IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i6p833-d830331.html
   My bibliography  Save this article

Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia

Author

Listed:
  • Siti Norsakinah Selamat

    (Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Nuriah Abd Majid

    (Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Mohd Raihan Taha

    (Department of Civil Engineering, Faculty of Engineering and Build Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia)

  • Ashraf Osman

    (Department of Engineering, The Palatine Centre, Durham University, Stockton Road, Durham DH1 3LE, UK)

Abstract

Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin.

Suggested Citation

  • Siti Norsakinah Selamat & Nuriah Abd Majid & Mohd Raihan Taha & Ashraf Osman, 2022. "Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia," Land, MDPI, vol. 11(6), pages 1-21, June.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:6:p:833-:d:830331
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/6/833/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/6/833/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodeano Roslee & Alvyn Clancey Mickey & Norbert Simon & Mohd. Norazman Norhisham, 2017. "Landslide susceptibility analysis lsa using weighted overlay method wom along the genting sempah to bentong highway pahang," Malaysian Journal of Geosciences (MJG), Zibeline International Publishing, vol. 1(2), pages 13-19, September.
    2. Sangseom Jeong & Azman Kassim & Moonhyun Hong & Nader Saadatkhah, 2018. "Susceptibility Assessments of Landslides in Hulu Kelang Area Using a Geographic Information System-Based Prediction Model," Sustainability, MDPI, vol. 10(8), pages 1-19, August.
    3. Mohammad Subhi Al-batah & Mutasem Sh. Alkhasawneh & Lea Tien Tay & Umi Kalthum Ngah & Habibah Hj Lateh & Nor Ashidi Mat Isa, 2015. "Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, October.
    4. Yanbo Cao & Xinsheng Wei & Wen Fan & Yalin Nan & Wei Xiong & Shilin Zhang, 2021. "Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.
    5. Luísa Vieira Lucchese & Guilherme Garcia Oliveira & Olavo Correa Pedrollo, 2021. "Mamdani fuzzy inference systems and artificial neural networks for 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. 106(3), pages 2381-2405, April.
    6. Amar Regmi & Kohki Yoshida & Hidehisa Nagata & Ananta Pradhan & Biswajeet Pradhan & Hamid Pourghasemi, 2013. "The relationship between geology and rock weathering on the rock instability along Mugling–Narayanghat road corridor, Central Nepal 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. 66(2), pages 501-532, March.
    7. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    8. Jie Dou & Hiromitsu Yamagishi & Hamid Pourghasemi & Ali Yunus & Xuan Song & Yueren Xu & Zhongfan Zhu, 2015. "An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan," 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. 78(3), pages 1749-1776, September.
    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. Nabila Nurul Hawa & Sharifah Zarina Syed Zakaria & Muhammad Rizal Razman & Nuriah Abd Majid & Aizat Mohd Taib & Emrizal, 2023. "Element of Disaster Risk Reduction in Geography Education in Malaysia," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    2. 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.
    3. Li He & Xiantan Wu & Zhengwei He & Dongjian Xue & Fang Luo & Wenqian Bai & Guichuan Kang & Xin Chen & Yuxiang Zhang, 2023. "Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    4. 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.

