1,721,029 research outputs found
Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice
No full textDue to a combination of climate change and urbanization, the instances of pluvial flooding are expected to in-crease in the next decades posing raising threats to properties, people and productive assets. Predicting and mapping pluvial flood-prone areas is becoming a crucial step in flood mitigation and early warnings, as well as climate change adaptation strategies, to be incorporate in urban planning. Most commonly applied machine learning (ML) procedures for pluvial flood risk assessment, neglect to account for spatio-temporal constraints, leading to overoptimistic models that underestimate the prediction error. In this paper, we propose a novel ML -based methodology for pluvial flood risk prediction in the Metropolitan City of Venice which, introducing a features selection process and spatio-temporal cross-validation, permits to reduce overfitting of the resulting ML models. Spatio-temporal characteristics of floods are derived from a dataset of 60 historical events occurred in the area between 1995 and 2020. Logistic Regression (LR), Neural Networks (NN) and Random Forest (RF) models are applied to identify and prioritize sub-areas that are more likely to be affected by pluvial flood risk, considering the daily precipitation amount and 12 different triggering factors. The models were validated using Random Cross-Validation (R-CV) and Leave Location and Time Out cross-validation (LLTO-CV), that split data in training and validation set considering both time and space. In addition, a forward features selection procedure was applied to identify the features, among the triggering factors, that better face spatio-temporal overfitting in pluvial flood prediction based on the Area Under the Curve (AUC) score. Results suggest that Logistic Regression and LLTO-CV represent the most reliable model to predict pluvial flood events in new spatio-temporal conditions, while, among the triggering factors, distance to river and distance to road resulted the prominent ones
Egypt's Coastal Vulnerability to Sea-Level Rise and Storm Surge: Present and Future Conditions
No full textWe assess the relative vulnerability of the Mediterranean shoreline of Egypt (about 1000 km in length) to climate change (i.e., sea-level rise [SLR], storm surge flooding, and coastal erosion) by using a Climate-improved Coastal Vulnerability Index (CCVI). We integrate information relative to a multidimensional set of physical, geological, and socioeconomic variables, and add to the mainstream literature the consideration of both a reference and a climate change scenario, assuming the representative concentration pathway 8.5 W/m2 (RCP8.5) for the 21st century in the Mediterranean region. Results report that approximately 1% (~43 km2) of the mapped shoreline is classifiable as having a high or very high vulnerability, whereas approximately 80% (4652 km2) shows very low vulnerability. As expected, exposure to inundation and erosion is especially relevant in highly developed and urbanized coastal areas. Along the shoreline, while the Nile Delta region is the most prone area to coastal erosion and permanent or occasional inundations (both in the reference and in the climate scenario), results show the Western Desert area to be less vulnerable due to its geological characteristics (i.e., rocky and cliffed coasts, steeper coastal slope). The application of the CCVI to the coast of Egypt can be considered as a first screening of the hot-spot risk areas at the national scale. The results of the analysis, including vulnerability maps and indicators, can be used to support the development of climate adaptation and integrated coastal zone management strategies. Integr Environ Assess Manag 2020;16:761–772. © 2020 SETAC
Multi-scenario analysis in the Adriatic Sea: A GIS-based Bayesian network to support maritime spatial planning
No full textOceans are changing faster than even observed before. Unprecedented climate variability is interacting with long-term trends, all against a backdrop of rising anthropogenic use of marine space. The growth of maritime activities is taking place without the full understanding of complex interactions between natural and human-induced changes, leading to a progressive decline of biodiversity and degradation of marine ecosystems. Against this complex interplay, marine managers and policy makers are increasingly calling for new approaches and tools allowing a multi-scenario assessment of environmental impacts arising from the complex interaction between natural and anthropogenic drivers, also in consideration of multiple marine plans objectives. Responding to this need, for the Adriatic Sea we developed a GIS-based Bayesian Network to evaluate the probability (and related uncertainty) of cumulative impacts under four ‘what-if’ scenarios representing different marine management options and climate conditions. We addressed issues concerning consequences of potential planning measures, as well as management programmes required to achieve environmental status targets, as required by relevant EU acquis. Results from the scenario analysis highlighted that an integrated approach to maritime spatial planning is required, combining more sustainable management options of marine spaces and resources with climate adaptation strategies. This approach to planning would allow to reduce human pressures on the marine environment and rise resilience of natural ecosystems to climate and human-induced disturbances, which would result in an overall decrease of cumulative impacts
Stochastic system dynamics modelling for climate change water scarcity assessment of a reservoir in the Italian Alps
Full text linkWater management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevant for mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Here stochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows in the Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulations into a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volume within the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow and volume as well as implementing a set of metrics to analyse volume extreme conditions.Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021-2050) future reductions for stored volume and turbined outflow are expected to be severe compared to the 14-year baseline (1999-2004 and 2009-2016; -24.9 % of turbined outflow and -19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041-2070; -26.2 % of turbined outflow and -20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially in the last part of the 2041-2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflow only), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021-2050. Reductions are persistently occurring for the rest of the year from April to November for turbined outflows (down to -56.3 % in August) and from May to December for stored volume (down to -44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest a general increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90th percentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volume values below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baseline conditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption of adaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods for water accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results and methodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g. hydropower, agriculture and tourism) and prepare for potential multi-risk conditions
The Temporal and Spatial Distribution Characteristics of Air Pollution Index and Meteorological Elements in Beijing, Tianjin, and Shijiazhuang, China
No full textWith rapid economic development and continuous population growth, several important cities in China suffer serious air pollution, especially in the Beijing–Tianjin–Hebei economic developing area. Based on the daily air pollution index (API) and surface meteorological elements in Beijing, Tianjin, and Shijiazhuang (the capital of Hebei province) from 2001 to 2010, the relationships between API and meteorological elements were analyzed. The statistical analysis focused on the relationships at seasonal and monthly average scales, on different air pollution grades and air pollution processes. The results revealed that the air pollution conditions in the 3 areas gradually improved from 2001 to 2010, especially during summer; the worst conditions in air quality were recorded in Beijing in spring due to the influences of dust, and in Tianjin and Shijiazhuang in winter due to household heating. Meteorological elements exhibited different influences on air pollution, showing similar relationships between API in monthly averages and 4 meteorological elements (i.e., the average, maximum, and minimum temperatures; maximum air pressure; vapor pressure; and maximum wind speed), whereas the relationships on a seasonal average scale demonstrated significant differences. Compared with seasonal and monthly average scales of API, the relation coefficients based on different air pollution grades were significantly lower, whereas the relationship between API and meteorological elements based on air pollution processes reduced the smoothing effect due to the average processing of seasonal and monthly API and improved the accuracy of the results. Finally, statistical analysis of the distribution of pollution days in different wind directions indicated the directions of extreme and maximum wind speeds that mainly influence air pollution, representing valuable information that could support the definition of air pollution control strategies through the identification of the regions (and the located emission sources) where the implementation of emission reduction actions should be focused. Integr Environ Assess Manag 2018;14:710–721. © 2018 SETAC
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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