Citizen participation and awareness raising in coastal protected areas. A case study from Italy

In this chapter, part of the research carried out within the SECOA project (www.projectsecoa.eu) is presented. Attention is devoted to methods and tools used for supporting the participatory process in a case of environmental conflict related to the definition of boundaries of a coastal protected area: the Costa Teatina National Park, in Abruzzo, central Italy. The Costa Teatina National Park was established by the National Law 93/2001. Its territory includes eight southern Abruzzo municipalities and covers a stretch of coastline of approximately 60 km. It is a coastal protected area, which incorporates land but not sea, characterized by the presence of important cultural and natural assets. The Italian Ministry of Environment (1998) defines the area as “winding and varied, with the alternation of sandy and gravel beaches, cliffs, river mouths, areas rich in indigenous vegetation and cultivated lands (mainly olives), dunes and forest trees”. The park boundaries were not defined by the law that set it up, and their determination has been postponed to a later stage of territorial negotiation that has not ended yet (Montanari and Staniscia, 2013). The definition of the park boundaries, indeed, has resulted in an intense debate between citizens and interest groups who believe that environmental protection does not conflict with economic growth and those who believe the opposite. That is why the process is still in act and a solution is far from being reached. In this chapter, the methodology and the tools used to involve the general public in active participation in decision making and to support institutional players in conflict mitigation will be presented. Those tools have also proven to be effective in the dissemination of information and transfer of knowledge. Results obtained through the use of each instrument will not be presented here since this falls outside the purpose of the present essay. The chapter is organized as follows: in the first section the importance of the theme of citizen participation in decision making will be highlighted; the focus will be on participation in the processes of ICZM, relevant to the management of coastal protected areas. In the second section a review of the most commonly used methods in social research is presented; advantages and disadvantages of each of them will be highlighted. In particular, the history and the evolution of the Delphi method and its derivatives are discussed; focus will be on the dissemination value of the logic underlying such iterative methods. In the third section the tools used in the case of the Costa Teatina National Park will be presented; strengths and weaknesses will be highlighted and proposals for their improvement will be advanced. Discussion and conclusions follow

Chapter Reducing inconsistency in AHP by combining Delphi and Nudge theory and network analysis of the judgements: an application to future scenarios

The Analytic Hierarchy Process (AHP) is a Multi-Criteria method in which a number of decision factors (typically criteria and alternatives) are compared pairwise by one or more experts, using the Saaty scale, with the goal of sorting the alternatives (Saaty, 1977; 1980). For group AHP the Delphi method can be used in parallel with the AHP (Di Zio and Maretti, 2014), and this allows the search for a consensus on each pairwise judgement. A big issue of the AHP regards the inconsistency of the pairwise comparison matrices and here we propose a new method to reduce the inconsistency. As a solution we exploit the Nudge theory (Thaler and Sunstein, 2008) and from the second round of the Delphi survey, we calculate and circulate a Nudge to “gentle push” the experts towards more consistent evaluations. Furthermore, we propose the representation of the AHP matrices through graphs. In a direct graph two nodes are linked with two direct and weighted edges (or one edge with the direction based on the weights), where the weights indicate the evaluation given by an expert or, for a group, the geometric mean of the judgements. This type of visualization facilitates the reading of the results and could also be used as real-time feedback in the Delphi process, by displaying on the edges also a measure of variability. An application is proposed, on the evaluation of four future scenarios on the regulation of genetic modification experiments, assessed by a panel of 27 experts according to different criteria (plausibility, consistency and simplicity). The application demonstrated that it is possible to: a) reduce the inconsistency; b) collect useful textual material which enrich the AHP itself; c) use the inconsistency index as a stopping criterion for the Delphi rounds; d) display the pairwise comparison matrices with graphs

A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear Power Plant

This paper presents a similarity-based approach for prognostics of the Remaining Useful Life (RUL) of a system, i.e. the lifetime remaining between the present and the instance when the system can no longer perform its function. Data from failure dynamic scenarios of the system are used to create a library of reference trajectory patterns to failure. Given a failure scenario developing in the system, the remaining time before failure is predicted by comparing by fuzzy similarity analysis its evolution data to the reference trajectory patterns and aggregating their times to failure in a weighted sum which accounts for their similarity to the developing pattern. The prediction on the failure time is dynamically updated as time goes by and measurements of signals representative of the system state are collected. The approach allows for the on-line estimation of the RUL. For illustration, a case study is considered regarding the estimation of RUL in failure scenarios of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS

Chapter Unsupervised spatial data mining for the development of future scenarios: a Covid-19 application

