Abstract
Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The aim of this work is to apply the Supervised ML technique to train a model able to classify a particular gravity-driven coastal hillslope geomorphic model (slope-over-wall) involving most of the soft rocks of Cilento (southern Italy). To train the model, only geometric data have been used, namely morphometric feature maps computed on a Digital Terrain Model (DTM) derived from Light Detection and Ranging (LiDAR) data. Morphometric maps were computed using third-order polynomials, so as to obtain products that best describe landforms. Not all morphometric parameters from literature were used to train the model, the most significant ones were chosen by applying the Neighborhood Component Analysis (NCA) method. Different models were trained and the main indicators derived from the confusion matrices were compared. The best results were obtained using the Weighted k-NN model (accuracy score = 75%). Analysis of the Receiver Operating Characteristic (ROC) curves also shows that the discriminating capacity of the test reached percentages higher than 95%. The model, resulting more accurate in the training area, will be extended to similar areas along the Tyrrhenian coastal land.
Highlights
Landslide-prone areas’ identification and classification play an important role in land assessment, planning and management
The authors introduced an innovative method based on the combination of data coming from the InSAR technique and Ensemble Modeling (EM); again, the results show that the variables that most influence the process for the determination of landslide risk are the geometric ones derived from remotely sensed data
The Light Detection and Ranging (LiDAR) point cloud was filtered before interpolating the Digital Terrain Models (DTM), using the Multiscale Curvature Classification (MCC) filtering algorithm, according to the procedure described in detail in [6]
Summary
Landslide-prone areas’ identification and classification play an important role in land assessment, planning and management. They are usually performed using a supervised approach, either through direct geomorphological analysis [1] or through visual interpretation of optical images, e.g., satellite (panchromatic, multispectral) or from Unmanned. Radar or Synthetic Aperture Radar (SAR) imagery is used to detect acting deformation; interferometric techniques allow the derivation of multi-temporal surface deformation maps with high accuracy and spatial resolution [7]. Automated or semi-automated methods for landslide identification based on remote sensing techniques have been studied greatly in recent years; several automated techniques for landslide susceptibility mapping have been proposed in the past two decades [8,9]. The exponential increase in the number of suggested methods is related to continued advances in computer technology, including the development of increasingly high-performance algorithms and computing processors and increased storage capacity
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