# Landslide Susceptibility Modeling Using a Deep Random Neural Network
###### Abstract  
Developing landslide susceptibility modeling is essential for detecting landslide-prone areas. Recently, deep learning theories and methods have been investigated in landslide modeling. However, their generalization is hindered because of the limited size of landslide data. In the present study, a novel deep learning-based landslide susceptibility assessment method named deep random neural network (DRNN) is proposed. In DRNN, a random mechanism is constructed to drop network layers and nodes randomly during landslide modeling. We take the Lushui area (Southwest China) as the case and select 12 landslide conditioning factors to perform landslide modeling. The performance evaluation results show that our method achieves desirable generalization performance (Kappa = 0.829) and outperforms other network models such as the convolution neural network (Kappa = 0.767), deep feedforward neural network (Kappa = 0.731), and Adaboost-based artificial neural network (Kappa = 0.732). Moreover, the robustness test shows the advantage of our DRNN, which is insensitive to variations in training data size. Our method yields an accuracy higher than 85% when the training data size stands at only 10%. The results demonstrate the effectiveness of the proposed landslide modeling method in enhancing generalization. The proposed DRNN produces accurate results in terms of delineating landslide-prone areas and shows promising applications. Keywords:landslide; deep learning; neural network; machine learning +
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the detailed mechanical process of slop sliding but rely on model structure and input data [4]. However, the relationship between landslide-triggering factors and landslides is complex, thereby complicating landslide susceptibility modeling. Numerous methods have been developed for landslide susceptibility modeling by exploring the relationship between landslides and their related factors. Early research mainly adopted statistics-based data models, including the grey model [5], exponential smoothing model [6], statistical index [7], frequency ratio [8], analytical hierarchy process [9], and weight of evidence [10]. These methods effectively deal with landslide prediction under simple situations or specific conditions. However, they have insufficient accuracies in complex and variable patterns. Given the rapid development of computer techniques, machine learning methods have been widely investigated for LSM. Commonly used machine learning methods include the maximum entropy [11], artificial neural network (ANN) [12], Bayesian network [13], support vector machine (SVM) [14], kernel logistic regression [15], boosted regression tree [16], and logistic model tree [17]. These algorithms treat landslide-influencing factors as independent variables to build the non-linear relationship between factors and landslides. Besides the landslide spatial prediction, the success of machine learning is exhibited in other geotechnical engineering problems, such as the prediction of the soil compression index [18], flood vulnerability analysis [19], and earthquake early warning [20]. However, single machine learning methods tend to suffer from local optimization problems due to the limited hypothesis space [21]. Therefore, scholars have explored ensemble learning methods for LSM [22]. Many ensemble algorithms have been developed and proposed for LSM, such as Bagging [23], Boosting [24], random forest (RF) [25], rotation forest [26], stacking [27], ANN-Bayes analysis [28], GWO-SVM [29], and random subspace-based naive Bayes tree [30]. Ensemble methods can expand the hypothesis space of models to search for the optimal solution; thus, their performance is better than that of single models [31; 32]. Recently, deep learning methods have achieved state-of-the-art results in LSM studies [33; 34; 35]. The existing deep learning-based LSM techniques are mainly inspired by the deep feedforward neural network (DNN) [36], recurrent neural network [37], convolution neural network (CNN) [38], and auto-encoder [39] (Table 1).