IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i2p468-d1067042.html
   My bibliography  Save this article

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Author

Listed:
  • Sheela Bhuvanendran Bhagya

    (Department of Coastal Disaster Management, Pondicherry University, Brookshabad Campus, Port Blair 744103, India)

  • Anita Saji Sumi

    (Department of Coastal Disaster Management, Pondicherry University, Brookshabad Campus, Port Blair 744103, India)

  • Sankaran Balaji

    (Department of Coastal Disaster Management, Pondicherry University, Brookshabad Campus, Port Blair 744103, India)

  • Jean Homian Danumah

    (Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), Université Félix Houphouët-Boigny, Abidjan 00225, Côte d’Ivoire)

  • Romulus Costache

    (National Institute of Hydrology and Water Management, 013686 Bucharest, Romania
    Department of Civil Engineering, Transilvania University of Brasov, 500036 Brasov, Romania
    Danube Delta National Institute for Research & Development, 820112 Tulcea, Romania)

  • Ambujendran Rajaneesh

    (Department of Geology, University of Kerala, Thiruvananthapuram 695581, India)

  • Ajayakumar Gokul

    (Kerala State Emergency Operations Centre (KSEOC), Kerala State Disaster Management Authority (KSDMA), Thiruvananthapuram 695033, India)

  • Chandini Padmanabhapanicker Chandrasenan

    (Kerala State Emergency Operations Centre (KSEOC), Kerala State Disaster Management Authority (KSDMA), Thiruvananthapuram 695033, India)

  • Renata Pacheco Quevedo

    (Earth Observation and Geoinformatics Division, National Institute for Space Research (INPE), São José dos Campos 12227010, Brazil)

  • Alfred Johny

    (Kerala State Emergency Operations Centre (KSEOC), Kerala State Disaster Management Authority (KSDMA), Thiruvananthapuram 695033, India)

  • Kochappi Sathyan Sajinkumar

    (Department of Geology, University of Kerala, Thiruvananthapuram 695581, India
    Department of Geological & Mining Engineering & Sciences, Michigan Technological University, Houghton, MI 49931, USA)

  • Sunil Saha

    (Department of Geography, University of Gour Banga, Malda 732101, India)

  • Rajendran Shobha Ajin

    (Kerala State Emergency Operations Centre (KSEOC), Kerala State Disaster Management Authority (KSDMA), Thiruvananthapuram 695033, India
    Resilience Development Initiative (RDI), Bandung 40123, Indonesia)

  • Pratheesh Chacko Mammen

    (Kerala State Emergency Operations Centre (KSEOC), Kerala State Disaster Management Authority (KSDMA), Thiruvananthapuram 695033, India)

  • Kamal Abdelrahman

    (Department of Geology & Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Mohammed S. Fnais

    (Department of Geology & Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Mohamed Abioui

    (Department of Earth Sciences, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco
    MARE-Marine and Environmental Sciences Centre, Sedimentary Geology Group, Department of Earth Sciences, Faculty of Sciences and Technology, University of Coimbra, 3030-790 Coimbra, Portugal)

Abstract

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency.

