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The paper proposes level-based approximate reasoning on a fuzzy ontology as a modeling framework to support the management of Volunteered Geographic Information (VGI) affected by an observation deficiency generating both uncertainty and fuzziness. The paper recalls the inadequacy of classic ontologies to create VGI, the limitation of the use of fuzzy ontologies to model both fuzziness and uncertainty, and proposes level based reasoning to answer user queries on VGI supported by a fuzzy ontology. A case study example is discussed.

Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c-statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).

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.

The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic (ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners, able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20% of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression, linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km 2 characterized by a very high density of landslides, mainly superficial slope failures triggered by intense rainfall events.

Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providing quantitative insight into the analysis of the results. This evaluation should be done at least to reduce the questionability of the results and so to limit the number of potential choices.

The aim of the study is to assess the degree of spatial agreement among different patterns of landslide susceptibility maps with almost similar success and prediction rate curves, obtained using different combinations of predictive factors. Our approach was tested in an alpine environment (Italian Alps) where debris flows represent one of the most frequent dangerous processes. A data-driven Bayesian method (the Weights of Evidence modelling technique) was successfully applied, and success and prediction rate curves were computed for supporting the modelling results and assessing the robustness of the models. The values of the area under curves were very similar for different models, ranging from 84.36% to 86.49% for the success rate curves and from 82.46% to 85.66% for the prediction rate curves. Then, the post-probability output maps were classified into rank-based maps, by using an equal-area criterion, to compare the predicted results.Afterwards, appropriate statistical techniques (kappa statistic, principal component analysis, and distance weighted entropy) were applied. Kappa statistic and principal component analysis outcomes called for significant differences within the output spatial patterns of the predicted maps as well as within the highest susceptibility classes. Moreover, the estimated distance weighted entropy values showed a very low overall entropy at the valley bottom, as all models predicted this area equally as low susceptible. In contrast, areas characterised by the highest values of entropy were more concentrated in the northern and southern slopes of the study site, lying in zones where landslide density was higher.Consequently, susceptibility maps with similar predictive power may not have the same meaning in terms of spatial pattern of predicted results. It is for this reason that landslide susceptibility maps should be distributed together with map documents aimed at defining the level of accuracy of the predicted results to provide the end-users with informative selection criteria. © 2010 Elsevier B.V.

Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI) capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI). This paper describes an original OGC standard interoperable SDI architecture and a geospatial data and metadata workflow for creating and managing multisource heterogeneous geospatial datasets and time series, and discusses it in the framework of the Space4Agri project study case developed to support the agricultural sector in Lombardy region, Northern Italy. The main novel contributions go beyond the application domain for which the SDI has been developed and are the following: the ingestion within an a-centric SDI, potentially distributed in several nodes on the Internet to support scalability, of products derived by processing remote sensing images, authoritative data, georeferenced in-situ measurements and voluntary information (VGI) created by farmers and agronomists using an original Smart App; the workflow automation for publishing sets and time series of heterogeneous multisource geospatial data and relative web services; and, finally, the project geoportal, that can ease the analysis of the geospatial datasets and time series by providing complex intelligent spatio-temporal query and answering facilities.

International audience; This paper investigates the causes of imprecision of the observations and uncertainty of the authors who create Volunteer Geographic Information (VGI), i.e., georeferenced contents enerated by volunteers when participating in some citizen science project. Specifically, various aspects of imprecision and uncertainty of VGI are outlined and, to cope with them, a nowledge-based approach is suggested based on the creation and management of “contextualized VGI”. A case study example in agriculture is reported where contextualized VGI can be created bout in situ crops observations by the use of a smart app that supports volunteers by means of both an ontology and the representation of the context of the geo-localization. Furthermore, n approach to cope with both ill-defined knowledge and volunteer’s uncertainty or imprecise observations is defined based on a fuzzy ontology with uncertainty level-based approximate easoning. By representing uncertainty and imprecision of VGI, users, i.e., consumers, can exploit quality checking mechanisms to filter VGI based on their needs.

