001604

    Article  Google Scholar 

  • Kumar S, Singh TN (2014) 11th May, 2013 Laipuitlang rockslide, Aizawl, Mizoram, North-East India. Landslide science for a safer geoenvironment: volume 3: targeted landslides. Springer International Publishing, London, pp 401–405

    Google Scholar 

  • Lalchhandama G, Saitluanga BL, Rinawma P (2021) An estimation of annual and seasonal rainfall anomaly index for Aizawl district Mizoram. Geographic 16(1):47–56

    Google Scholar 

  • Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Process Landf J Br Geomorphol Res Group 28(12):1361–1376

    Article  Google Scholar 

  • Lee S, Lee MJ, Lee S (2018) Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees. Environ Earth Sci 77:1–22

    Article  CAS  Google Scholar 

  • Li H, Wang W, Fan L, Li Q, Chen X (2020) A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Appl Soft Comput 91:106207

    Article  Google Scholar 

  • Luirei K, Lokho K, Kothyari GC (2018) Neotectonic activity along the Churachandpur-Mao Fault in and around Karong, Manipur, India: based on morphotectonics and morphometric analyses. Arab J Geosci 11:1–16

    Article  Google Scholar 

  • Ma J, Wang Y, Niu X, Jiang S, Liu Z (2022a) A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression. Stoch Env Res Risk Assess 36(10):3109–3129

    Article  Google Scholar 

  • Ma J, Xia D, Guo H, Wang Y, Niu X, Liu Z, Jiang S (2022b) Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study. Landslides 19(10):2489–2511

    Article  Google Scholar 

  • Ma J, Xia D, Wang Y, Niu X, Jiang S, Liu Z, Guo H (2022c) A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: insights from a case study of landslide displacement prediction. Eng Appl Artif Intell 114:105150

    Article  Google Scholar 

  • Mandal B, Mandal S (2018a) Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya India. Adv Space Res 62(11):3114–3132

    Article  Google Scholar 

  • Mandal S, Mandal K (2018b) 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 4(1):69–88

    Article  Google Scholar 

  • Mandal P, Maiti A, Paul S, Bhattacharya S, Paul S (2022) Mapping the multi-hazards risk index for coastal block of Sundarban, India using AHP and machine learning algorithms. Trop Cyclone Res Review 11(4):225–243

    Article  Google Scholar 

  • Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234

    Article  Google Scholar 

  • Menezes P, Kailas S, Lovell M (2012) Tribological response of materials during sliding against various surface textures. In: Materials and surface engineering. Woodhead Publishing, Sawston, pp 207–242

  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev 207:103225

    Article  Google Scholar 

  • Mhaske SN, Pathak K, Dash SS, Nayak DB (2021) Assessment and management of soil erosion in the hilltop mining dominated catchment using GIS integrated RUSLE model. J Environ Manage 294:112987. https://doi.org/10.1016/j.jenvman.2021.112987

    Article  Google Scholar 

  • Mondal S, Maiti R (2012) Landslide susceptibility analysis of Shiv-Khola watershed, Darjiling: a remote sensing & GIS based Analytical Hierarchy Process (AHP). J Ind Soc Remote Sens 40(3):483–496

    Article  Google Scholar 

  • Mondal M, Haldar S, Biswas A, Mandal S, Bhattacharya S, Paul S (2021) Modeling cyclone-induced multi-hazard risk assessment using analytical hierarchical processing and GIS for coastal West Bengal India. Reg Stud Mar Sci 44:101779

    Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30

    Article  Google Scholar 

  • Moradian M, Modanloo V, Aghaiee S (2019) Comparative analysis of multi criteria decision making techniques for material selection of brake booster valve body. J Traffic Trans Eng (english Edition) 6(5):526–534

    Article  Google Scholar 

  • Morgan RPC, Davidson DA (1991) Soil erosion and conservation. Longman Group, London

    Google Scholar 

  • Moslem S, Çelikbilek Y (2020) An integrated grey AHP-MOORA model for ameliorating public transport service quality. Eur Transp Res Rev 12:1–13

    Article  Google Scholar 

  • Nefeslioglu HA, Sezer EBRU, Gokceoglu C, Bozkir AS, Duman TY (2010) Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey. Mathematical Problems in Engineering, 2010

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Parkash S (2011) Historical records of socio-economically significant landslides in India. J S Asia Disaster Stud 4(2):177–204

    Google Scholar 

  • Patil P (2012) Disaster management in India. Indian Res J 2(1):1–6

    Google Scholar 

  • Patnaik PK, Swain PTR, Mishra SK, Purohit A, Biswas S (2020) Composite material selection for structural applications based on AHP-MOORA approach. Mater Today Proc 33:5659–5663