Suggested Citation

  • Sheela Bhuvanendran Bhagya & Anita Saji Sumi & Sankaran Balaji & Jean Homian Danumah & Romulus Costache & Ambujendran Rajaneesh & Ajayakumar Gokul & Chandini Padmanabhapanicker Chandrasenan & Renata P, 2023. "Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps," Land, MDPI, vol. 12(2), pages 1-29, February.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:468-:d:1067042
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/2/468/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/2/468/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sk Ajim Ali & Farhana Parvin & Quoc Bao Pham & Khaled Mohamed Khedher & Mahro Dehbozorgi & Yasin Wahid Rabby & Duong Tran Anh & Duc Hiep Nguyen, 2022. "An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1601-1633, September.
    2. Dieu Tien Bui & Biswajeet Pradhan & Owe Lofman & Inge Revhaug, 2012. "Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-26, July.
    3. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    4. Indrajit Chowdhuri & Subodh Chandra Pal & Rabin Chakrabortty & Sadhan Malik & Biswajit Das & Paramita Roy, 2021. "Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 697-722, May.
    5. Mario Arroyo-Solórzano & Adolfo Quesada-Román & Gustavo Barrantes-Castillo, 2022. "Seismic and geomorphic assessment for coseismic landslides zonation in tropical volcanic contexts," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2811-2837, December.
    6. H. A. Nefeslioglu & E. Sezer & C. Gokceoglu & A. S. Bozkir & T. Y. Duman, 2010. "Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-15, February.
    7. Katharina Gompf & Marzia Traverso & Jörg Hetterich, 2021. "Using Analytical Hierarchy Process (AHP) to Introduce Weights to Social Life Cycle Assessment of Mobility Services," Sustainability, MDPI, vol. 13(3), pages 1-10, January.
    8. Reshma T. Vilasan & Vijay S. Kapse, 2022. "Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1767-1793, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feifei Jiang & Fu Chen & Yan Sun & Ziyi Hua & Xinhua Zhu & Jing Ma, 2023. "Spatiotemporal Pattern and Driving Mechanism of Cultivated Land Use Transition in China," Land, MDPI, vol. 12(10), pages 1-20, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Syaidatul Azwani Zulkafli & Nuriah Abd Majid & Ruslan Rainis, 2023. "Spatial Analysis on the Variances of Landslide Factors Using Geographically Weighted Logistic Regression in Penang Island, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-26, January.
    2. Uzodigwe Emmanuel Nnanwuba & Shengwu Qin & Oluwafemi Adewole Adeyeye & Ndichie Chinemelu Cosmas & Jingyu Yao & Shuangshuang Qiao & Sun Jingbo & Ekene Mathew Egwuonwu, 2022. "Prediction of Spatial Likelihood of Shallow Landslide Using GIS-Based Machine Learning in Awgu, Southeast/Nigeria," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    3. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    4. Qiang Liu & Aiping Tang & Ziyuan Huang & Lixin Sun & Xiaosheng Han, 2022. "Discussion on the tree-based machine learning model in the study of landslide susceptibility," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 887-911, September.
    5. Shabnam Mehrnoor & Maryam Robati & Mir Masoud Kheirkhah Zarkesh & Forough Farsad & Shahram Baikpour, 2023. "Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(3), pages 1997-2030, February.
    6. Abhik Saha & Vasanta Govind Kumar Villuri & Ashutosh Bhardwaj, 2022. "Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India," Land, MDPI, vol. 11(10), pages 1-27, October.
    7. José Carlos Romero & Pedro Linares, 2021. "Multiple Criteria Decision-Making as an Operational Conceptualization of Energy Sustainability," Sustainability, MDPI, vol. 13(21), pages 1-14, October.
    8. Adolfo Quesada-Román & Lidia Torres-Bernhard & Karla Hernández & Natalia Martínez-Rojas, 2024. "Historical trends and future implications of disasters in Honduras," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12313-12339, October.
    9. Kai Sun & Zhiqing Li & Shuangjiao Wang & Ruilin Hu, 2024. "A support vector machine model of landslide susceptibility mapping based on hyperparameter optimization using the Bayesian algorithm: a case study of the highways in the southern Qinghai–Tibet Plateau," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 11377-11398, September.
    10. Siti Norsakinah Selamat & Nuriah Abd Majid & Aizat Mohd Taib, 2023. "A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-21, January.
    11. Idiano D’Adamo & Rocío González-Sánchez & Maria Sonia Medina-Salgado & Davide Settembre-Blundo, 2021. "E-Commerce Calls for Cyber-Security and Sustainability: How European Citizens Look for a Trusted Online Environment," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    12. Yu Duan & Junnan Xiong & Weiming Cheng & Nan Wang & Yi Li & Yufeng He & Jun Liu & Wen He & Gang Yang, 2022. "Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 269-294, January.
    13. Halil Akinci & Mustafa Zeybek, 2021. "Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 1515-1543, September.
    14. Omar Sharaf-addeen Alansary & Tareq Al-Ansari, 2023. "Developing a Strategic Sustainability Assessment Methodology for Free Zones Using the Analytical Hierarchy Process Approach," Sustainability, MDPI, vol. 15(13), pages 1-28, June.
    15. Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Farbod Farhangi & Soo-Mi Choi, 2021. "COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(18), pages 1-21, September.
    16. Bangjie Fu & Yange Li & Zheng Han & Zhenxiong Fang & Ningsheng Chen & Guisheng Hu & Weidong Wang, 2023. "RIPF-Unet for regional landslides detection: a novel deep learning model boosted by reversed image pyramid features," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 701-719, October.
    17. Arlene Lu-Gonzales & Takuji W. Tsusaka & Sylvia Szabo & Reuben M. J. Kadigi & Camilla Blasi Foglietti & Seree Park & Zoe Matthews, 2023. "Evaluating the Contribution of Complex International Research-for-Development Programmes to the Sustainable Development Goals," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 35(2), pages 380-401, April.
    18. Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 471-497, October.
    19. Okoli Jude Emeka & Haslinda Nahazanan & Bahareh Kalantar & Zailani Khuzaimah & Ojogbane Success Sani, 2021. "Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement," Land, MDPI, vol. 10(10), pages 1-23, September.
    20. Husnain Arshad & Muhammad Jamaluddin Thaheem & Beenish Bakhtawar & Asheem Shrestha, 2021. "Evaluation of Road Infrastructure Projects: A Life Cycle Sustainability-Based Decision-Making Approach," Sustainability, MDPI, vol. 13(7), pages 1-26, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:468-:d:1067042. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.