1,720,971 research outputs found
Use of the Conduit Flow Process for the simulation of passive mitigation measures against the piezometric damming effect at the new underground High Speed railway station of Florence
No full textThe new High Speed railway station of the city of Florence will be located below ground level inside a huge excavation, 25 m b.g.s deep, 450 m long and 50 m wide; the pit, already built, is surrounded by concrete cutoff walls causing a piezometric damming effect against the main aquifer of the Arno River. As permanent mitigation measure against the piezometric mounding, the design of the station considers a series of drain pipes drilled horizontally on up and down gradient sides of the pit and connected by blind wall tubes. A numerical finite differences model has been implemented with the Modflow code, in order to quantify the expected damming effect and to properly design the drain-based mitigation measures (i.e. frequency and length of the drains). One of the major challenges of the numeric approach was the coupling between the groundwater flow in the porous medium and the water flow towards and through the drains and the blind wall tubes connecting the sides of the excavation. Thus, the drains were simulated using a recently published Modflow Process originally developed for karst conduits (Conduit Flow Process - CFP), which allows combining the groundwater flow in the porous medium with laminar or turbulent flow into pipes. In order to guarantee an acceptable head differential between both sides of the excavation, the results showed that the system should permit a discharge flow rate of about 0.026 m3/s, considering a hydraulic gradient equal to 0.5% (i.e. hydraulic gradient from the averaged piezometric distribution before pit excavation). Conductance turned out to be the most influent parameter towards the effectiveness of the drainage system
A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data
Full text linkCash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data—such as municipality-level records of crop seeding—for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using “good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task
Flood impacts beyond the direct and physical ones: the case of the Po catchment in Italy
No full textINSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis
Full text linkMethodologies to estimate economic flood damages
are increasingly important for flood risk assessment and
management. In this work, we present a new synthetic flood
damage model based on a component-by-component analysis
of physical damage to buildings. The damage functions
are designed using an expert-based approach with the support
of existing scientific and technical literature, loss adjustment
studies, and damage surveys carried out for past flood events
in Italy. The model structure is designed to be transparent
and flexible, and therefore it can be applied in different geographical
contexts and adapted to the actual knowledge of
hazard and vulnerability variables.
The model has been tested in a recent flood event in northern
Italy. Validation results provided good estimates of postevent
damages, with similar or superior performances when
compared with other damage models available in the literature.
In addition, a local sensitivity analysis was performed
in order to identify the hazard variables that have more influence
on damage assessment results
Opportunities and Risks of Disaster Data from Social Media: A Systematic Review of Incident Information
Full text linkCompiling and disseminating information about incidents and disasters is key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether, and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 large disaster and incident databases covering 27 incident types, organize the contained data and its collection process, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: impact assessment and verification of model predictions, narrative generation, enabling enhanced citizen involvement, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance. Aside from this analysis, we discuss the advantages and disadvantages of the use of social media data for closing information gaps related to incidents and disasters
Review article: Detection of actionable tweets in crisis events
Full text linkMessages on social media can be an important source of information during crisis situations. They can frequently provide details about developments much faster than traditional sources (e.g., official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks.
One challenge for utilizing social media in crisis situations is the reliable detection of relevant messages in a flood of data. Researchers have started to look into this problem in recent years, beginning with crowdsourced methods. Lately, approaches have shifted towards an automatic analysis of messages. A major stumbling block here is the question of exactly what messages are considered relevant or informative, as this is dependent on the specific usage scenario and the role of the user in this scenario.
In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter. We start by showing the varying definitions of importance and relevance relating to disasters, leading into the concept of use case-dependent actionability that has recently become more popular and is the focal point of the review paper. This is followed by an overview of existing crisis-related social media data sets for evaluation and training purposes. We then compare approaches for solving the detection problem based (1) on filtering by characteristics like keywords and location, (2) on crowdsourcing, and (3) on machine learning technique. We analyze their suitability and limitations of the approaches with regards to actionability. We then point out particular challenges, such as the linguistic issues concerning social media data. Finally, we suggest future avenues of research and show connections to related tasks, such as the subsequent semantic classification of tweets
Bayesian rainfall thresholds for flash flood guidance
No full textThe aim of this work is to determine the FFG rainfall depth and compute FFG-values based on the minimization of a Bayesian Loss Function of the discharge conditional upon the state of saturation of the catchment. Rainfall thresholds are here defined as the cumulated volume of rainfall during a storm event which can generate a critical water stage (or discharge) at a specific river section. When the rainfall threshold value is exceeded, the likelihood that the critical river level (or discharge) will be reached is high and consequently it becomes appropriate to issue a flood alert; alternatively, no flood alert is going to be issued when the threshold level is not reached. In other words the rainfall thresholds must incorporate a “convenient” dependence between the cumulated rainfall volume during the storm duration and the possible consequences on the water level or discharge in a river section. The term “convenient” is here used according to the meaning of the decision theory under uncertainty conditions, namely the decision which corresponds to the minimum (or the maximum) expected value of a Bayesian cost utility function. There are described two possible approaches for the same methodology: (a) using the Monte-Carlo simulations or (2) using the Normal Quantile Transform. The main difference of the two is the requirements in terms of data, i.e. the timeseries of rainfall and discharge. Application of the methodology and comparison with other methodologies are provided for the Posina catchment in Italy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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