    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. 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.
    2. Yigen Qin & Genlan Yang & Kunpeng Lu & Qianzheng Sun & Jin Xie & Yunwu Wu, 2021. "Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China," Sustainability, MDPI, vol. 13(11), pages 1-20, June.
    3. 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.
    4. Xianyu Yu & Tingting Xiong & Weiwei Jiang & Jianguo Zhou, 2023. "Comparative Assessment of the Efficacy of the Five Kinds of Models in Landslide Susceptibility Map for Factor Screening: A Case Study at Zigui-Badong in the Three Gorges Reservoir Area, China," Sustainability, MDPI, vol. 15(1), pages 1-26, January.
    5. Paul Sestraș & Ștefan Bilașco & Sanda Roșca & Sanda Naș & Mircea V. Bondrea & Raluca Gâlgău & Ioel Vereș & Tudor Sălăgean & Velibor Spalević & Sorin M. Cîmpeanu, 2019. "Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    6. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    7. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," 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. 103(3), pages 2631-2689, September.
    8. Fang Zou & Qingming Zhan & Weisi Zhang, 2018. "Quantifying the impact of human activities on geological hazards in mountainous areas: evidence from Shennongjia, China," 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. 90(1), pages 137-155, January.
    9. Kinley Tshering & Phuntsho Thinley & Mahyat Shafapour Tehrany & Ugyen Thinley & Farzin Shabani, 2020. "A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS," Forecasting, MDPI, vol. 2(2), pages 1-23, March.
    10. Rongwei Li & Shucheng Tan & Mingfei Zhang & Shaohan Zhang & Haishan Wang & Lei Zhu, 2024. "Geological Disaster Susceptibility Evaluation Using a Random Forest Empowerment Information Quantity Model," Sustainability, MDPI, vol. 16(2), pages 1-18, January.
    11. Mohammad Mehrabi, 2022. "Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy," 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. 111(1), pages 901-937, March.
    12. Quang-Khanh Nguyen & Dieu Tien Bui & Nhat-Duc Hoang & Phan Trong Trinh & Viet-Ha Nguyen & Isık Yilmaz, 2017. "A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS," Sustainability, MDPI, vol. 9(5), pages 1-24, May.
    13. Jie Dou & Ali P. Yunus & Yueren Xu & Zhongfan Zhu & Chi-Wen Chen & Mehebub Sahana & Khabat Khosravi & Yong Yang & Binh Thai Pham, 2019. "Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China," 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. 97(2), pages 579-609, June.
    14. Wamba Danny Love Djukem & Anika Braun & Armand Sylvain Ludovic Wouatong & Christian Guedjeo & Katrin Dohmen & Pierre Wotchoko & Tomas Manuel Fernandez-Steeger & Hans-Balder Havenith, 2020. "Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon," IJERPH, MDPI, vol. 17(18), pages 1-27, September.
    15. R. Sivakumar & Snehasish Ghosh, 2021. "Assessment of the influence of physical and seismotectonic parameters on landslide occurrence: an integrated geoinformatic approach," 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(3), pages 2765-2811, September.
    16. Rahim Tavakolifar & Himan Shahabi & Mohsen Alizadeh & Sayed M. Bateni & Mazlan Hashim & Ataollah Shirzadi & Effi Helmy Ariffin & Isabelle D. Wolf & Saman Shojae Chaeikar, 2023. "Spatial Prediction of Landslides Using Hybrid Multi-Criteria Decision-Making Methods: A Case Study of the Saqqez-Marivan Mountain Road in Iran," Land, MDPI, vol. 12(6), pages 1-19, May.
    17. Derya Ozturk & Nergiz Uzel-Gunini, 2022. "Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, 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. 114(3), pages 2571-2604, December.
    18. Suresh Chaudhary & Yukuan Wang & Amod Mani Dixit & Narendra Raj Khanal & Pei Xu & Kun Yan & Qin Liu & Yafeng Lu & Ming Li, 2019. "Eco-Environmental Risk Evaluation for Land Use Planning in Areas of Potential Farmland Abandonment in the High Mountains of Nepal Himalayas," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    19. Weimin Ye & Cen Gao & Zhangrong Liu & Qiong Wang & Wei Su, 2023. "A Fuzzy-AHP-based variable weight safety evaluation model for expansive soil slope," 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 559-581, October.
    20. Lanqian Feng & Mingming Guo & Wenlong Wang & Yulan Chen & Qianhua Shi & Wenzhao Guo & Yibao Lou & Hongliang Kang & Zhouxin Chen & Yanan Zhu, 2022. "Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling," Sustainability, MDPI, vol. 15(1), pages 1-18, December.

    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:11:y:2022:i:6:p:833-:d:830331. 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.