Comparing the performance of Machine Learning Methods in landslide susceptibility modelling

Landslide phenomena are considered as one of the most significant geohazards with a great impact on the man-made and natural environment. If one search the scientific literature, the most studied topic in landslide assessments is the identification of areas that potentially may exhibit instability issues by modelling the influence of landslide-related variables with methods and techniques from the domain of knowledge and data-driven approaches. This is not an easy task, since the complexity, and in most cases the unknown processes that are responsible for the evolution of landslide phenomena triggered either of natural or man-made activities, influence their performance. Landslide susceptibility assessments, which models the spatial component of the evolution of landslides are the most reliable investigation tool capable of predicting the spatial dimension of the phenomenon with high accuracy. During the past two decades, artificial intelligence methods and specifically machine learning algorithms have dominated landslide susceptibility assessments, as the main sophisticated methods of analysis. Fuzzy logic algorithms, decision trees, artificial neural networks, ensemble methods and evolutionary population-based algorithms were among the most advanced methods that proved to be reliable and accurate.

In this context, the main objective of the present study was to compare the performance of various Machine Learning models (MLm) in landslide susceptibility assessments. Concerning the followed methodology, it could be separated into a five-phase procedure: (i) creating the inventory map, (ii) selecting, classifying, and weighting the landslide-related variables, (iii) performing a multicollinearity, an importance analysis (iv) implementing the developed methodology and testing the produced models, and (v) comparing the predictive performance of the various models. The computational process was carried out coding in R and Python language, whereas ArcGIS 10.5 was used for compiling the data and producing the landslide susceptibility maps.

In more details, Logistic Regression, Support Vector Machines, Random Forest, and Artificial Neural Network were implemented, and their predictive performance were compared. The efficiency of the MLM was estimated for an area of northwestern Peloponnese region, Greece, an area characterized by the presence of numerous landslide phenomena. Twelve landslide-related variables, elevation, slope angle, aspect, plan and profile curvature, topographic wetness index, lithology, silt, sand and clay content, distance to faults, distance to river network and 128 landslide locations, were used to produce the training and test datasets. The Certainty Factor was implemented to calculate the correlation among the landslide-related variables and to assign to each variable class a weight value. Multi-collinearity analysis was used to estimate the existence of collinearity among the landslide related variables. Learning Vector Quantization (LVQ) was used for ranking features by importance, whereas the evaluation process involved estimating the predictive ability of the MLm via the classification accuracy, the sensitivity, the specificity and the area under the success and predictive rate curves (AUC). Overall, the outcome of the study indicates that all MLm provided high accurate results with the Artificial Neural Network approach being the most accurate followed by Random Forest, Support Vector Machines and Logistic Regression.