Search

The recent upsurge in the incidence of extreme wildfire events, the expected impact of climate change on the frequency and severity of fires, and the progressive expansion of wildland-urban interface areas highlight the tangible need for improvement in our ability to predict, mitigate and manage the growing risk to which communities are exposed. The aim of this research is to contribute to deepen the knowledge on the spatial simulation of complex dynamics of wildland surface fire behaviour through the development and validation of a spatially distributed predictive model for the simulation of wildland surface fire spread intended for operational purposes. Given the position of one or more ignition points, the developed model allows to (i) obtain near real time dynamic estimates of the geo-environmental variables that control the fire spread, (ii) compute the direction and intensity of the maximum rate of fire spread in heterogeneous environments, and (iii) simulate the surface fire spread using agent-based models. The final aim is to provide competent authorities with timely information on the expected evolution of the flame front to optimise decision-making processes. The model, developed under synthetic conditions, is then applied to case studies recorded in the territory of the Autonomous Region of Sardinia, that offers institutional information on the ignition location, the evolution of the flame front, and the completed fire suppression activities, which are implemented in the model as well. Overall, the model showed a promising predictive capacity evaluated in quantitative terms of morphological matching between the observed and predicted fire spread patterns, returning more accurate results in areas with less complex morphologies and dominated by herbaceous rather than shrubby fuels. The model also made it possible to obtain accurate simulations in rapid processing times, compatible with its operational application as a tool for optimising and planning fire risk prevention and mitigation strategies and policies as well as fire management activities. Future research will be addressed at improving the predictive capacity of the model and estimating the propagation of the parametric uncertainty of the geo-environmental variables within the model.

“Mappa Italia” is a WebGIS service, issued by ANIA Servizi (Associazione Nazionale fra le Imprese Assicuratrici), conceived to support the business activity of insurance companies. It returns a “Risk Index” for the entire Italian territory by analyzing national institutional geo-hazard maps and the physical exposure of the assets located in each terrain unit used in this study. The analysis has been performed by processing data from census tracts, land use map and flood and landslide hazard maps. Census tracts provides data about population, buildings (residential, commercial and industrial) and firms (number of companies and employees). Data about land use (urban and rural areas), have been extracted from the Corine LandCover. Data about flood and landslide hazard have been obtained from institutional maps published by ISPRA (Italian Institute for Environmental Protection and Research). The assets have been classified into four classes (D1, D2, D3, D4) considering their type and amount in each terrain unit using a k-means clustering technique.Then, four different risk matrixes were applied to get a Risk Index one for each asset class (D1, D2, D3, D4), by plotting the classified asset classes against the probability of occurrence of a given flood/landslide event. Then, a total Risk Index was derived for each terrain unit, by summing up the Risk Index of each asset class.Insurance companies may access results by a WebApp and a Web Service by authentication Service providing login and password.This work has to be considered a starting point targeted to assess and make available information on exposure, hazard and of the vulnerable elements.

The Third United Nations World Conference on Disaster Risk Reduction, held in Sendai in 2015, has defined a global strategy directed at enhancing risk-exposed communities’ resilience. In line with those needs, the study intends to improve and optimize decision-making processes in wildfire risk management by implementing predictive spatially distributed models of wildfire behaviour.The proposed methodology has been applied to simulate some large and fully documented wildfire events in Umbria and Sardinia regions, in Central and Southern Italy respectively. The predictive model for wildfire behaviour is based on the reviewed Rothermel’s quasi-empirical mathematical model, which investigates propagation-driving parameters, i.e. the local geomorphometrical and meteorological parameters along with the pyrological and phenological characteristics of the local plant communities, to estimate the rate of spread of the fire. Propagation-driving parameters and their spatiotemporal variability have been estimated in the pre-fire environment by applying and adapting empirical relationships well-established in literature. Remote sensing-derived data have been analysed over phenologically distinct periods, along with ancillary data, to elicit information necessary to distinguish the mosaic of fuel model types and to monitor spatiotemporal variations in either live or dead fuel moisture content. According to input data availability, the methodology has been adapted to different case studies, focusing major attention on MODIS instrument by NASA on board the Terra satellite as well as on Sentinel constellations of satellites of the ESA Copernicus programme due to their accessibility and to their medium-high spatial and temporal resolution. A two-dimensional Agent-Based Model with a hexagonal grid, which, given a map of the rate of spread and an ignition point as inputs, returns a map of the cumulative propagation time, has been developed in order to simulate the wildland surface fire behaviour.Satellite estimated propagation-driving parameters have been compared with information collected in the field and recorded by the regional annual reports on wildfire events, revealing a good predictive ability. Likewise, the wildfire behaviour model has provided accurate predictions, up to 70% in terms of morphological matching between obtained simulations and respective documented historical events boundaries, also if compared with results from other well-known wildfire simulation toolset and software. Obtained results suggest the developed wildfire behaviour model could represent a promising tool in prioritizing firefighting interventions in near-real time.

