IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i1p861-d1023853.html
   My bibliography  Save this article

A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model 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)

  • Aizat Mohd Taib

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

Abstract

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:861-:d:1023853
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/861/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/861/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xudong Hu & Hongbo Mei & Han Zhang & Yuanyuan Li & Mengdi Li, 2021. "Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest 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. 105(2), pages 1663-1689, January.
    2. 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.
    3. Jie Liu & Zhen Wu & Huiwen Zhang, 2021. "Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    4. 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.
    5. Yongwei Li & Xianmin Wang & Hang Mao, 2020. "Influence of human activity on landslide susceptibility development in the Three Gorges area," 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. 104(3), pages 2115-2151, December.
    6. Tusar Kanti Hembram & Sunil Saha, 2020. "Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand, Eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 1241-1268, February.
    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. Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    2. 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.

    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. Jie Liu & Zhen Wu & Huiwen Zhang, 2021. "Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    2. Cebi, Selcuk & Karakurt, Necip Fazıl & Kurtulus, Erkan & Tokgoz, Bunyamin, 2024. "Development of a decision support system for client acceptance in independent audit process," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    3. Jihyun Yang & Jeffrey Shragge & Aaron J. Girard & Edgard Gonzales & Javier Ticona & Armando Minaya & Richard Krahenbuhl, 2023. "Seismic Characterization of a Landslide Complex: A Case History from Majes, Peru," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    4. Fanyu Zhang & Jianbing Peng & Xiaowei Huang & Hengxing Lan, 2021. "Hazard assessment and mitigation of non-seismically fatal landslides in 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. 106(1), pages 785-804, March.
    5. 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.
    6. Liying Sun & Bingjuan Ma & Liang Pei & Xiaohang Zhang & John L. Zhou, 2021. "The relationship of human activities and rainfall-induced landslide and debris flow hazards in Central 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. 107(1), pages 147-169, May.
    7. Jinming Zhang & Jianxi Qian & Yuefeng Lu & Xueyuan Li & Zhenqi Song, 2024. "Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    8. 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.
    9. Parinaz Rashidi & Sopan D. Patil & Aafke M. Schipper & Rob Alkemade & Isabel Rosa, 2023. "Downscaling Global Land-Use Scenario Data to the National Level: A Case Study for Belgium," Land, MDPI, vol. 12(9), pages 1-19, September.
    10. 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.
    11. 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.
    12. Rabin Chakrabortty & Subodh Chandra Pal & Alireza Arabameri & Phuong Thao Thi Ngo & Indrajit Chowdhuri & Paramita Roy & Sadhan Malik & Biswajit Das, 2022. "Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3518-3557, March.
    13. 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.
    14. Jiaxuan Huang & Weichao Du & Mowen Xie, 2023. "Numerical Modeling of Kinetic Features and Stability Analysis of Jinpingzi Landslide," Land, MDPI, vol. 12(3), pages 1-17, March.
    15. Chelsea Dandridge & Thomas Stanley & Dalia Kirschbaum & Pukar Amatya & Venkataraman Lakshmi, 2023. "The influence of land use and land cover change on landslide susceptibility in the Lower Mekong River Basin," 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(2), pages 1499-1523, January.
    16. Shuai Li & Zhongyun Ni & Yinbing Zhao & Wei Hu & Zhenrui Long & Haiyu Ma & Guoli Zhou & Yuhao Luo & Chuntao Geng, 2022. "Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake," IJERPH, MDPI, vol. 19(6), pages 1-30, March.
    17. Hamza Bouguerra & Salah Eddine Tachi & Hamza Bouchehed & Gordon Gilja & Nadir Aloui & Yacine Hasnaoui & Abdelmalek Aliche & Saâdia Benmamar & Jose Navarro-Pedreño, 2023. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    18. Sudipto Halder & Abhishek RoyChowdhury & Sayanti Kar & Debdas Ray & Gupinath Bhandari, 2024. "Critical Watershed Prioritization through Multi-Criteria Decision-Making Techniques and Geographical Information System Integration for Watershed Management," Sustainability, MDPI, vol. 16(8), pages 1-25, April.
    19. 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.
    20. 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.

    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:jsusta:v:15:y:2023:i:1:p:861-:d:1023853. 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.