# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 4 Results and Discussion  
### Results  
To provide landslide susceptibility mapping with high accuracy, machine learning and deep neural network have successfully been implemented. The application of such networks requires preparing a dataset from triggering factors and historical landslides, which is used to develop proper zonation maps. The prepared dataset forms the landslide triggering factors, and historical landslides were used for training and evaluating the predictive capability of the landslide occurrence in the regions. These parameters, which are mentioned in Table 1, were utilized to produce a 15-layered CNN-based framework and to predict the sliding prone areas. The applied CNN-based landslide prediction algorithm trained the 80% overall landslide-prone dataset where each of the pixelated grid cells was used for susceptibility assessment. To achieve accurate results, the trained estimated grid cells were used for pixel intensity evaluations. The result of the evaluation is presented in Figure 9. After training the dataset, the remaining 20% was tested. The ratio of the test to train is important for the model's learning rate, which is a response to the estimated error each time the model weights are updated. In fact, the learning rate controls how quickly the model adapts to the problem. Smaller learning rates require more training epochs, whereas larger learning rates result in rapid changes and require fewer training epochs. Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. This rate is used to evaluate the overall accuracy and models' performance. Figure 9 provides the pre-processing to filter out feature redundancy and to ensure the overall performance consistency of the CNN model to aid in increasing the accuracy and detectability of the images. After conducting the pre-processing on the training datasets, which plays a crucial role in improving the performance of the CNN model, the model must be quantified so that the test dataset learns as a trained set for predictive detection. Figure 10 presents the results of the overlaying convolution implementation test and trained relevance maps. A comparison of the pixelated classifications (i.e., TP, FP, TN, and FN) according to Table 3 is used for detecting the corrected classification and recognition of the landslide susceptibility indices. A loss function has been estimated for the training-test description. Figure 11 provides the diagrams of training and validation based on the confusion matrix for the CNN model. Regarding this figure, the evaluation parameters (accuracy, precision, and recall) are estimated as the basic matrix of performance for the learning model. Also, Figure 12 illustrates the loss model. Referring to Figures 11 and 12, the estimated loss for the model performance is less than 0.02 (average value) over 1750 epochs, which is considerable. The estimated accuracy for the model is 0.79 (average value), precision is 0.73 (average value), and recall is 0.75 (average value). Figure 13 presents the landslide susceptibility map. Due to the landslide susceptibility assessment results, the main focus of the landslide sliding hazard is striped in high elevations in the middle and north-northwest parts of the study region, where the landslide susceptibility is classified into 5 classes, as obtained from the CNN model, and the performance matrix results, as indicated in Figure 14. By considering the estimated loss function for the CNN model, it can be clearly stated that the evaluation error rate has been reduced to 0.02 for the training dataset and to 0.01 for the test dataset in 1750 epochs. This indicates the high accuracy of the calculations. Based on Figure 14, it can be  
Figure 10: The overlaying results of convolution implementation (CNN-based relevance maps) to estimate the region landslides susceptibility (**a**) CNN class 1, (**b**) CNN class 2, (**c**) CNN class 3, (**d**) CNN class 4.