Proactive management is particularly important to deal with the increasing occurrence of hydro-meteorological hazards in mountain areas were threats are often caused by multiple and sudden onset hazards such as debris flows. Citizen volunteers can be involved in supporting technicians on inspecting the structures' functional status. Such collaborative effort between managing organizations and local volunteers becomes more important under limited resources. To consider volunteers' information in support of proactive inspection of hydraulic structures, we developed a methodology applicable in day-to-day risk management. At first, in collaboration with technicians-in-charge, a data collection approach was developed for first level or pre-screening visual inspections that can be performed by volunteers. Methods comprise of a data collection exercise, an inspection forms and a learning session based on existent procedures in the FVG region and neighbouring regions. To systematically evaluate the individual inspection reports, we designed a support method by means of a multi-criteria method with fuzzy terms. The method allows the technicians-in-charge to categorize the reports in one of three levels, each corresponding with a course of action. To facilitate the evaluation of inspection reports, we transformed the decision support method into a prototype Web-GIS application. The design process of the Web-GIS framework followed a user-centred approach. The conceptual design incorporates four modules for managing the inspection reports: 1) Registered users, 2) Inspection planning; 3) Available reports and 4) Evaluation of reports. The development of the prototype focused on the evaluation module and was implemented based on standard and interoperable open source tools. Finally, we organized a workshop with technicians in the study area to test the decision support method and get insights about the usefulness of the Web-GIS framework. Participants that took part of the workshop included technicians that were not involved in previous research activities. The involvement of new technicians was important due to their fresh perspectives. We looked at the effect of the quality of the input reports on the output of the decision support method. In addition, we compared the differences in the participants' advice during the inspection and the output from the decision support method. Participants' feedback led to a set of suggested improvements in the decision support method and the web-GIS application. We hope that the knowledge, theory and concept behind this decision support method can be developed into a full-scale web-GIS application. The advantage of using this decision support method is that it allows inspections to be carried out by either skilled volunteers or technicians while ensuring technicians-in-charge that they can systematically evaluate the collected reports. Volunteers can become skilled inspectors by teaming up with technicians for the inspection of hydraulic structures. Technicians can become more aware about local impacts and changes in the structures' status by teaming up with volunteers.

First-level inspections could be provided by skilled volunteers or technicians to pre-screen the functional status of check dams. This paper discusses the design and testing of a support method in collaboration with the responsible technicians in evaluating inspection reports. Reports are based on linguistic rating scales that are systematically aggregated into indices by means of a multi-criteria TOPSIS method with fuzzy terms. The aggregation procedure is carried out for three parameters representing the structure’s status while highlighting any lack of completeness of inspection reports. The method was evaluated using inspection reports collected during a workshop in the Fella basin in the Italian Alps. The method allows the responsible technicians to set rules to categorise the aggregated indices in one of three levels, each corresponding with a course of action. Rules were useful to categorise the aggregated indices according to the structure’s status. Disagreements on rating defects suggest that a weighted aggregation procedure to calculate the indices might lead to overestimating or underestimating defects. Complementary data from historical inspections or remote sensing are required to initiate specific actions. The method can be applied to pre-screen different types of hydraulic structures after adaptation to the local conditions and functional requirements.

The occurrence of elevated nitrate (NO 3 − ) concentration in the aquifer of the Province of Milan (northern Italy) is related to both natural and anthropogenic variables. Using the weights-of-evidence modeling technique a specific vulnerability assessment has been performed. This study presents an evolution of previous applications of the proposed methodology as a consequence of an updating of the available database, in terms of data type, quality, and accuracy, and of a more specific and enhanced statistical controls onto the final results. A comparison between the spatial distribution of vulnerability classes and the frequency of occurrences of nitrate in wells shows a high degree of correlation, both for low and high nitrate concentration. Similar results may be evidenced considering the correlation between posterior probability classes and mean nitrate concentrations in wells located in each of these classes: a high R 2 value (0.99) and the agreement with the threshold concentration value used to define prior probability testifies a general good quality of results. Groundwater-specific vulnerability has been classified in terms of vulnerability classes and, according to the outcomes of the model, the density of population can be considered the most impacting source of nitrate. Mean annual irrigation and groundwater depth can be identified as influencing factors in the distribution of nitrate, while agricultural practice appears a negligible factor.

Nowadays Spatial Data Infrastructures are the best practice to publish huge amount of spatial data on the Web in an interoperable and distributed way. Nevertheless, this operation requires a significant effort, expertise and motivation to data providers. In this paper, we propose an original approach to support geo-data providers by automating the workflows for publishing geo-data and relative web services for a given application. The prototypal solution has been tested on a real case study to support the regional or national agricultural sector in Italy.

Les techniques d'analyse spatialisee par SIG sont de plus en plus utilisees pour evaluer la susceptibilite des versants aux glissements de terrain. Parmi ces methodes l'analyse bivariee par theorie de l'evidence est consideree comme la plus robuste. Cependant, l'inconvenient principal de cette technique est le probleme de redondance d'information entre variables predictives. L'analyse bivariee par regression logistique, assez peu utilisee actuellement, donne des resultats prometteurs dans ce domaine sans necessiter un travail d'evaluation prealable des donnees. Par une analyse de sensibilite sur les variables predictives, cet article presente une comparaison de ces deux methodes pour obtenir des cartes de susceptibilite realistes et statistiquement acceptables pour une echelle du 1 /10 000e dans un environnement montagneux complexe.

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 .

Coastal areas are complex systems that represent the interface between the human, physical and natural components. This paper describes the design, development and application of a conceptual foundation for a quantitative integrated coastal element vulnerability assessment using the up to date Source-Pathway-Receptor-Consequence (SPRC) approach. It is a conceptual model that combines a well-established approach in the field of waste and pollution management with the possibility of introducing the concept of system diagrams. Through the implementation of hazard classification, the approach leads to critical facilities identification and the loss estimation for specific hazards when different types of buildings are selected. In the example of Cayman Islands, the presence of exposed elements at risk, as the port or the airport, named critical facilities, drives serious potential damage effects due to high winds and storm surge. This approach provides both a spatial data infrastructure design, for collecting, storing and managing critical facilities information and a vulnerability assessment procedures for structural and operational components, concerning coastal zones affected by hurricane and related hazards. The final part of the paper synthesizes the conceptual treatment of coastal vulnerability in the Grand Cayman Island and underlines the ready-to-use GIS based vulnerability methodologies for risk assessment allowing to build capacity and resilience of the local communities. © 2014 Elsevier Ltd. All rights reserved.

