This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements, processes whose intermittent activity makes challenging any risk analysis worldwide. MLAs has been trained on datasets including Interferometric Synthetic Aperture Radar (InSAR) and additional remote sensing datasets such as aerial stereo photographs and LiDAR and tested in the Termini-Nerano landslides system (southern Apennines, Italy). The availability of such a wealth of materials allows also an unprecedented spatio-temporal reconstruction of the volume and the kinematic of the landslides system through which we could generate and validate the hazard map. Our analysis identifies fifteen slow-moving phenomena, traceable since 1955, whose total area amounts to 4.1 × 105 m2 and volume to ~1.4 × 106 m3. InSAR results prove that seven out of the fifteen slow-moving landslides are currently active and characterized by seasonal velocity patterns. These new insights on the dynamic of the landslides system have been selected as the main independent variables to train three MLAs (Artificial Neural Network, Generalized Boosting Model and Maximum Entropy) and derive the landslide hazard for the area. Finally, official population and buildings census data have been used to assess the landslide risk whose highest values are located in the crown area, south of Termini village, and nearby Nerano. This new methodology provides a different landslide risk scenario compared to the existing official documents for the study area and overall new insights on how to develop landslide risk management strategies worldwide based on a better understanding of slope processes thanks to the latest satellite technologies available.

Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques / Novellino, A.; Cesarano, M.; Cappelletti, P.; Di Martire, D.; Di Napoli, M.; Ramondini, M.; Sowter, A.; Calcaterra, D.. - In: CATENA. - ISSN 0341-8162. - 203:(2021). [10.1016/j.catena.2021.105317]

Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques

A. Novellino
Primo
;
P. Cappelletti;D. Di Martire;M. Di Napoli;M. Ramondini
Penultimo
;
D. Calcaterra
Ultimo
2021

Abstract

This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements, processes whose intermittent activity makes challenging any risk analysis worldwide. MLAs has been trained on datasets including Interferometric Synthetic Aperture Radar (InSAR) and additional remote sensing datasets such as aerial stereo photographs and LiDAR and tested in the Termini-Nerano landslides system (southern Apennines, Italy). The availability of such a wealth of materials allows also an unprecedented spatio-temporal reconstruction of the volume and the kinematic of the landslides system through which we could generate and validate the hazard map. Our analysis identifies fifteen slow-moving phenomena, traceable since 1955, whose total area amounts to 4.1 × 105 m2 and volume to ~1.4 × 106 m3. InSAR results prove that seven out of the fifteen slow-moving landslides are currently active and characterized by seasonal velocity patterns. These new insights on the dynamic of the landslides system have been selected as the main independent variables to train three MLAs (Artificial Neural Network, Generalized Boosting Model and Maximum Entropy) and derive the landslide hazard for the area. Finally, official population and buildings census data have been used to assess the landslide risk whose highest values are located in the crown area, south of Termini village, and nearby Nerano. This new methodology provides a different landslide risk scenario compared to the existing official documents for the study area and overall new insights on how to develop landslide risk management strategies worldwide based on a better understanding of slope processes thanks to the latest satellite technologies available.
2021
Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques / Novellino, A.; Cesarano, M.; Cappelletti, P.; Di Martire, D.; Di Napoli, M.; Ramondini, M.; Sowter, A.; Calcaterra, D.. - In: CATENA. - ISSN 0341-8162. - 203:(2021). [10.1016/j.catena.2021.105317]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/849167
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