Planning in advance to prepare for and respond to a natural hazard-induced disaster-related emergency is a key action that allows decision makers to mitigate unexpected impacts and potential damage. To further this aim, a collaborative, modular, and information and communications technology-based Spatial Data Infrastructure (SDI) called SIRENE—Sistema Informativo per la Preparazione e la Risposta alle Emergenze (Information System for Emergency Preparedness and Response) is designed and implemented to access and share, over the Internet, relevant multisource and distributed geospatial data to support decision makers in reducing disaster risks. SIRENE flexibly searches and retrieves strategic information from local and/or remote repositories to cope with different emergency phases. The system collects, queries, and analyzes geographic information provided voluntarily by observers directly in the field (volunteered geographic information (VGI) reports) to identify potentially critical environmental conditions. SIRENE can visualize and cross-validate institutional and research-based data against VGI reports, as well as provide disaster managers with a decision support system able to suggest the mode and timing of intervention, before and in the aftermath of different types of emergencies, on the basis of the available information and in agreement with the laws in force at the national and regional levels. Testing installations of SIRENE have been deployed in 18 hilly or mountain municipalities (12 located in the Italian Central Alps of northern Italy, and six in the Umbria region of central Italy), which have been affected by natural hazard-induced disasters over the past years (landslides, debris flows, floods, and wildfire) and experienced significant social and economic losses.

Developing a video game is a costly activity, not only from an economic perspective, but also in terms of the time devoted to researching innovative solutions and managing interfaces between the creative and technical team members. In this article, we explore the possibility to use an approach rooted into AI techniques (namely Behaviour Trees) to help establish a lingua franca between these two different functions, in order to optimize both the development time and the quality of the final product. In particular, we have developed BETTER, a middleware which can be easily embedded into any game engine, and tested it during the design and development of Crazy-Hill, a commercial video game for iOS-based mobile devices.

Executive functions are crucial for performance of everyday activities. In Multiple Sclerosis (MS), executive dysfunctions can be apparent from the early onset of the disease. Technology-based time-efficient and resource-saving tools for early evaluation of executive functions using an ecological approach are needed to assess functional performance in real-life. The aim was to compare the efficiency of the Picture Interpretation Test 360° (PIT 360°) with traditional measures on executive dysfunction in Persons with Multiple Sclerosis (PwMS) and Healthy Controls (HC). Participants were 31 patients with Relapsing-Remitting MS (mean age = 44.323 ± 13.149; mean Expanded Disability Status Scale = 2) and 39 HC (mean age = 39.538 ± 15.728). All were tested with standard neuropsychological tests of executive functions, PIT 360°, and measures of user experience. While standard neuropsychological tests failed to differentiate between PwMS and HC group, the PIT 360° was successful in detecting executive dysfunction in PwMS. All participants reported the PIT 360° to be an engaging tool and endorsed positive reactions to their experience. Overall, the PIT 360° is a quick, sensitive, and ecological tool that captures real-world executive dysfunction in PwMS. This engaging measure is sensitive for the detection of executive deficits since the early phases of the disease.

Il contributo, dopo aver analizzato i fattori che influenzano la capacità di affrontare e superare efficacemente situazioni di emergenza legate a rischi naturali e/o antropici, propone una soluzione implementata nell'ambito del progetto "SIMULATOR_ADS: un Sistema Integrato ModULAre per la gesTione e prevenziOne dei Rischi - Arricchito con Dati Satellitari", una piattaforma ICT originale, con un'architettura basata su servizi Web, a supporto degli operatori e delle autorità locali di Protezione Civile nelle fasi di preparazione e di gestione delle emergenze.