The SISTEMATI project is developing a distributed information and communication system aimed at distributing and sharing information among people in charge of preparedness and response activities in the field of Spatial Planning and Civil Protection. To this end, the project provides authorities and volunteers with a Spatial Data Infrastructure (SDI) supporting different tools and facilities targeted to: retrieve natural hazard risk maps on the basis of geo-information included in metadata files; it makes use of a Spatial Decision Support System to suggest and control the flow of actions to put in practice during an emergency; and manages Volunteered Geographic Information (VGI) provided by citizens to point out anomalous conditions of the territory. Although, the SDI has been originally conceived and designed to support the management of distinct natural and man-made risks, in the prototypal implementation of the proposal, the management of wild fire risk in a study area approximately covering 3.900 km2 in the central-eastern part of the Sardinia Island (Nuoro province) is the primary issue.

Density based clustering is proposed as an effective way to perform geographic and temporal exploration of messages freely generated within social contexts, in order to reveal and map their latent spatio-temporal structure. The approach is exemplified to identify geographic regions where many geotagged Twitter messages about a given event have been created, possibly in the same time period in the case of aperiodic event, or at regular timestamps in the case of periodic events.

For hydro-meteorological hazards in mountain areas, the regular inspection of check dams and bridges is important due to the effect of their functional status on water-sediment processes. Moreover, the inspection of these structures is time consuming for organizations due to their extensive number in many regions. However, trained citizen-volunteers can support civil protection and technical services in the frequency, timeliness and coverage of monitoring the functional status of hydraulic structures. Technicians should evaluate and validate these reports to get an index for the status of the structure. Thus, preventive actions could initiate such as the cleaning of obstructions or to pre-screen potential problems for a second level inspection. This study proposes a decision support methodology that technicians can use to assess an index for three parameters representing the functional status of the structure: a) condition of the structure at the opening of the stream flow, b) level of obstruction at the structure and c) the level of erosion in the stream bank. The calculation of the index for each parameter is based upon fuzzy logic theory to handle ranges in precision of the reports and to convert the linguistic rating scales into numbers representing the structure's status. A weighting method and multi-criteria method (Analytic Hierarchy Process- AHP and TOPSIS), can be used by technicians to combine the different ratings according to the component elements of the structure and the completeness of the reports. Finally, technicians can set decision rules based on the worst rating and a threshold for the functional indexes. The methodology was implemented as a prototype web-based tool to be tested with technicians of the Civil Protection in the Fella basin, Northern Italy. Results at this stage comprise the design and implementation of the web-based tool with GIS interaction to evaluate available reports and to set priorities on the inspection of structures.

Trained citizen-volunteers can support civil protection and technical services to inspect the functional status of hydraulic structures. Volunteers carry out first level inspections of bridges and check dams by compiling standardised forms while looking the following parameters: A) Condition of the structure, B) Level of obstruction at the structure and C) Presence of protection works and erosion level in the stream bank. The aim of this work is to support technicians on the evaluation of volunteers' inspections. The output are indexes at parameter level on the functional status of the inspected structure. To that end, this research presents the definition and design of a decision support methodology to be implemented as web-based tool. Technicians evaluate volunteers' inspections at parameter level. First, technicians assign weights to the components questions of the inspection form assisted by the analytical hierarchy process (AHP). From available volunteers' inspections, volunteer ratings are systematically converted into rating scores using fuzzy logic theory. Finally, we used the multi-criteria TOPSIS method to aggregate the weighted-rating scores into indexes for the functional status of the structure. Based on those indexes, technicians can set priorities on the management of hydraulic structures. We established the user requirements based on a case study in the Fella Basin, Friuli-Venezia-Giulia region, Italy. Future research will test the conceptual design with actual technicians of the region for improving the methodology and the functional requirements of the web-based tool.

In this chapter, a soft fusion approach of a set of aligned registered images is described, in which each image represents a distinct theme of the same territory. The objective of the fusion is to evaluate the spatial units of the territory, pixels or cells, by a group of experts through associating to the spatial units scores representing their suitability as best locations with respect to given criteria expressed by soft constraints. The decision process first determines automatically the coherence among a majority of experts' evaluations with respect to each given criterion and then computes the coherent evaluation of the criterion representing the majority opinion. Finally, it computes the overall coherent evaluation with respect to a majority of the criteria that is used to rank the spatial units. The fuzzy majority is defined by linguistic quantifiers; the coherence among a fuzzy majority of experts is defined based on the Minkowski OWA operators while the fuzzy majority of coherent evaluations is modeled by an Induced OWA operator.

