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Stacking ensemble approach in data mining methods for landslide prediction

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Abstract

Landslide susceptibility mapping is still an ongoing requirement for variety of applications such as land use management plans. The central objective of the present research was to investigate the effect of using ensemble machine learning methods for developing accurate landslide prediction. We aimed to explore and compare three techniques, namely the random forests, support vector machine and multiple-layer neural networks with an adaptive neuro-fuzzy inference system, which incorporates three metaheuristic methods including grey wolf optimization, particle swarm optimization, and shuffled frog leaping algorithm for landslide susceptibility assessment in the East Azerbaijan of Iran. Also, two ensemble ways (voting and stacking) were used in final decision stage. A sum of 766 locations with landslide inventory was recognized in the context of the study. Then the all models were trained using tenfold cross-validation technique. Lastly, the receiver operating characteristic and statistical procedures were employed to validate and contrast the predictive capability of the models. The findings of the study show the ANFIS–PSO model had high performance on the validation dataset (AUC = 0.89). Besides, the study revealed that using stacking ensemble technique could increase the predicting capability of all models (AUC = 0.911).

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Data availability

The datasets generated during and/or analyzed during the current study and source codes are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank the Iranian Department of Water Resources Management (IDWRM), Iranian Statistical Institute (ISI), and Meteorological Organization (MetO) for providing whole investigation reports. We are grateful to all those who helped us for their expert comments.

Funding

No funds, grants, or other support was received.

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Authors and Affiliations

  1. Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, 57169-63896, Iran

    Solmaz Abdollahizad, Mohammad Ali Balafar, Bakhtiar Feizizadeh & Amin Babazadeh Sangar

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, 51666-16471, Iran

    Mohammad Ali Balafar

  3. Department of Remote Sensing and GIS, University of Tabriz, Tabriz, 57169-63896, Iran

    Bakhtiar Feizizadeh

  4. Department of Computer Engineering, University College of Nabi Akram, Tabriz, 51839-18993, Iran

    Karim Samadzamini

Authors
  1. Solmaz Abdollahizad
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  2. Mohammad Ali Balafar
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  3. Bakhtiar Feizizadeh
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  4. Amin Babazadeh Sangar
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  5. Karim Samadzamini
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Contributions

Conceptualization, SA; methodology, SA; software, SA, MB; validation, BF; formal analysis, ABS; investigation, BF; resources, KS; data curation, SA; writing—original draft preparation, KS; writing—review and editing, SA, MB and KS; visualization, ABS; supervision, BF; project administration, SA All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mohammad Ali Balafar.

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Abdollahizad, S., Balafar, M.A., Feizizadeh, B. et al. Stacking ensemble approach in data mining methods for landslide prediction. J Supercomput 79, 8583–8610 (2023). https://doi.org/10.1007/s11227-022-05006-0

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  • DOI: https://doi.org/10.1007/s11227-022-05006-0

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