Application of statistical analysis to estimate the costal hazard. A case study in Liguria region

Liguria Region is totally exposed to the action of the sea storms and too the natural evolution of the profile of the shoreline. The modification along the time of the shape of the shoreface is measured from the official administrative and technical offices of the Liguria Region and Italian Environmental Ministry, this information is available in shape format starting from 1944. The phenomenon of coastal flood produces a direct damage represented by the loss of soil and an indirect damage correlated to the impact on tourism activity, social aspects, and damage to heritage buildings. In recent years another type of damage source must be considered, and this is the phenomenon of the increasing of the mean sea water level, known as Sea Level Rise (SLR). It is necessary to introduce this phenomenon in the hazard analysis and this is a direct and known consequence of the climate change. Results from the hazard index encompass both the relative magnitude of erosion and/or coastal flooding, and the probability that these hazards may occur based on the distribution of the index using different scenarios. The paper analyzes a Liguria case study in which the effects of SLR is particularly critical in terms of heritage and economic and social activities hazar

Remote Sensing-Based Methodology for the Quick Update of the Assessment of the Population Exposed to Natural Hazards

The assessment of the number of people exposed to natural hazards, especially in countries with strong urban growth, is difficult to be updated at the same rate as land use develops. This paper presents a remote sensing-based procedure for quickly updating the assessment of the population exposed to natural hazards. A relationship between satellite nightlights intensity and urbanization density from global available cartography is first assessed when all data are available. This is used to extrapolate urbanization data at different time steps, updating exposure each time new nightlights intensity maps are available. To test the reliability of the proposed methodology, the number of people exposed to riverine flood in Italy is assessed, deriving a probabilistic relationship between DMSP nightlights intensity and urbanization density from the GUF database for the year 2011. People exposed to riverine flood are assessed crossing the population distributed on the derived urbanization density with flood hazard zones provided by ISPRA. The validation against reliable exposures derived from ISTAT data shows good agreement. The possibility to update exposure maps with a higher refresh rate makes this approach particularly suitable for applications in developing countries, where urbanization and population densities may change at a sub-yearly time scale

ANALYSIS OF THE SPATIAL STRUCTURE OF THE 4 OCTOBER 2021 EXTREME RAINFALL EVENT IN LIGURIA AND EVALUATION OF ITS IMPACT ON THE ESTIMATION OF ANNUAL MAXIMA

The paper shows the analysis of the spatial scale of rainfall produced by a back-building Mesoscale Convective System. • The analysis is conducted combining rain gauge and radar observations. • The results show that the spatial scale is of the same order of (or lower than) the density of rain gauge networks. • This may significantly impact the estimation of the actual rainfall maxima and their return period

Waste Management under Emergency Conditions: Life-Cycle Multicriteria Analysis as Decision Support System

Waste management under emergency conditions requires proper handling. The sudden closure of a strategic final disposal site can result in serious environmental and health hazards which need to be addressed. Furthermore, this situation requires the identification of new sites to be used for waste disposal. This study analysed the case-study of Genoa, Northern Italy, following the closure of the Scarpino landfill previously dedicated to the disposal of waste generated in this municipality. A multi-objective tool was developed and applied from long-term planning to day-to-day scheduling. A sensitivity analysis was performed on the basis of collected waste volumes, in order to study the utilization and actual rate of fulfilling of the plants according to the leading objective function. Considering all of the objective functions, the emissions optimization shows better behaviour in terms of simultaneous global accomplishment of each function. In this context, the introduction of a decision support system for waste management shows its usefulness in setting and effectively pursuing long-term targets in term of total costs, emissions generated by waste transport, and exploitation of single plants from a sustainability perspective

UNA METODOLOGIA BASATA SULL\u2019USO DI DATI SATELLITARI PER LA STIMA DELLA POPOLAZIONE ESPOSTA AL RISCHIO D\u2019INONDAZIONE/ A REMOTE-SENSING BASED METHODOLOGY FOR THE ESTIMATION OF POPULATION IN FLOOD PRONE AREAS

This work presents a general and simple remote sensing-based procedure for the assessment of the popula- tion exposed to riverine flood, which allows to (i) redistribute the population provided by census data within administrative/census units; (ii) quickly update flood exposure maps with a high refresh rate, making this approach particularly suitable where urbanization and population densities may change at a sub-yearly ti- me scale. The paper explores the possibility to calibrate reliable relationships between satellite nightlights intensity and urbanization density from global available cartography to update population exposure maps each time new nightlights intensity maps are ready for use. Italy is used as a test case for nightlight inten- sity/urban density curves calibration and validation

Supervised and semi-supervised classifiers for the detection of flood-prone areas

Supervised and semi-supervised machine-learning techniques are applied and compared for the recognition of the flood hazard. The learning goal consists in distinguishing between flood-exposed and marginal-risk areas. Kernel-based binary classifiers using six quantitative morphological features, derived from data stored in digital elevation models, are trained to model the relationship between morphology and the flood hazard. According to the experimental outcomes, such classifiers are appropriate tools when one is interested in performing an initial low-cost detection of flood-exposed areas, to be possibly refined in successive steps by more time-consuming and costly investigations by experts. The use of these automatic classification techniques is valuable, e.g., in insurance applications, where one is interested in estimating the flood hazard of areas for which limited labeled information is available. The proposed machine-learning techniques are applied to the basin of the Italian Tanaro River. The experimental results show that for this case study, semi-supervised methods outperform supervised ones when—the number of labeled examples being the same for the two cases—only a few labeled examples are used, together with a much larger number of unsupervised ones