Volunteers have been trained to perform first-level inspections of hydraulic structures within campaigns promoted by civil protection of Friuli Venezia Giulia (Italy). Two inspection forms and a learning session were prepared to standardize data collection on the functional status of bridges and check dams. In all, 11 technicians and 25 volunteers inspected a maximum of six structures in Pontebba, a mountain community within the Fella Basin. Volunteers included civil-protection volunteers, geosciences and social sciences students. Some participants carried out the inspection without attending the learning session. Thus, we used the mode of technicians in the learning group to distinguish accuracy levels between volunteers and technicians. Data quality was assessed by their accuracy, precision and completeness. We assigned ordinal scores to the rating scales in order to get an indication of the structure status. We also considered performance and feedback of participants to identify corrective actions in survey procedures. Results showed that volunteers could perform comparably to technicians, but only with a given range in precision. However, a completeness ratio (question/parameter) was still needed any time volunteers used unspecified options. Then, volunteers' ratings could be considered as preliminary assessments without replacing other procedures. Future research should consider advantages of mobile applications for data-collection methods.

Early-Warning Systems (EWS) include the provision of timely and effective information, through identified institutions, that allows individuals exposed to hazard to take action in order to avoid or reduce risk and prepare for effective response. EWS are extensive frameworks that integrate different components of risk governance and disaster risk reduction policies with the main purpose of minimizing loss of life and reducing the economic and social impact of a threatening event on the physical assets and populations exposed to hazards. This section describes and analyzes different types of EWS with the aim to connect scientific advances in hazard and risk assessment with management (emergency preparedness and response) strategies and practical demands of stakeholders and end-users. Besides a structural approach, an Integrated People-Centred EWS (IEWS) is also presented. The system is mainly based on prevention as a key element for disaster risk reduction and aims not only to increase the level of awareness and preparedness of the community and decrease its vulnerability, but also to strengthen institutional collaboration, in particular at a local level, in order to assure sustainability of the efforts in the long term and to strength the risk governance process. In this way, the whole disaster cycle can be covered, trying to apply the most advanced technology available and also making the solutions easier to use by people not accustomed to manage these techniques in their daily tasks.

One of the most interesting aspects of large-scale funding from the European Union over the last 30 years has been the potential to create extensive, international, multidisciplinary research programs that tackle substantial problems facing society. Whilst in the early days, these programs mostly consisted of teams of experienced researchers, more recently, the Marie Curie Intensive Training Network (ITN) schemehas facilitated thedevelopment of teams of early-career researchers: students undertaking doctoral research and those in the immediate postdoctoral phase. These ITNs provide a double benefit, not only permitting large teams of dedicated researchers to tackle in detail an important topic, but also offering the opportunity to develop a group of researchers who have been trained to a very high level in that specific area. The evaluation of risk in high mountains is one such substantial area, and this volume represents the outcome of a recent ITN, the Mountain Risks consortium, which ran from 2007 to 2011, with 14 partners. The program sought to provide research and training on mountain hazards and their associated risks and management strategies. The editors of the book are well known in this field, and most of the papers have been written by a combination of established experts (the supervisors) and earlycareer researchers (the students and postdoctoral researchers). The evaluation of mountain risk is complex. The book is based on a conventional (but almost universally adopted) approach to risk assessment: a simple equation that views risk as the product of the hazard, the vulnerability of the assets in question, and their exposure to the peril. Such an approach is best suited to simple physical assets such as buildings and roads. However, there is increasing concern that this type of approach struggles to deal with the complex nature of risk, especially for remote communities in high mountain areas. Risk in such an environment is a nebulous, multifaceted entity that, critically, is highly dynamic. Thus, the vulnerability of a community depends not only on its physical attributes but also on the degree of social cohesion, from the local to the national level. The risk to a particular community may vary greatly in time, as social conditions and structures change: For example, losses to landslides in Nepal increased substantially during the civil war in the early part of the last decade, when social cohesion collapsed in remote mountain communities. Capturing this in a conventional risk assessment is extremely difficult. Inevitably, the 15 papers are a slightly eclectic mix, but the quality is high. A notable strength is that most draw heavily upon the literature reviews that the doctoral students had undertaken as a part of their theses, providing a very useful oversight of the state of knowledge in each area. Some of the papers examine in some detail specific aspects of mountain risks, such as the use of ground-based interferometric synthetic aperture radar (InSAR) for landslide monitoring, whilst others are more broadly based reviews of key topics. The most interesting passages are those that focus on the vulnerability and exposure aspects of the risk equation, although the approach is generally quite conventional. There are a number of useful chapters that consider how vulnerability and exposure can be built into a quantitative risk evaluation, and on the implications of the outcomes for the management of those risks. Some aspects of the volume are a little frustrating. Critically, most of the hazards that are considered are those associated withmass movements, from individual rockfalls to large-scale landslides. Other mountain risks, such as snow avalanches, glacial lake outbursts, earthquakes, and flash floods, are barely considered. Thus, the book is perhaps slightly poorly titled. The book has a very strong European focus, largely with a western European slant. It is perhaps a shame that more consideration is not given to approaches in, for example, Canada and Japan, both of which have been innovative in this field. Finally, a few of the chapters are disappointingly short. For example, a chapter on lessons learnt from past disasters is potentially very interesting; that the text in the chapter itself, excluding the abstract, references, diagrams, etc, is only 5 pages feels slightly disappointing. In summary, this is a very interesting and useful volume that represents an important contribution to the field. The great strengths are the ways in which the book integrates both natural and social sciences and provides useful reviews of the state of the art in a wide variety of areas of risk assessment and management. Several of the chapters that examine different aspects of risk assessment are very valuable and deserve to be widely read. It will be of interest to both the research and the practitioner communities. However, the most important outcome from the ITN Mountain Risks consortium is probably the development of a large number of early-career professionals with high-level skills in the field of mountain risk. This book is a testament to their knowledge and skills.