Groundwater level forecasting is a useful tool for a more efficient and sustainable groundwater resource management. Developing models that can accurately reproduce groundwater level response to meteorological conditions can lead to a better understanding of the groundwater resource availability. Here an autoregressive neural network (NNARx) approach is proposed and compared with autoregressive linear models with exogenous input (ARx) in order to forecast groundwater level in an aquifer system where a linear groundwater level response to recharge by rainfall is observed. A well known problem regarding neural networks consists in the high risk of overfitting. Here, three NNARx model were trained using different methods to avoid overfitting: Early stopping, Bayesian regularization and a combination of both. The results show that on the short term forecasting (up to 15 days) the performance of NNARx and ARx are comparable but the ARx model generalizes better, while the NNARx trained with Bayesian regularization outperforms the linear models and the other NNARx models on longer scenarios on the test set. As linear models are less time demanding and do not require high computational power, they can be considered as suitable tools for short term groundwater level forecasting in linear systems while when longer scenarios are needed neural networks can be considered more reliable, and training them with Bayesian regularization allows to minimize the risk of overfitting.

I principali prodotti del progetto comprendono: originali stime dell'esposizione economica (beni capitali e fatturato) delle imprese italiane a livello comunale; le prime stime in Italia della 'disponibilità a pagare' per la riduzione del rischio (alluvioni e terremoti) e connesse stime della VoSL (Value of Statistical Life); l'utilizzo dei dati OMI Osservatorio del Mercato Immobiliare) per stime ai valori di mercato di danno e rischio per le abitazioni a scala sub-comunale; un originale indice sintetico di rischio socio-economico a livello comunale (hazard, esposizioni e vulnerabilità/resilienza socio-economica); un'originale mappatura della pericolosità idraulica su scala nazionale; un tool originale per la valutazione del danno socio-economico atteso da alluvione; mappature di suscettibilità e non suscettibilità a frana per il territorio nazionale integrate con i dati OMI per l'analisi del rischio economico da frana; originali stime delle perdite di valori immobiliari nel terremoto emiliano 2012 e, in via preliminare, nel terremoto in Centro Italia 2016-2017.

Two complementary GIS-based functions are designed and implemented to assess the expected degree of loss due to the occurrence of flood events. Each function processes institutional thematic layers and allows decision makers first to quantify the physical and the economic exposure of the elements at risk in a given study region and then to assess the expected degree of economic loss in relation to the flood water depth chosen for the analysis. The functions are implemented using QGIS with GRASS Python API extension and the workflow is exposed as a QGIS plug-in. The GUI is built over QT multi-platform framework and, therefore, the results are consistently integrated into the QGIS system.

The assessment of executive functions poses researchers with several challenges related to both the complexity of the construct of executive functions itself and/or the methodological difficulties related to its evaluation. The main objective of the current study was to evaluate a 360° version of an ecologically valid assessment called the Picture Interpretation Test (PIT). Participants included 19 patients with Parkinson’s disease (PD) and 19 healthy controls. All participants endorsed globally positive experiences of the PIT 360°. Furthermore, findings indicated that patients with PD took longer to correctly interpret the PIT 360° scene and tended to significantly focus on details of the 360° scene instead of the most informative elements. The time needed for a correct interpretation of the presented scene also correlated significantly with performance in conventional paper and pencil tests of executive functions for patients with PD. Classification analysis indicated the potential of the PIT 360° for distinguishing between patients with PD and healthy controls. Overall, these data provide preliminary evidence in support of the PIT 360° for evaluating executive functions.

