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Using the integrated application of computational intelligence for landslide susceptibility modeling in East Azerbaijan Province, Iran

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Abstract

Mapping of landslide susceptibility is an important tool to prevent and control landslide disasters for a variety of applications, such as land use management plans. The main objective of this study was to propose an application of artificial intelligence systems, then evaluate and compare their efficiency for developing accurate landslide susceptibility mapping (LSM). The present study aims to explore and compare the frequency ratio (FR) method with three machine learning (ML) techniques, namely, random forests (RF), support vector machines (SVM), and multiple layer neural networks (MLP), for landslide susceptibility assessment in East Azerbaijan, Iran. To achieve this goal, 20 landslide-occurrence-related influencing factors were considered. A sum of 766 locations with landslide inventory was recognized in the context of the study, and the relief-F method was utilized in order to measure the conditioning factors’ prediction capacity in landslide models. In the forthcoming phase, three ML models (SVM, RF, and MLP) were trained by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were employed to validate and contrast the predictive capability of the FR model with the obtained three models. The findings of the study in terms of the relief-F method for the importance ranking of conditioning factors in the context area uncovered those eleven factors, such as slope, aspect, normalized difference vegetation index (NDVI), and elevation, have the highest impact on the occurrence of the landslide. The results show that the MLP model had the utmost rate of landslide spatial prediction capability (87.06%), after which the SVM model (80.0%), the RF model (76.67%), and the FR model (61.25%) demonstrated the second, third, and fourth rates. Besides, the study revealed that benefiting the optimal machine with the proper selection of the techniques could facilitate landslide susceptibility modeling.

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Data Availability

The datasets generated during and/or analyzed during the current study 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), the Iranian Statistical Institute (ISI), and the Meteorological Organization (MetO) for providing whole investigation reports. We are grateful to all those who helped us with their expert comments.

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Authors and Affiliations

  1. Department of Computer Engineering, Sardroud Center, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Solmaz Abdollahizad

  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 IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, 57169-63896, Iran

    Amin Babazadeh Sangar

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

    Karim Samadzamini

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  1. Solmaz Abdollahizad
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Abdollahizad, S., Balafar, M.A., Feizizadeh, B. et al. Using the integrated application of computational intelligence for landslide susceptibility modeling in East Azerbaijan Province, Iran. Appl Geomat 15, 109–125 (2023). https://doi.org/10.1007/s12518-023-00488-w

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