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Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran

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A Correction to this article was published on 14 September 2021

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Abstract

Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide susceptibility mapping framework that involves an adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods including grey wolf optimization (GWO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA) in the East Azerbaijan of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 766 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. In the next phase, three data mining techniques were united with the ANFIS model, comprising ANFIS-grey wolf algorithm (ANFIS-GW), ANFIS-particle swarm optimization (ANFIS-PSO), and ANFIS-shuffled frog leaping algorithm (ANFIS-SFLA), were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were utilized with the aim of validating and contrasting the predictive capability of the models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the ANFIS-PSO model had high performance on both the training (RMSE = 0.288, MAE = 0.069, AUC = 0.89) and validation dataset (RMSE = 0.309, MAE = 0.097, AUC = 0.89), after which, the ANFIS-GWO model and the ANFIS-SFLA model demonstrated the second and third rates. Besides, the study revealed that benefiting the optimization algorithm with the proper selection of the techniques could facilitate landslide susceptibility modeling.

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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.

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Authors and Affiliations

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

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

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

    Mohammad Ali Balafar

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

    Bakhtiar Feizizadeh

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

    Karim Samadzamini

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  1. Solmaz Abdollahizad
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Abdollahizad, S., Balafar, M.A., Feizizadeh, B. et al. Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran. Earth Sci Inform 14, 1861–1882 (2021). https://doi.org/10.1007/s12145-021-00644-z

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