## 4 Results and Discussion
### Performance of the MLP-NN and RNN  
The models were implemented in Python using the open source TensorFlow deep learning framework developed by Google [26]. Meanwhile, the accuracy of the proposed NN models was tested using a 10-fold cross-validation method. The results are presented in Table 5. The best accuracy of 83.33% in the analysis area was achieved by the RNN model. The MLR-NN model achieved an accuracy of 78.38% in the same area. Furthermore, the RNN model outperformed the MLR-NN model in terms of stability of accuracy across different folds of the tested dataset. In the test area, accuracies of 81.11% and 74.56% were achieved with the RNN and MLP-NN models, respectively. These results indicated that the RNN model has better accuracy than the MLR-NN model in the analysis and test areas and indicated the high stability of the RNN model in detecting the spatial distribution of landslides.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Iteration** & **Rank** \\ \hline StdDe DTM & 20 & 1 \\ GLCM homogeneity & 18 & 2 \\ Mean slope & 20 & 3 \\ GLCM angular second moment & 20 & 4 \\ Mean intensity & 17 & 5 \\ Mean red & 20 & 6 \\ Mean DTM & 20 & 7 \\ GLCM contrast & 18 & 8 \\ GLCM dissimilarity & 15 & 9 \\ StdDev blue & 20 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation-based feature selection (CFS) results for the most relevant feature subset at a scale of 75.52; StdDe: Standard deviation, DTM: Digital terrain model, GLCM: Gray level co-occurrence matrix.  
However, producing neural network models such as LSTM and convolution layers with fully connected networks is a crucial task. Complex networks with more hidden units and many modules often tend to have a better overfit due to the detection ability with respect to any possible interaction so the model becomes too specific to the training dataset. Thus, optimizing the network structures is very crucial for avoiding over-fitting. This study indicated that the hyperparameters in both models have a significant effect on their results. For example, the effect of learning rate varied from 0.1, 0.01, 0.05 and 0.001 in landslide detection. The highest accuracy was obtained when a learning rate reached 0.001. In contrast, increasing the learning rate to 0.1 significantly reduced the accuracy in both models. The batch size parameter in both models had significant effects on the result accuracy. The results of MLR-NN and RNN models showed high accuracies with batch sizes of 64 and 128, respectively. This indicates that RNN model achieved high accuracy with the increase of the batch size, whereas the accuracy of MLB-NN model was decreased.  
Furthermore, it was revealed that the dropout rate had a substantial influence on the results of the RNN model. The RNN model showed higher accuracy when the dropout rate reached 0.6. The results of the RNN model indicated that the accuracy increased when the dropout rate parameter was increased.  
The results of two models (Table 5), show that the accuracies of the RNN model outperformed the MLP-NN model in both study areas. This is due to several reasons, for example the fact that the MLP-NN model uses only local contexts and therefore it does not capture the temporal and spatial correlation in the dataset. Meanwhile, the hidden units of the RNN model contain historical information from the previous step. This indicates RNN model has more information about the data structure and accurate as compared to the MLP-NN model.