Citizen science typically involves trained volunteers on monitoring and management activities to support researchers and risk managers on their understanding of the territory and hazard-related processes. From the technical perspective, survey tools are systematically tested and adapted according to quality requirements and management needs. Moreover, there is an explosive growth in the usage of mobile applications to guide participants and to collect citizen-information. However, the usability of such applications should have more attention in order to collect useful information for decision-making and to address the diversity of citizen volunteers that may get involved. This study aims at the inspection of the functional status of hydraulic structures with trained-citizens. To that end, we are testing a prototype mobile application in the framework of prevention activities promoted by the civil protection and technical services of the Friuli-Venezia-Giulia. Methods include an exercise of data collection and a learning session to guide participants on how to inspect the structures and use the mobile application. In order to validate data collected by volunteers, some participants carry out the inspection without attending the learning session. Then, we get an indication about the usability of the prototype mobile application while looking among other aspects at the effectiveness, memorability and user satisfaction. Finally, we use the evaluation of data collected, general perceptions and potential problems discovered during this study to provide recommendations in order to improve the survey protocols.

Risk assessment is the process of determining the likelihood or threat of a damage, injury, liability, loss, or other negative occurrence that is caused by external or internal vulnerabilities and that may be neutralized through preventive action. More precisely, risk assessment is the systematic prospective analysis aimed at defining, as quantitatively as possible, the potential loss of life, personal injury, economic loss, and property damage resulting from natural and/or anthropogenic hazards, by assessing the exposure and vulnerability of people and property to those hazards. The risk assessment procedure, developed in the Mountain Risks project, is based on the following five steps: (1) Identification and analysis of the specific types of hazards that could affect a territory and its community; (2) Definition of the spatial and temporal likelihood of the damaging events considered in the analysis as well as their magnitude; (3) Inventory of the assets and study of the social and economic features of the study areas; (4) Assessment of vulnerability, evaluating all the hazard consequences for each dimension composing the systems at risk (physical/functional, economic, socio-cultural, ecological/environmental and; political/institutional); (5) Evaluation of the prospective cost of damage or costs avoided through mitigation strategies. Vulnerability assessment plays a crucial role both in ‘translating’ the assessed level of hazard into an estimated level of risk and in providing leading information in mitigation planning processes and emergency management strategies. Under this perspective, it is really difficult, or even impossible, to address risk assessment without assessing vulnerability first and it appears unquestionable that a multi-disciplinary approach is required in vulnerability assessment studies. In this section, the different components (dimensions) of vulnerability are analyzed, both theoretically and practically, and then different methodological approaches, applications and solutions are provided.

The Caribbean region has been identified as one of the most hazard prone region, being vulnerable to tropical storms andfloods. This vulnerability is highlighted by the recent R3i project reporting that over the period 1846-1978, thirty four percent (34%) of all recorded disasters occurred in the Latin America-Caribbean region, and caused over one million deaths.The sustainable development of the region is therefore under constant threat of being eroded from the occurrence of one or more of these hazards. These events have the capacity to retard the development process by destroying the wealth created over time. The current research examines the basic requirements for the structural and operational vulnerability assessmentof the built environmentand infrastructure to hazards through the element at risk delineation and the estimate of potential losses. Starting from the regional Caribbean islands scale,with a specific downscaling focus on the Cayman Islands the expert knowledge,based onthe Source-Pathway-Receptor-Consequence (S-P-R-C) framework of critical facilities, the research develops an appliedprogram designed to keep managersinvolved within aGIS-based probabilistic approach to support effective vulnerability assessment enabling operational resilience of the local communities. Based on three steps, the study develops a risk assessment for human use and critical facilities for different coastal zones subject to multiple hazards. Ranges of ready-to-use GIS methodologies are considered, including element at risk calculation (tabulate analysis of critical infrastructure), estimation of loss (such as vulnerability curves) and operational vulnerability analysisto identify potential impacts. The three step exercise has been successfully tested with training activities and it can be considered a valuable tool to build capacities for decision-makers and to analyze current and future vulnerability levels to natural and anthropogenichazard. It would support hazard management offices and related territorial planning infrastructure in preparedness, response, rescue and recovery strategies implementation.

