STATISTICAL METHODS TO ASSESS ROCKFALL SUSCEPTIBILITY IN AN ALPINE ENVIRONMENT: A FOCUS ON CLIMATIC FORCING AND GEOMECHANICAL VARIABLES
Tesi di Dottorato
Data di Pubblicazione:
2022
Citazione:
STATISTICAL METHODS TO ASSESS ROCKFALL SUSCEPTIBILITY IN AN ALPINE ENVIRONMENT: A FOCUS ON CLIMATIC FORCING AND GEOMECHANICAL VARIABLES / G. Bajni ; tutor: T. Apuani ; co-tutor: C.A.S. Camera ; coordinatore: M.I. Spalla, F. Camara Artigas. Dipartimento di Scienze della Terra Ardito Desio, 2022 Mar 23. 34. ciclo, Anno Accademico 2021. [10.13130/bajni-greta_phd2022-03-23].
Abstract:
The overarching goal of the doctoral thesis was thus the development of a systematic procedure capable to examine and enhance the role of geomechanical and climatic processes in rockfall susceptibility, performed with statistically based and Machine Learning techniques. To achieve this purpose, two case studies were analysed in the Italian Alps (Valchiavenna, Lombardy Region; Mountain Communities of Mont Cervin and Mont Emilius, Aosta Valley Region).
For both case studies, Generalized Additive Models (GAM) were used for rockfall susceptibility assessment; for the Valchiavenna case study, a Random Forest (RF) model was tested too. All models were validated through k-fold cross validation routines and their performance evaluated in terms of area under the receiver operating characteristic curve (AUROC). Predictors’ behaviour physical plausibility was verified through the analysis of the mathematical functions describing the predictors-susceptibility modelled relationships. Specific objectives of the two case studies differed.
The Valchiavenna case study was dedicated to testing the role of the outcrop-scale geomechanical properties in a rockfall susceptibility model. Specific objectives were: (i) the optimal selection of sampling points for the execution of geomechanical surveys to be integrated within an already available dataset; (ii) the regionalization over the study area of three geomechanical properties, namely Joint Volumetric Count (Jv), rock-mass weathering index (Wi) and rock-mass equivalent permeability (Keq); (iii) the implementation of the regionalized properties as predictors in a rockfall susceptibility model, along with the traditional morphometric variables; (iv) the investigation of prediction limitations related to inventory incompleteness; (v) the implementation of a methodology for the interpretation of predictors’ behaviour in the RF model, usually considered a black box algorithm; (vi) the integration of the RF and GAM outputs to furnish a spatially distributed measure of uncertainty; (vii) the exploitation of satellite-derived ground deformation data to verify susceptibility outputs and interpret them in an environmental management perspective.
The additional geomechanical sampling points were selected by means of the Spatial Simulated Annealing technique. Once collected the necessary geomechanical data, regionalization of the geomechanical target properties was carried out by comparing different deterministic, regressive and geostatistical techniques. The most suitable technique for each property was selected and geomechanical predictors were implemented in the susceptibility models. To verify rockfall inventory completeness related effects, the GAM model was performed both on rockfall data from the official landslide Italian inventory (IFFI) and on its updating with a field-mapped rockfall dataset. Regarding the RF model, the Shapely Additive exPlanations (SHAP) were employed for the interpretation of the predictors’ behaviour. A comparison between GAM and RF related outputs was carried out to verify their coherency, as well as a quantitative integration of the resulting susceptibility maps to reduce uncertainties. Finally, the rockfall susceptibility maps were coupled with Synthetic Aperture Radar (SAR) data from 2014 to 2021: a qualitative geomorphological verification of the outputs was performed, and composite maps were produced.
The key results were: (i) geomechanical predictor maps were obtained applying an ordinary kriging for Jv and Wi (NRMSE equal to 13.7% and 14.5%, respectively) and by means of Thin Plate Splines for Keq (NRMSE= 18.5%). (ii) Jv was the most important geomechanical predictor both in the GAM (witha deviance explained of 7.5%) and in the RF model, with a rockfall suscepti
Tipologia IRIS:
Tesi di dottorato
Keywords:
Rockfalls; Susceptibility; GAMs; Random Forest; Alps; Joint Volumetric Count; Weathering index; Equivalent permeability; Effective Water Inputs; Snow melting; Freeze-Thaw cycles; Wet and Dry cycles
Elenco autori:
G. Bajni
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