Assessing the efficiency and the criticality of the elements belonging to a complex territorial system subject to natural hazards

International audienceThe effects of natural hazards can be mitigated by the use of proper "pre-event" interventions on "key" elements of the territory, that is on elements that are mostly vulnerable to a given catastrophic scenario and whose loss of functionality can cause damages on people, property and environment. In this respect, methodologies and tools should be studied to support decision makers in the analysis of a territory, in order to point out such elements. In this work, vulnerability is taken into account under two aspects: "physical vulnerability", which measures the propensity of a territorial element to suffer damage when subject to an external stress corresponding to the occurrence of a natural phenomenon; "functional vulnerability", which measures the propensity of a territorial element to suffer loss in functionality, even when that is caused by the loss of functionality of other territorial elements. In the proposed modeling approach, vulnerability is represented through the use of a graph-based formalization. A territorial system is represented as a complex set of elements or sub-systems. Such elements have differentiated and dedicated functions, and they may be functionally interconnected among them. In addition, vulnerability is defined through the use of two different variables, namely the criticality and the efficiency. Focusing the attention on the temporal phases corresponding to the occurrence of a calamitous event, the first one measures the service demand of an element, whereas the efficiency is a measure of the service that can be offered by such an element. The approach presented is largely independent from the natural risk considered. Besides, the tools introduced for the vulnerability analysis of the territorial system can also be used to formalize decision problems relevant to the location of the available resources for emergency management. A specific case study pertaining to the hydrological risk in the Val di Vara area (Italy) is presented

Natural risk assessment and decision planning for disaster mitigation

International audienceIn this paper, decisional models are introduced aiming at defining a general framework for natural disaster mitigation. More specifically, an integrated approach based on system modelling and optimal resource assignment is presented in order to support the decision makers in pre-operational and real-time management of forest fire emergencies. Some strategies for pre-operative and real time risk management will be described and formalized as optimal resource assignment problems. To this end, some models capable to describe the resources dynamics will be introduced, both in pre-operative phase and in real-time phase

A geostatistical approach to multisensor rain field reconstruction and downscaling

International audienceA rain field reconstruction and downscaling methodology is presented, which allows suitable integration of large scale rainfall information and rain-gauge measurements at the ground. The former data set is assumed to provide probabilistic indicators that are used to infer the parameters of the probability density function of the stochastic rain process at each pixel site. Rain-gauge measurements are assumed as the ground truth and used to constrain the reconstructed rain field to the associated point values. Downscaling is performed by assuming the a posteriori estimates of the rain figures at each grid cell as the a priori large-scale conditioning values for reconstruction of the rain field at finer scale. The case study of an intense rain event recently observed in northern Italy is presented and results are discussed with reference to the modelling capabilities of the proposed methodology. Keywords: Reconstruction, downscaling, remote sensing, geostatistics, Meteosa

Optimal charging of electric vehicles in microgrids through discrete event optimization

In this paper, a discrete event approach is proposed for the optimal charging of electrical vehicles in microgrids. In particular, the considered system is characterized by renewable energy sources (RES), non-renewable energy sources, electrical storage, a connection to the external grid and a charging station for electric vehicles (EVs). The decision variables are relevant to the schedule of production plants, storage systems and EVs' charging. The objective function to be minimized is related to the cost of purchasing energy from the external grid, the use of nonrenewable energy sources and tardiness of customer's service. The proposed approach is applied to a real case study and it is shown that it allows to considerably reduce the dimension of the problem (and thus the computational time required) as compared to a discrete-time approach

A DSS for the evaluation of the consequences of natural hazards on a complex territorial system

In this paper, a decision support system (DSS) able to assist urban planners in natural hazard mitigation in preventive phase is proposed. In order to define the structure of this DSS, a methodology based on influence graphs is defined, in which nodes represent entities that are important for their social, environmental and social functions on the territory, and links represent the influence between the entities. The level of functionality of each entity is influenced by both its physical integrity and the functionality of the related entities. As not all the entities influence the considered one in the same way, different levels of influence are taken into account. Several rules to automatically identify the level of relationship between different entities are defined. The proposed methodology has been applied to the territory of Imperia, in the north-western coast of Italy, with special regard to wildfires risk. Several entities have been selected, over the considered territory, according to their relevance. The hazard distribution over the considered area, due to wildfire risk has been estimated. On this basis, an overall procedure capable to assist the decision maker in the analysis of the territorial risk can be established

A multi-objective approach for sustainable Municipal Solid Waste (MSW) management

A multi-objective approach to sustainable Municipal Solid Waste (MSW) management is presented, with the aim of supporting the decisions about the optimal flows of solid waste to be sent to landfill, recycling and to the different treatment plants. To achieve this goal, an approach is proposed in which the decision makers (DMs) are interactively involved in the decision process, following the reference point methodology [Wierzbicki et al, 2002]. The method can be viewed as an integration/modification of techniques already introduced in the literature. The purpose of the DMs is to determine the various flows of the different materials in the whole MSW management system in order to satisfy a number of technological and normative constraints and minimizing four main objectives: the economic cost of material treatment, the quantity of unrecycled waste, the quantity of waste sent to the landfill, and the emissions of the incinerator. The model proposed has been applied to a case study concerning the municipality of Genova. The case has been analysed assuming the presence of two different decision makers, characterized by different attitudes in selecting the initial reference solution and in interacting with the methodology. Results and final comments are reported

