# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 5 Conclusions  
Landslides are complicated geological events that are controlled by many variables such as topographic, geologic, climatologic, seismic, human activity-related factors, etc., which are associated with numerous uncertainties in susceptibility evaluations. In this regard, many procedures are used in susceptibility assessments, which are mainly classified as quantitative and qualitative methods. In the meantime, quantitative procedures, especially knowledge-based methods (due to their ability to cover more variables), have gained importance nowadays. This study presents an application of the convolutional neural network (CNN) as one of the recent concepts of knowledge-based methodology to landslide susceptibility assessment in the Gorzineh-khil region located in the Savadkooh county, Mazandaran province, northeast of Iran. The study followed three steps for landslide hazard zonation and for providing hazard potential maps. In the first step, by using satellite images, DEM data, and a field survey, the landslide influencing (triggering) factors were identified. In Step Two, the dataset was provided and a CNN model to extract the relevant features was performed. In Step three, verification and justification of the model by using a loss function and regular machine-learning classifiers (e.g., SVM, k-NN, and DT) were performed.  
As a result of the study, the topographic factor has been taken into account as the main triggering factor, and the others were considered as triggering factors. This factor alone would not be enough to lead to a landslide occurrence, but it has had a significant effect on land movements in Gorzineh-khil. Topography also influences other parameters such as vegetation, wind, sunlight, and freezing, which are directly related to weathering.  
The 15-layer convolutional neural network model was used to classify the hazard potential of the studied region into five susceptibility classes to prepare the hazard potential map of the Gorzineh-khil region. According to the results, the main focus of the landslide sliding hazard is striped in high elevations in the middle and north-northwest parts of the studied region.  
For estimating the CNN model's performance, the model was verified by a loss function and justified by SVM, k-NN, and DT. Based on the loss function, it can be stated that the evaluation error rate has been reduced to 0.02 for the training dataset and to 0.033 for the test dataset in 200 epochs and performance evaluations for the susceptibility assessment. The CNN, with 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, was capable of providing results with better accuracy as compared to the other models with the least computational error.  
S.N. and M.A.: writing--original draft preparation, methodology, conceptualization, investigation, resources, validation, visualization, formal analysis; H.A.: validation, visualization, supervision, writing--review and editing; R.D.: supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.