    Article  CAS  Google Scholar 

  • Peng D, Xu Q, Liu F, He Y, Zhang S, Qi X et al (2018) Distribution and failure modes of the landslides in Heitai terrace, China. Eng Geol 236:97–110

    Article  Google Scholar 

  • Perley MM, Guo J (2016) A case study of geologic hazards affecting school buildings: evaluating seismic structural vulnerability and landslide hazards at schools in Aizawl, India. In: AGU fall meeting abstracts, vol 2016, pp ED31B-0890

  • Perlman DL, Milder J (2005) Practical ecology for planners, developers, and citizens. Island Press, Washington

    Google Scholar 

  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250

    Article  Google Scholar 

  • Pham BT, Jaafari A, Prakash I, Bui DT (2019) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bull Eng Geol Env 78(4):2865–2886

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed Iran. Nat Hazards 63:965–996

    Article  Google Scholar 

  • Pourtaghi ZS, Pourghasemi HR, Rossi M (2015) Forest fire susceptibility mapping in the Minudasht forests, Golestan province Iran. Environ Earth Sci 73(4):1515–1533

    Article  Google Scholar 

  • Prima ODA, Yoshida T (2010) Characterization of volcanic geomorphology and geology by slope and topographic openness. Geomorphology 118(1–2):22–32

    Article  Google Scholar 

  • Rahaman A, Biswas B, Barman J, Suresh VM, Kishor B, Das J (2023) Delineation of groundwater potential zones through AHP: a case study from Tamil Nadu, India. In: Das J, Bhattacharya SK (eds) Monitoring and managing multi-hazards. GIScience and geo-environmental modelling. Springer, Cham. https://doi.org/10.1007/978-3-031-15377-8_21

  • Rahmati O, Darabi H, Panahi M, Kalantari Z, Naghibi SA, Ferreira CSS, Haghighi AT (2020) Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep 10(1):12937

    Article  CAS  Google Scholar 

  • Rao CUB, Verma R (2017) Micro-zonation of landslide hazards between Aizawl City and Lengpui Airport, Mizoram, India, using geoinformatics. Int J Basic Appl Sci 17(05):7–17

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Saaty TL (1970) Optimization in integers and related extremal problems. McGraw-Hill, New York

    Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

  • Saha S, Roy J, Arabameri A, Blaschke T, Tien Bui D (2020) Machine learning-based gully erosion susceptibility mapping: a case study of Eastern India. Sensors 20(5):1313

    Article  CAS  Google Scholar 

  • Salehpour Jam A, Mosaffaie J, Sarfaraz F, Shadfar S, Akhtari R (2021) GIS-based landslide susceptibility mapping using hybrid MCDM models. Nat Hazards 108:1025–1046

    Article  Google Scholar 

  • Sar N, Khan A, Das A, Mipun BS, Chatterjee S (2016) Coupling of analytical hierarchy process and frequency ratio based spatial prediction of soil erosion susceptibility in Keleghai river basin, India. Int Soil Water Conserv Res

  • Schaeffer C, Huang MH, Smedley A, Sitar N, Dreger DS (2014) Landslide hazard in Aizawl, India revealed from field and geodetic observations and hillslope stability analysis. In: AGU fall meeting abstracts, vol 2014, pp NH43A-3795

  • Sedghiyan D, Ashouri A, Maftouni N, Xiong Q, Rezaee E, Sadeghi S (2021) Prioritization of renewable energy resources in five climate zones in Iran using AHP, hybrid AHP-TOPSIS and AHP-SAW methods. Sustain Energy Technol Assess 44:101045

    Google Scholar 

  • Senouci R, Taibi NE, Teodoro AC, Duarte L, Mansour H, Yahia Meddah R (2021) GIS-based expert knowledge for landslide susceptibility mapping (LSM): case of Mostaganem coast district, west of Algeria. Sustainability 13(2):630

    Article  Google Scholar 

  • Shano L, Raghuvanshi TK, Meten M (2020) Landslide susceptibility evaluation and hazard zonation techniques—a review. Geoenviron Disasters 7(1):1–19

    Article  Google Scholar 

  • 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 Env 78(4):2431–2448

    Article  Google Scholar 

  • Sharma A, Sur U, Singh P, Rai PK, Srivastava PK (2020) Probabilistic landslide hazard assessment using statistical information value (SIV) and GIS techniques: a case study of Himachal Pradesh, India. Techn Disaster Risk Manag Mitig 197–208

  • Shirvani Z (2020) A holistic analysis for landslide susceptibility mapping applying geographic object-based random forest: a comparison between protected and non-protected forests. Remote Sens 12(3):434