Forecasting natural disasters, as wildfires or floods, is a mandatory activity to reduce the level of risk and damage to people, properties and infrastructures. Since estimating real-time the susceptibility to a given phenomenon is computationally onerous, susceptibility maps are usually pre-computed. So, techniques are needed to efficiently query such maps, in order to retrieve the most plausible scenario for the current situation. We propose a flexible querying and answering framework by which the operator, in charge of managing an ongoing disaster, can retrieve the list of susceptibility maps in decreasing order of satisfaction with respect to the query conditions. The operator can also describe trends of the conditions that are related with environmental parameters, assessing what happens if a dynamic parameter is increasing or decreasing in value.

A review of the current emergency response strategies at European level was carried out from the key conclusions and recommendations identified within past experiences of developed platform for civil protection and emergency management. The review is focused on the recent research initiatives funded by the European Community about methods and techniques for emergency response in case of flooding and debris flow risks, mainly. The ultimate aim is to reduce the fragmentation of the research on hydro-meteorological hazards so to possibly identify a best practice on disaster risk management. This paper is organized as follows, first a brief overview on emergency response strategies is presented along with an analysis of the challenges that arisen while implementing the following tools: geo-information and remote sensing, emergency plans, early warning and decision support systems. Then, conclusions have been drawn.

A previously studied spatial database for representing landslide susceptibility is reanalyzed to bring out aspects of relative quality and uncertainty of the resulting prediction pattern. Using the empirical likelihood ratio function, a processing strategy, based on cross-validation, target pattern and prediction rates, is used to asses the suitability of the database for prediction modeling. Sharing of databases is seen as fundamental for discussing and understanding prediction modeling.

This paper presents the results of a research concerning available historical information about natural hazards (landslides and floods) and consequent disasters in the Consortium of Mountain Municipalities of Valtellina di Tirano, in Northern Italy. A geo-referenced database, collecting information till 2008, was designed with the aim of using available data of historical events for hazard estimation and the definition of risk scenarios as a basis for Civil Protection planning and emergency management purposes. This database and related statistics about landslides and floods are shown and a brief overview of historical disasters caused by natural hazards in the study area is presented. A case study showing how useful the database could be to define a simple but realistic scenario is described. Information availability and reliability is discussed and possible uncertainties are underlined. The study shows that collecting and making use of historical information for the definition of hypothetical scenarios and the evaluation of territorial threats is a fundamental source of knowledge to deal with future emergencies.

Natural and anthropogenic factors are identified as critical in characterizing aquifer vulnerability in the Milan Province study area, where the impact of elevated concentrations of NO3 - is being assessed. In this contribution, map versions of continuous and categorical data layers are used to establish relationships between map units and the location of 305 water wells with nitrate levels either clearly above a threshold of 25 mg/l (impacted wells), or with wells clearly below that (non-impacted wells). The natural and anthropogenic data layers that are assumed to reflect (a) potential sources of nitrate, and (b) the relative ease with which nitrate may migrate in groundwater, are: population density, nitrogen fertilizer loading, precipitation and irrigation, the protective capacity of soils, land use, vadose zone permeability, groundwater depth, and groundwater velocity. The water wells are separated first into the two groups to locate and recognize sites to be used to map high vulnerabilities using a prediction model based on the empirical likelihood ratio, ELR. Further partitions of the two sub-groups into prediction and validation wells allows setting up blind tests to cross-validate the predictions of relative vulnerability classes (ranks). Prediction-rate tables are obtained and visualized either as histograms or as cumulative proportions of the study area in decreasing order of predicted vulnerability class versus the corresponding relative proportion of impacted validation wells, i.e., not used to predict. Predictions are thus compared and interpreted and repeated predictions are obtained using different sub-sets of prediction and validation wells in the two regions to obtain maps of uncertainty of the prediction classes. The target of the strategy used is not only to assess the goodness of predictions but also to estimate their reliability levels. In this application the uncertainty of the classes in the prediction map happens to be relatively high, which is due to the small number of water wells available in the spatial database.

Statistical techniques can be used in groundwater pollution problems to determine the relationships among observed contamination (impacted wells representing an occurrence of what has to be predicted), environmental factors that may influence it and the potential contamination sources. Determination of a threshold concentration to discriminate between impacted or non impacted wells represents a key issue in the application of these techniques. In this work the effects on groundwater vulnerability assessment by statistical methods due to the use of different threshold values have been evaluated. The study area (Province of Milan, northern Italy) is about 2000 km2 and groundwater nitrate concentration is constantly monitored by a net of about 300 wells. Along with different predictor factors three different threshold values of nitrate concentration have been considered to perform the vulnerability assessment of the shallow unconfined aquifer. The likelihood ratio model has been chosen to analyze the spatial distribution of the vulnerable areas. The reliability of the three final vulnerability maps has been tested showing that all maps identify a general positive trend relating mean nitrate concentration in the wells and vulnerability classes the same wells belong to. Then using the kappa coefficient the influence of the different threshold values has been evaluated comparing the spatial distribution of the resulting vulnerability classes in each map. The use of different threshold does not determine different vulnerability assessment if results are analyzed on a broad scale, even if the smaller threshold value gives the poorest performance in terms of reliability. On the contrary, the spatial distribution of a detailed vulnerability assessment is strongly influenced by the selected threshold used to identify the occurrences, suggesting that there is a strong relationship among the number of identified occurrences, the scale of the maps representing the predictor factors and the model efficiency in discriminating different vulnerable areas.

