Landslide susceptibility modelling using integrated application of computational intelligence in Ahar County, Iran

Subject Areas : journal of Artificial Intelligence in Electrical Engineering

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

1 - Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2 - 1-Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran 2-Dep. IT. Faculty of Electrical and Computer Eng. Univ of Tabriz
3 - 1-Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran 2-Department of Remote sensing and GIS, University of Tabriz, Tabriz, Iran
4 - Department of IT and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
5 - Department of Computer Engineering, University College of Nabi Akram, Tabriz, Iran

Received: 2022-03-13 Accepted : 2022-04-06 Published : 2020-09-01

Keywords: Support vector machine, Random forest, Multiple Layer Neural Network,

Abstract :

Landslide susceptibility analysis is beneficial information for a wide range of applications. We aimed to explore and compare three machine learning (ML) techniques, namely the random forests (RF), support vector machine (SVM) and multiple layer neural networks (MLP) for landslide susceptibility assessment in the Ahar county of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 266 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. Three landslide models (SVM, RF, and MLP) were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were employed to validate and contrast the predictive capability of the obtained three 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 MLP model had the utmost rate of prediction capability (85.22 %), after which, the SVM model (78.26 %) and the RF model (75.22 %) demonstrated the second and third rates. Besides, the study revealed that benefiting the optimal machine with the proper selection of the techniques could facilitate landslide susceptibility modeling.