An Operational Scheme For Dynamic Resource Management In Case Of Natural Disaster Events

The management of the resources engaged in emergencies due to the occurrence of very intense natural events requires the acquisition and processing of a huge number of heterogeneous data. Such data, both of deterministic and random nature, generally refer to different spatial and temporal scales. Considering the three main frameworks in which a natural risk scenario can be classified (i.e., preventive, emergency, and post-emergency phases), risk assessment has to be followed by a decision-oriented phase, whose objective is the selection of the optimal actions to undertake, on the basis of the available information. In the paper, the conceptual scheme and the system architecture of a hierarchical decisional model relevant to a natural risk emergency scenario are considered and discussed in detail. Such a scheme relies on system modelling and optimisation techniques, and is based on different decisions layers. At the top of the hierarchy, a decision centre makes use of aggregated information, generated by specific models, in order to relocate resources to the local centres. The lower centres must cope with the (forecasted or actually reported) emergency events with their own resources, basing their strategy both on the local information sets, and on the aggregated data provided by the highest decisional centre

Optimal Sampling for Parameters Estimation

In the problems concerning prediction and modeling, parameters estimation constitutes one of the main uncertain items that must be taken into account. The easiest way to minimize this uncertainty is to collect great amounts of data. The aim of this work is to build a decision model able to choose the optimal position of the sample point used for the parameters estimation, minimizing the parameters uncertainty. The decision model is applied to the estimation of the dispersivity coefficients, longitudinal and transversal, from soil column experiment. The classical design of experiments techniques are based on the optimization of the amount of information obtained from experimental data with the hypothesis that the sample domain is defined on a continuous space over time and position. Since this assumption does not reflect the real experimental situation, especially when field campaigns are to be performed and the position of the piezometric wells is fixed, an approach based on discrete optimization over a fixed grid of possible sampling is proposed. The soil column representation is discretized in the 2D domain, while the concentration experimental data are generated using a rigorous analytical solution of the advection dispersion model and a Monte Carlo simulator to generate the experimental error at given variance. In order to define the optimal sampling points in the soil column, binary decision variables are introduced: they assume value one when the concentration is measured at a specific point and time, zero otherwise. The objective function to be finally minimized is proportional to the calculated covariance of the estimated parameters and to the decision variables.. The formalized constraints regard the possible number of measures, according to the available funds. Finally, the results of the optimisation problem are discussed

A Multiobjective Approach for Solid Waste Management

Nowadays, solid waste management is a problem of major relevance for all societies. Finding acceptable strategies to cope with such a problem is becoming a quite hard task, owing to the increasing awareness of environmental issues by population and authorities. In general, this awareness has led to the development of enhanced pollution control technologies and to a more rigorous legislation on waste handling and disposal, to minimize the related environmental impact. Solid waste management is a problem that is even more felt at the municipal level, where decision makers should plan an effective strategy, taking simultaneously into account conflicting objectives (e.g. economic, technical, normative, environmental). In addition, the problem is characterized by an intrinsic uncertainty of the estimates of costs and environmental impacts. These reasons have led several authors to propose multi-criteria decision approaches. In this paper, a Municipal Solid Waste (MSW) management system, including one separator, one plant for production of Refuse Derived Fuel (RDF), one incinerator with energy recovery, one plant for treatment of organic material coming out from one separator and one landfill, has been considered. Decisions concern optimal flows of solid waste to be sent to the different plants and to recycling, as well as the sizing of the different treatment plants. A multiobjective approach to support municipal decision makers in the planning of their MSW management system is described. Four main objectives have been proposed, reflecting the most important and conflicting aspects of the decision, specifically: minimizing economical costs (installation, maintenance, transport, and separate collection costs), minimizing incinerator emissions (such as SO2, HCl, HF, NOx, dust, and heavy metals emissions coming out the incinerator plants), minimizing the filling time of the sanitary landfill, and maximizing material recovery. Finally, the proposed approach is applied to a specific case study and results are reported

A Decision Support System for energy production from renewable resources: logistics aspects of sustainable forest biomass collection

The consequences of the use of traditional fossil fuels has led, in the last years, the European Union to promote and encourage the development and the use of renewable energies rather than the traditional ones. Energy production from forest biomass does not involve the CO2 increase in the atmosphere, contributing in this way to the duties assumed by the European Community during the International Conference of Kyoto (1997). The developed DSS, that is a generalization and the further research activity presented in a previous work by the same authors (Freppaz et al. [2004]), is based on the integration of Geographic Information Systems tools, a relational database, and decision models (in terms of decision variables, objectives, and constraints). Specifically, two decision models have been created in order to face two sustainability verifications. The first one regards the planning of biomass collection and transport considering the biomass as a constant over time. In this case, the objective is to find the forest parcels from where it is convenient to collect biomass, for certain fixed plant sizes, and establish the quantity to take from each of them and to send to a specific plant, considering the geographical characteristics and the legislative constraints. This problem corresponds to a static decision model. The second decision model has a dynamic structure and it considers the biomass collection planning over five years, on the basis of a preliminary plant size, found solving the previous mathematical programming problem. The structure of the dynamic decision model is strictly connected with the growth model of the trees that are present in the different forest parcels. A user friendly interface is used to link the optimization models, the GIS, and the metadata necessary for the problem solution