# Landslide Susceptibility Assessment by Using Convolutional Neural Network
###### Abstract  
This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landslide triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landslide dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models. artificial intelligence; convolutional neural networks (CNN); deep-learning; susceptibility assessment; Gorzineh-khil region +
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developing methods and techniques to enable the faster and more efficient acquisition and processing of the geological/geomorphological data, which are both relevant in assessing landslide susceptibility that is mappable at an effective cost over wide regions [9; 10; 11; 12]. To this end, successful approaches that have been applied to susceptibility assessments can be formulated as deterministic methods (these methods are built on mathematical calculations, computational modelling, physical and geomechanical identification, mechanism recognition, slope failure stability and stabilisation), statistical methods (these methods are effective in accuracy and cover compiling, mapping, and digitising ground data by using statistical distributions, like bivariate, multivariate, and data-driven statistical applications), heuristic methods (these methods are based on comparing and qualitative estimation of actual landslide information with geomorphology and geological characteristics), inventory methods (these methods are based on a combination of landslide triggering factors, such as key functional procedure of quantitative predictions which are determined by stochastic, statistical, or probabilistic methods), geostatistical methods (these methods are used in natural phenomena modelling, missing values evaluation, gap covering, and reduced errors in statistical limitation and data filtrations), and knowledge-based analysis (these methods use computer modelling of a learning process for extracting patterns, and datasets) [13; 14; 15; 16; 17; 18; 19; 20]. Although each of the methods described above has advantages and disadvantages [21; 22], several methods, such as knowledge-based analysis by using computational intelligence for analyses, have always been much more suitable for susceptibility assessments and have marked milestones to accurately predict the slip potential [23]. The application of knowledge-based analysis to susceptibility assessment is growing significantly and more scholars have used artificial intelligence techniques, such as fuzzy logic, adaptive network-based fuzzy inference system (ANFIS), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF), logistic regression, multilayer perceptron (MLP), etc. [24; 25; 26; 27; 28]. Although the use of knowledge-based analysis in susceptibility assessment has expanded drastically, deep-learning methods, such as convolutional neural networks (CNN), have received less attention.