In the context of Futures Studies, the scenario development process permits to make assumptions on what the futures can be in order to support better today decisions. In the initial stages of the scenario building (Framing and Scanning phases), the process requires much time and efforts to scanning data and information (reading of documents, literature review and consultation of experts) to understand more about the object of the foresight study. The daily use of social networks causes an exponential increase of data and for this reason here we deal with the problem of speeding up and optimizing the Scanning phase by applying a new combined method based on the analysis of tweets with the use of unsupervised classification models, text-mining and spatial data mining techniques. For the purpose of having a qualitative overview, we applied the bag-of-words model and a Sentiment Analysis with the Afinn and Vader algorithms. Then, in order to extrapolate the influence factors, and the relevant key factors (Kayser and Blind, 2017; 2020) the Latent Dirichlet Allocation (LDA) was used (Tong and Zhang, 2016). Furthermore, to acquire also spatial information we used spatial data mining technique to extract georeferenced data from which it was possible to analyse and obtain a geographic analysis of the data. To showcase our method, we provide an example using Covid-19 tweets (Uhl and Schiebel, 2017), upon which 5 topics and 6 key factors have been extracted. In the last instance, for each influence factor, a cartogram was created through the relative frequencies in order to have a spatial distribution of the users discussing each particular topic. The results fully answer the research objectives and the model used could be a new approach that can offer benefits in the scenario developments process

Risk-based clustering for near misses identification in integrated deterministic and probabilistic safety analysis

In integrated deterministic and probabilistic safety analysis (IDPSA), safe scenarios and prime implicants (PIs) are generated by simulation. In this paper, we propose a novel postprocessing method, which resorts to a risk-based clustering method for identifying Near Misses among the safe scenarios. This is important because the possibility of recovering these combinations of failures within a tolerable grace time allows avoiding deviations to accident and, thus, reducing the downtime (and the risk) of the system. The postprocessing risk-significant features for the clustering are extracted from the following: (i) the probability of a scenario to develop into an accidental scenario, (ii) the severity of the consequences that the developing scenario would cause to the system, and (iii) the combination of (i) and (ii) into the overall risk of the developing scenario. The optimal selection of the extracted features is done by a wrapper approach, whereby a modified binary differential evolution (MBDE) embeds a K-means clustering algorithm. The characteristics of the Near Misses scenarios are identified solving a multiobjective optimization problem, using the Hamming distance as a measure of similarity. The feasibility of the analysis is shown with respect to fault scenarios in a dynamic steam generator (SG) of a nuclear power plant (NPP)

Transient identification by clustering based on Integrated Deterministic and Probabilistic Safety Analysis outcomes

open3noIn this work, we present a transient identification approach that utilizes clustering for retrieving scenarios information from an Integrated Deterministic and Probabilistic Safety Analysis (IDPSA). The approach requires: (i) creation of a database of scenarios by IDPSA; (ii) scenario post-processing for clustering Prime Implicants (PIs), i.e., minimum combinations of failure events that are capable of leading the system into a fault state, and Near Misses, i.e., combinations of failure events that lead the system to a quasi-fault state; (iii) on-line cluster assignment of an unknown developing scenario. In the step (ii), we adopt a visual interactive method and risk-based clustering to identify PIs and Near Misses, respectively; in the on-line step (iii), to assign a scenario to a cluster we consider the sequence of events in the scenario and evaluate the Hamming similarity to the sequences of the previously clustered scenarios. The feasibility of the analysis is shown with respect to the accidental scenarios of a dynamic Steam Generator (SG) of a NPP.Di Maio, Francesco; Vagnoli, Matteo; Zio, EnricoDI MAIO, Francesco; Vagnoli, Matteo; Zio, Enric

A visual interactive method for prime implicants identification

We propose a visual interactive method for the identification of the Prime Implicants (PIs) of dynamic non-coherent systems. Visual interactive methods integrate mathematical and symbolic models with runtime interaction and real-time graphic display, which allow visualizing the underlying physical relationships among process parameters. The proposed method is based on a parallel coordinates data mining tool that relies on an innovative pruning procedure which, on the basis of a proper selection of characteristic features of the accident sequences, retrieves the PIs among the whole set of Implicants in terms of process parameters values and/or components failure states. The method is exemplified on an artificial case study and, then, applied for the dynamic reliability analysis of the Airlock System (AS) of a CANDU reactor

A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis

The challenge of Risk-Informed Safety Margin Characterization (RISMC) is to develop a methodology for estimating system safety margins in the presence of stochastic and epistemic uncertainties affecting the system dynamic behavior. This is useful to support decision-making for licensing purposes. In the present work, safety margin uncertainties are handled by Order Statistics (OS) (with both Bracketing and Coverage approaches) to jointly estimate percentiles of the distributions of the safety parameter and of the time required for it to reach these percentiles values during its dynamic evolution. The novelty of the proposed approach consists in the integration of dynamic aspects (i.e., timing of events) into the definition of a dynamic safety margin for a probabilistic Quantification of Margin and Uncertainties (QMU). The system here considered for demonstration purposes is the Lead-Bismuth Eutectic- eXperimental Accelerator Driven System (LBE-XADS)

A sensitivity analysis for the adequacy assessment of a multi-state physics modeling approach for reliability analysis

In this work, a moment-independent Sensitivity Analysis (SA) based on Hellinger distance and Kullback-Leibler divergence is proposed to identify the component of a system most affecting its reliability (Diaconis et al., 1982; Gibbs et al., 2002; Di Maio et al., 2014). This result is used to adequately allocate modeling efforts on the most important component that, therefore, deserves a component-level Multi-State Physics Modeling (MSPM) to be integrated into a system-level model, to estimate the system failure probability