Landslide risk management through spatial analysis and stochastic prediction for territorial resilience evaluation

Natural materials, such as soils, are influenced by many factors acting during their formative and evolutionary process: atmospheric agents, erosion and transport phenomena, sedimentation conditions that give soil properties a non-reducible randomness by using sophisticated survey techniques and technologies. This character is reflected not only in spatial variability of properties which differs from point to point, but also in multivariate correlation as a function of reciprocal distance. Cognitive enrichment, offered by the response of soils associated with their intrinsic spatial variability, implies an increase in the evaluative capacity of the contributing causes and potential effects in failure phenomena. Stability analysis of natural slopes is well suited to stochastic treatment of uncertainty which characterized landslide risk. In particular, this study has been applied through a back- analysis procedure to a slope located in Southern Italy that was subject to repeated phenomena of hydrogeological instability (extended for several kilometres in recent years). The back-analysis has been carried out by applying spatial analysis to the controlling factors as well as quantifying the hydrogeological hazard through unbiased estimators. A natural phenomenon, defined as stochastic process characterized by mutually interacting spatial variables, has led to identify the most critical areas, giving reliability to the scenarios and improving the forecasting content. Moreover, the phenomenological characterization allows the optimization of the risk levels to the wide territory involved, supporting decision-making process for intervention priorities as well as the effective allocation of the available resources in social, environmental and economic contexts

Detailed and large-scale cost/benefit analyses of landslide prevention vs. post-event actions

The main aim of this paper is to test economic benefits of landslide prevention measures vs. post-event emergency actions. To this end, detailed- and large-scale analyses were performed in a training area located in the northeastern Italian pre-Alps that was hit by an exceptional rainfall event occurred in November 2010. On the detailed scale, a landslide reactivated after 2010 event was investigated. Numerical modeling demonstrated that remedial works carried out after the landslide – water-removal intervention such as a drainage trench – could have improved slope stability if applied before its occurrence. Then, a cost/benefit analysis was employed. It defined that prevention would have been economically convenient compared to a non-preventive and passive attitude, allowing a 30 % saving relative to total costs. On the large scale, one of the most affected areas after 2010 event was considered. A susceptibility analysis was performed using a simple probabilistic model, which allowed to highlight the main landslide conditioning factors and the most hazardous and vulnerable sectors. In particular, such low-cost analysis demonstrated that almost 50 % of landslides occurred after 2010 event could be foreseen and allowed to roughly quantify benefits from regional landslide prevention. However, a large-scale approach is insufficient to carry out a quantitative cost/benefit analysis, for which a detailed case-by-case risk assessment is needed. The here proposed approaches could be used as a means of preventive soil protection in not only the investigated case study but also all those hazardous areas where preventive measures are needed

Debris flow susceptibility mapping using the Rock Engineering System (RES) method: a case study

The main purpose of the present study is to develop a debris flow susceptibility map of a mountain area (Susa Valley, Western Italian Alps) by using an upgraded version of the Bonetto et al. (Journal of Mountain Science 18, 2021) approach based on the Rock Engineering System (RES) method. In particular, the area under investigation was discretized in a 5 × 5-m grid on which GIS based analyses were performed. Starting from available databases, several geological, geo-structural, morphological and hydrographical predisposing parameters were identified and codified into two interaction matrices (one for outcropping lithologies and one for Quaternary deposits), to evaluate their mutual interactions and their weight in the susceptibility estimation. The result for each grid point is the debris flow propensity index (DfPI), an index that estimates the susceptibility of the cell to be a potential debris flow source. The debris flow susceptibility map obtained was compared with those obtained from two expedited and universally recognized susceptibility methods, i.e. the Regional Qualitative Heuristic Susceptibility Mapping (RQHSM) and the Likelihood Ratio (LR). Each map was validated by using the Prediction Rate Curve method. The limitations and strong points of the approaches analysed are discussed, with a focus on the innovativeness and uniqueness of the RES. In fact, in the study site, the RES method was the most efficient for the detection of potential source areas. These results prove its robustness, cost-effectiveness and speed of application in the identification and mapping of sectors capable of triggering debris flow

A novel rule-based approach in mapping landslide susceptibility

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes

Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.JRC.H.5-Land Resources Managemen