    Article  Google Scholar 

  • Singh PK, Ratan D, Singh KK, Singh TN (2016) Landslide in fractured and stratified rocks—a case from Aizawl, Mizoram, India. In: Recent advances in rock engineering (RARE 2016). Atlantis Press, Amsterdam, pp 375–380

  • Soeters R, Van Westen CJ (1996) Slope instability recognition, analysis and zonation. Landslides Investig Mitig 247:129–177

  • Sureeyatanapas P (2016) Comparison of rank-based weighting methods for multi-criteria decision making. Eng Appl Sci Res 43:376–379

    Google Scholar 

  • Thao NX (2021) MOORA models based on new score function of interval-valued intuitionistic sets and apply to select materials for mushroom cultivation. Neural Comput Appl 33(17):10975–10985

    Article  Google Scholar 

  • Trevisani S, Cavalli M, Marchi L (2012) Surface texture analysis of a high-resolution DTM: interpreting an alpine basin. Geomorphology 161:26–39

    Article  Google Scholar 

  • Turner AK (2018) Social and environmental impacts of landslides. Innov Infrastruct Solut 3:1–25

    Article  CAS  Google Scholar 

  • Utama DM, Asrofi MS, Amallynda I (2021) Integration of AHP-MOORA algorithm in green supplier selection in the Indonesian textile industry. In: Journal of Physics: Conference Series, vol 1933, No. 1. IOP Publishing, Bristol, p 012058

  • Velasquez M, Hester PT (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66

    Google Scholar 

  • Verma R (2014) Ngaizel landslide, Aizawl, Mizoram, India: a case of wedge failure. In: XII IAEG Congress, Torino, Italy

  • Vinoth M, Prasad PS, Mathur S, Kumar K (2022) Investigation and design of remedial measures for landslide in Hunthar Veng, Mizoram—a case study. In: Stability of slopes and underground excavations: proceedings of Indian geotechnical conference 2020, vol 3. Springer Singapore, Singapore, pp 79–90

  • Wang TC, Lee HD (2009) Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst Appl 36(5):8980–8985

    Article  Google Scholar 

  • Wang Y, Zhao B, Li J (2018) Mechanism of the catastrophic June 2017 landslide at Xinmo village, Songping River, Sichuan province China. Landslides 15(2):333–345

    Article  Google Scholar 

  • Westen CV, Terlien MJT (1996) An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth Surf Process Landforms 21(9):853–868

  • World Bank (2005) The World Bank annual report 2005: year in review, vol 1. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/7537. License: CCBY3.0IGO

  • 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 2:1–19

    Article  Google Scholar 

  • Xin P, Liu Z, Wu SR, Liang C, Lin C (2018) Rotational–translational landslides in the Neogene basins at the northeast margin of the Tibetan Plateau. Eng Geol 244:107–115

    Article  Google Scholar 

  • Yokoyama R, Shirasawa M, Pike RJ (2002) Visualizing topography by openness: a new application of image processing to digital elevation models. Photogramm Eng Remote Sens 68(3):257–266

    Google Scholar 

  • Zavadskas EK, Antuchevičienė J, Kapliński O (2015) Multi-criteria decision making in civil engineering: part I–a state-of-the-art survey. Eng Struct Technol 7(3):103–113

    Google Scholar 

  • Zhang G, Wang M, Liu K (2019) Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China. Int J Disaster Risk Sci 10:386–403

    Article  CAS  Google Scholar 

  • Zhang T, Li Y, Wang T, Wang H, Chen T, Sun Z, Han L (2022) Evaluation of different machine learning models and novel deep learning-based algorithm for landslide susceptibility mapping. Geosci Lett 9(1):1–16

    Article  Google Scholar 

  • 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 2021:1–15

    Google Scholar 

Download references

Acknowledgements

Authors would like to acknowledge the Head of Departments of: Geography and RM and Geology department of Mizoram University. The first author (J. Barman) is thankful to the UGC for awarding the Junior Research fellowship. The first author heartily acknowledges the Head, Department of Geography, Mizoram University, India, for providing suitable research infrastructure to carry out this work. The author also expresses heartfelt thanks to the University Grants Commission, New Delhi, for providing a Junior Research Fellowship under Arts and Humanities (UGC ref. no. 190510087462). All the authors are thankful to the anonymous reviewers whose helpful comments have increased the quality of the present work.

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Brototi Biswas.

Ethics declarations

Conflict of interests

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Barman, J., Biswas, B. & Rao, K.S. A hybrid integration of analytical hierarchy process (AHP) and the multiobjective optimization on the basis of ratio analysis (MOORA) for landslide susceptibility zonation of Aizawl, India. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06538-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11069-024-06538-9

Keywords

Navigation