Abstract
The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as to construct a landslide susceptibility map. Genetic algorithms (GA) are used as a feature selection method, whereas the particle swarm optimization (PSO) method is used to optimize the structural parameters of two ML models, support vector machines (SVM) and artificial neural network (ANN). A well-defined spatial database, which included 335 landslides and twelve landslide-related variables (elevation, slope angle, slope aspect, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, distance to faults, distance to river, lithology, and hydrological cover) are considered for the analysis, in the Achaia Regional Unit located in Northern Peloponnese, Greece. The outcome of the study illustrates that both ML models have an excellent performance, with the SVM model achieving the highest learning accuracy (0.977 area under the receiver operating characteristic curve value (AUC)), followed by the ANN model (0.969). However, the ANN model shows the highest prediction accuracy (0.800 AUC), followed by the SVM (0.750 AUC) model. Overall, the proposed ML models highlights the necessity of feature selection and tuning procedures via evolutionary optimization algorithms and that such approaches could be successfully used for landslide susceptibility mapping as an alternative investigation tool.
Highlights
Landslides are natural disasters, the evolution of which has significant adverse consequences on the natural and build environment and in some cases, unfortunate human losses [1,2,3]
The results showed that the support vector machines (SVM) model had a slightly higher Area under the ROC curve (AUC) value (0.977) followed by the artificial neural network (ANN) model (0.969)
An advanced approach for landslide susceptibility mapping was developed that involved the usage of evolutionary algorithms for feature selection procedures and tuning the structural parameters of machine learning (ML) models
Summary
Landslides are natural disasters, the evolution of which has significant adverse consequences on the natural and build environment and in some cases, unfortunate human losses [1,2,3]. An increase in landslides has been reported worldwide, which is mainly associated with the impact of human activities and natural processes. It is well documented that favorable geo-environmental settings, climate change (the manifestation of extreme rainfall), human activities (the construction of new settlements and infrastructures in landslide-prone areas), and changes in land-use patterns increase the possibility of landslides, which is a trend that likely will continue in the future [3,4,5]. According to [6], the estimation of the landslide-prone areas, taking into account only their spatial component referred to as landslide susceptibility, is by far the most investigated topic and one of the most important components in a landslide risk study [7]. The prediction of landslides has been described mainly as a classification problem. The classification process is performed based on the complex and non-linear relation that exists between the spatial distribution of landslides and the landslide-related parameters [8,9]
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