International audience; Statistical assessment of landslide susceptibility has become a major topic of research in the last decade. Most progress has been accomplished on producing susceptibility maps at meso-scales (1:50,000–1:25,000). At 1:10,000 scale, which is the scale of production of most regulatory landslide hazard and risk maps in Europe, few tests on the performance of these methods have been performed. This paper presents a procedure to identify the best variables for landslide susceptibility assessment through a bivariate technique (weights of evidence, WOE) and discusses the best way to minimize conditional independence (CI) between the predictive variables. Indeed, violating CI can severely bias the simulated maps by over- or under-estimating landslide probabilities. The proposed strategy includes four steps: (i) identification of the best response variable (RV) to represent landslide events, (ii) identification of the best combination of predictive variables (PVs) and neo-predictive variables (nPVs) to increase the performance of the statistical model, (iii) evaluation of the performance of the simulations by appropriate tests, and (iv) evaluation of the statistical model by expert judgment. The study site is the north-facing hillslope of the Barcelonnette Basin (France), affected by several types of landslides and characterized by a complex morphology. Results indicate that bivariate methods are powerful to assess landslide susceptibility at 1:10,000 scale. However, the method is limited from a geomorphological viewpoint when RVs and PVs are complex or poorly informative. It is demonstrated that expert knowledge has still to be introduced in statistical models to produce reliable landslide susceptibility maps.

The development of specialist software for 3D visualisation (e.g. Lynx, g0cad, Earth Vision) permits reconstruction of complex geological bodies. Examples have been published concerning studies based on boreholes and geophysics, but few have concerned mapped structural bodies (de Kemp, 2000).

This paper is focused primarily on how to represent landslide scarp areas, how to analyze results achieved by the application of specific strategies of representation and how to compare the outcomes derived by different tests, within a general framework related to landslide susceptibility assessment. These topics are analyzed taking into account the scale of data survey (1:10,000) and the role of a landslide susceptibility map into projects targeted toward the definition of prediction, prevention, and mitigation measures, in a wider context of civil protection planning. These aims are achieved by using ArcSDM (Arc Spatial Data Modeler), a software extension to ArcView GIS useful for developing spatial prediction models using regional datasets. This extension requires a representation by points of the investigated problems (landslide susceptibility, aquifer vulnerability, detection of mineral deposits, identification of natural habitats of animals, and plants, etc.). Maps of spatial evidence from regional geological and geomorphological datasets were used to generate maps showing susceptibility to slope failures in two different study areas, located in the northern Apennines and in the central Alps (Italy), respectively. The final susceptibility maps for both study areas were derived by the application of the weights-of-evidence (WofE) modeling technique. By this method a series of subjective decisions were required, strongly dependent on an understanding of the natural processes under study, supported by statistical analysis of the spatial associations between known landslides and evidential themes. Except for maps of attitude, permeability, and structure, that were not available for both study areas, the other data were the same and comprised geological, land use, slope, and internal relief maps. The paper illustrates how different representations of scarp areas by points (in terms of different number of points) did not greatly influence the final response map, considering the scale of this work. On the contrary, some differences were observed in the capability of the model to describe the relations between predictor variables and landslides. In effect, a representation of the scarp areas using one point every 50 m led to a more efficient model able to better define relationships of this type. It avoided both problems of redundancy of information, deriving by the use of too many points, and problems related to a random positioning of the centroid. Moreover, it permitted to minimize the uncertainty related with identification and mapping of landslides.

The weights of evidence (WofE) modeling technique has been used to analyze both natural and anthropogenic factors influencing the occurrence of high nitrate concentrations in groundwater resources located in the central part of the Po Plain (Northern Italy). The proposed methodology applied in the Lodi District combines measurements of nitrate concentrations, carried out by means of a monitoring net of 69 wells, with spatial data representing both categorical and numerical variables. These variables describe either potential sources of nitrate and the relative ease with which it may migrate towards groundwater. They include population density, nitrogen fertilizer loading, groundwater recharge, soil protective capacity, vadose zone permeability, groundwater depth, and saturated zone permeability. Once conditional dependence problems among factors have been solved and validation tests performed, the statistical approach has highlighted negative and positive correlations between geoenvironmental factors and nitrate concentration in groundwater. These results have been achieved analysing the calculated statistical parameters (weights, contrasts, normalized contrasts) of each class by which each factor has been previously subdivided. This has permitted to outline: the overall influence each factor has on the presence/ absence of nitrate; the range of their values mostly influencing this presence/absence; the most and least critical combination of factor classes existing in each specific zone; areas where the influence of impacting factor classes is reduced by the presence of not impacting factor classes. This last aspect could represent an important support for a correct land use management to preserve groundwater quality.