Il lavoro ha avuto come obiettivo la definizione di una metodologia finalizzata alla delineazione delle aree inondabili in funzione di diversi tempi di ritorno applicabile a tutto il territorio nazionale. Le aree inondabili sono generalmente definite utilizzando modelli di flusso mono e bi-dimensionali, l'applicazione di questi modelli è condizionata dalla disponibilità di dati accurati e sono generalmente molto costosi in termini di disponibilità dei dati, tempo di calcolo e risorse umane impiegate. Per questo motivo la loro applicazione è limitata a studi specifici di singoli tratti delle aste fluviali. Per ovviare a queste limitazioni alcuni autori ((Degiorgis et al., 2012); (De Risi, Jalayer, & De Paola, 2015); (Manfreda et al., 2015)) hanno sperimentato l'utilizzo dei metodi statistici finalizzati all'individuazione delle aree inondabili, applicandoli con risultati soddisfacenti a bacini idrografici di dimensioni limitate utilizzati come aree test. In ragione dell'estensione dell'area di studio, corrispondente all'intero territorio nazionale, e della numerosità dei corsi d'acqua da valutare, si è scelto di far uso di un approccio statistico simile a quelli proposti in letteratura e precedentemente elencati. Sulla base di tali lavori, nel corso del biennio di progetto, IRPI ha sviluppato una procedura statistica che utilizza modelli statistici di tipo «machine- learning» per classificare il territorio (i.e. inondabile vs non-inondabile) basandosi sull'uso di variabili morfometriche derivate dai modelli digitali di elevazione (DEM). In pratica, l'approccio proposto per la determinazioni delle aree inondabili si basa su criteri di tipo geomorfologico. La modellazione ha previsto due fasi distinte: (i) fase di calibrazione del modello; (ii) fase di validazione del modello. Per la calibrazione sono stati utilizzati i dati relativi alle aree inondabili resi disponibili nei PAI ed elaborati ai sensi del D.Lgs 49/2010 di recepimento della Direttiva Flood. La procedura statistica elaborata nell'ambito del progetto e l'applicazione su scala nazionale ha consentito l'individuazione delle aree inondabili delle aste fluviali e dei bacini secondari che non sono stati oggetto delle perimetrazioni della pericolosità idraulica elaborate ai sensi del D.Lgs 49/2010 che costituiscono lo scenario di pericolosità istituzionale di riferimento. Il risultato finale è uno scenario di pericolosità modellato, ovvero una zonazione sinottica della propensione del territorio ad essere inondato (suscettibilità da inondazione), per vari tempi di ritorno, uniforme e confrontabile per tutto il territorio nazionale basato su criteri di tipo geomorfologico Questo primo tentativo di modellazione statistica a scala nazionale finalizzata alla perimetrazione della pericolosità idraulica è da considerarsi un notevole valore aggiunto alla conoscenza della pericolosità idraulica del territorio italiano, complementare, e non sostituibile, agli scenari istituzionali dei PAI e delle perimetrazioni ai sensi D.Lgs 49/2010.

In order to effectively plan both preparedness and response to emergency situations it is necessary to access and analyse timely information on plausible scenarios of occurrence of ongoing events. Scenario maps representing the estimated susceptibility, hazard or risk of occurrence of an event on a territory are hardly generated real time. In fact the application of physical or statistical models using environmental parameters representing current dynamic conditions is time consuming on low cost hardware equipment. To cope with this practical issue we propose an off line generation of scenario maps under diversified environmental dynamic parameters, and a geo-Spatial Data Infrastructure (SDI) to allow people in charge of emergency preparedness and response activities to flexibly discover, retrieve, fuse and visualize the most plausible scenarios that may happen given some ongoing or forecasted dynamic conditions influencing the event. The novelty described in this chapter is related with both the ability to interpret flexible queries in order to retrieve risk scenario maps that are related to the current situation and to show the most plausible worst and best scenarios that may occur in each elementary area of the territory. Although, the SDI proposal has been conceived and designed to support the management of distinct natural and man-made risks, in the proof of concept prototypal implementation the scenarios maps target wild fire events.

In these last few years we are witnessing an increasing adoption of video games in learning and teaching environments. This change is coming thanks to the fact that video games allow students to take a more active role in learning as they develop skills needed to succeed in their professional careers. At the same time, we are also observing a change in the way video games are implemented. Today, the existence of very large teams with a multi-layered organisation calls for the adoption of structured development approaches with associated environments. These environments have been baptised game engines. Availability and usability of game engines, in the near future, will positively influence educational activities for the next generations. In this paper, we discuss the general structure of modern game engines and put into question their current architectural approach. Our goal is to raise the attention of the scientific community on the fact that re-baptised software stacks are unlikely, on the long shot, to provide the flexibility and functionalities required by game developers in the coming years. After a detailed discussion of the possible problems on the horizon, an alternative approach for a modular and scalable game engine architecture will also be presented.

Deliverable finale del WP 4 - CARE-G (Progetto dell'Accordo Quadro di collaborazione tra Regione Lombardia e Consiglio Nazionale delle Ricerche, Delibera di Giunta Regionale n. 3728 dell'11 luglio 2012 - Area tematica: Tecnologie per le Smart Communities - Durata 24 mesi : inizio 25 Luglio 2013 - Capofila: Istituto di Bioimmagini e Fisiologia Molecolare (IBFM) - Unità operative: Istituto di Fisiologia Clinica (IFC) - Istituto di Tecnologie Biomediche (ITB) - Istituto per il Rilevamento Elettromagnetico (IREA) - Istituto per la Dinamica dei Processi Ambientali (IDPA)) con la descrizione delle funzionalità, dati e valutazione utente del portale del sistema informativo geografico per supportare i care giver di utenti anziani .