This study involves the integration of multiple thematic datasets for landslide susceptibility assessment through spatial prediction models. The proposed methodology has been applied in the Bystrzanka-Biczyska area (Beskid Niski Mts., Carpathians Mts., Poland), characterised by a very high density of landslides. The susceptibility assessment has been based on an indirect bivariate statistical analysis ("Weights of Evidence" modelling technique, Bonham-Carter et al., 1989) performed in order to predict the occurrence of an event (landslide) where well-known evidences (predictor variables) are available. According to the relative importance of each evidence, a landslide susceptibility map has been produced. Observing final prediction results, it is concluded that the susceptibility map gives useful information both on present instability of the area and its possible future evolution in agreement with the morphological evolution of the area.

This study involves the integration of multiple thematic data sets for landslide susceptibility assessment through spatial prediction models, at different scale levels. The proposed methodology has been applied in the Oltrepo Pavese area, characterized by a very high density of landslides, mainly surficial slope failures triggered by intense rainfall events. Firstly, the susceptibility assessment has been based on an indirect bivariate statistical analysis performed in order to evaluate the relative weights of each factor contributing to the instability. Among the susceptible areas predicted by the statistical analysis, a test site was selected to apply the infinite slope model, using data derived from the application of a groundwater numerical model for unsaturated and saturated conditions on selected slope profiles. All the applied models have been integrated and implemented in a raster-based geographical information system (GIS), giving useful information both on present instability of the area and its possible future evolution.

Landslide hazard assessment aims at profiling the prospective damaging events and identifying the potentially affected areas to reduce risk. This means that procedures for the management of emergency situations can be effectively set up in advance. Where and when a potentially damaging event will occur is one of the most important topics of discussion in the scientific community. As a matter of fact, landslide area will contribute to identify the vulnerable elements potentially affected by the event of a given magnitude and the degree of physical damage and the economic loss. Knowledge of the area and the time of occurrence, related to the triggering factors, permits to apply in advance an «early warning» system and related procedures to manage the crisis phase, according to Civil Protection Authorities and the laws in force. An example of the proposed methodology is presented; it exploits GIS techniques, Decision Support System, and mobile technologies to reduce landslide risk in the Corvara in Badia test site.

This study describes an analysis carried out within the European community project "ALARM" (Assessment of Landslide Risk and Mitigation in Mountain Areas, 2004) on landslide risk assessment in the municipality of Corvara in Badia, Italy. This mountainous area, located in the central Dolomites (Italian Alps), poses a significant landslide hazard to several man-made and natural objects. Three parameters for determining risk were analysed as an aid to preparedness and mitigation planning: event occurrence probability, elements at risk, and the vulnerability of these elements. Initially, a landslide hazard scenario was defined; this step was followed by the identification of the potential vulnerable elements, by the estimation of the expected physical effects, due to the occurrence of a damaging phenomenon, and by the analysis of social and economic features of the area. Finally, a potential risk scenario was defined, where the relationships between the event, its physical effects, and its economic consequences were investigated. People and public administrators with training and experience in local landsliding and slope processes were involved in each step of the analysis. A "cause-effect" correlation was applied, derived from the "dose-response" equation initially used in the biological sciences and then adapted by economists for the assessment of environmental risks. The relationship was analysed from a physical point of view and the cause (the natural event) was correlated to the physical effects, i.e. the aesthetic, functional, and structural damage. An economic evaluation of direct and indirect damage was carried out considering the assets in the affected area (i.e., tourist flows, goods, transport and the effect on other social and economic activities). This study shows the importance of indirect damage, which is as significant as direct damage. The total amount of direct damage was estimated in 8 913 000 €; on the contrary, indirect damage ranged considerably from 2 840 000 to 9 350 000 €, depending on the selected temporal scenario and the expected closing time of the potentially affected structures. The multi-disciplinary approach discussed in this study may assist local decision makers in determining the nature and magnitude of the expected losses due to a dangerous event, which can be anticipated in a given study area, during a specified time period. Besides, a preventive knowledge of the prospective physical effects and economic consequences may help local decision makers to choose the best prevention and mitigation options and to decide how to allocate resources properly, so that potential benefits are maximised at an acceptable cost.

In order to model the distribution of areas where groundwater resources are susceptible to nitrate contamination , the data-driven Weights of Evidence (WofE) and Weighted-Logistic Regression (WLR) methods were used. Using this couple of techniques, different tests were performed considering various combinations of predictor factors, deriving posterior probability maps (probability that a unit area contains a training point). In order to establish the best model, success rate curve and prediction rate curve were calculated. At the end simple statistical techniques were then used to individuate the best model between two tests that showed very similar values of prediction rate. This last test could be useful to determine the distribution of the probabilities to find wells with different values of nitrate concentration. Comparison between simulation shows the best performance of Weights of Evidence.