Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

Nova metodologija za procenu šteta usled plavljenja urbanih površina

Urban flooding caused by extreme rainfall events is becoming considerably more frequent and more destructive. Thus, enhanced models to predict accurately flood magnitude and location are of paramount importance. These models can then be used for urban planning, flood forecasting, flood management (real-time control, raise of flood alerts (emergency services management, etc.) and, ultimately, to estimate flood damage assessment. This paper demonstrates the capability of the Automatic Overland Flow Delineation (AOFD) methodology developed by the authors for flood damage estimation in urban areas. Properties in risk of flood are identified based on a spatial analysis, using the locations of flood - prone areas (ponds) and the location of buildings. The results obtained in this study open new research directions to estimate flood damage with even more detail, and extend flood damage estimation beyond property level, i.e. considering also traffic disruption, health issues and alike.Plavljenja urbanih površina usled jakih pljuskova postaje sve češće i opasnije. Zbog toga je neophodno raspolagati sa kvalitetnim modelom koji može predvideti intenzitet i lokaciju plavljenja. Takav model se može koristiti za urbanistička planiranja, predviđanje poplava i šteta usled poplava, kao i za upozorenja usled očekivanih poplava. U ovom radu se istražuje mogućnost primene metodologije za automatsku delineaciju površinskih tokova za procenu šteta u urbanim površinama. Objekti koji se plave se određuju na osnovu prostorne analize, koristeći rezultate analiza depresija na urbanim površinama. Dobijeni rezultati u ovom radu otvaraju nove oblasti za istraživanje: uticaj bolje prostorne rezolucije na proračuna šteta, i uticaj poplava na saobraćaj, zdravlje ljudi i slično

Comparison Of Statistical Failure Models To Support Sewer System Operation

Currently, achieving appropriate operative performance of water infrastructure has become a high priority in urbanized areas. Particularly, providing reliable sewerage service is central for human well-being and its development (Kleidorfer, et al. 2013). Having that wastewater system management is an increasingly complex task due to a number of hardly predictable factors (e.g. deterioration of system components and climate variability), recent research efforts have been focusing on developing methods to identify optimum proactive rehabilitation and maintenance strategies, some of which are based on the identification of the sewerage structures in most need of attention. To meet such a goal, different forecast failure models for urban water infrastructure have been recently developed. These models are able to assess the future behavior of water supply and sewer system structures. This study presents the comparison of two different failure statistical packages for urban water systems: (a) The FAIL software that calculates failure predictions based on two alternative stochastic processes, the single-variate Poisson process and the Linear Extended Yule process (LEYP) (see Martins et al., 2013) and (b) The SIMA software that, trough out a series of statistical tests, selects a failure model that is based either on an homogeneous Poisson process (HPP), a renewal process or a non-homogeneous Poisson process (NHPP), which allows changes of trend in the failure intensity (see Rodríguez, et al. 2012). Those different statistical models are applied to two contrasting urban wastewater systems: Bogotá (Colombia, 7.5 million inhabitants) and Oeiras e Amadora (Portugal, 10.000 inhabitants). Customer complaints and failure databases were gathered in order to analyze two different types of sewer failures named sediment-related blockages and structural failures. Multiple analyses are carried out in order to assess the impact of sewer system characteristics, system complexity, spatial resolution and data availability onto models forecasting efficiency

Influence of storm drain inlet locations on urban pluvial flooding hazard at local scale

The assessment of the impact of surface drainage conditions and the related effect on urban flooding is the general aim of the present research study. Aim of the work presented here is to assess the impact of surface drainage conditions and the related effect on urban flooding. The main objective is to analyze the surface drainage efficiency by evaluating the influence of storm drain inlet location on pluvial flooding. In this study the FLURB-2D propagation model has been used, a two-dimensional inertial model based on the Saint Venant equations and it was, originally, developed with a different purpose. This study focuses on the impact of surface drainage system, in terms of positioning, number, on pluvial flood hazard, actually, four different hypothetical scenarios for the location of the drain inlets were considered. The methodological approach presented in this study is applied in a real case study in the town of Messina (Italy) The area is, entirely, densely urbanized, with streets and blocks with limited pervious parts. The drainage system is mainly separated from the sewer system and there is no stormwater drainage system

Nova metodologija za procenu šteta usled plavljenja urbanih površina

Urban flooding caused by extreme rainfall events is becoming considerably more frequent and more destructive. Thus, enhanced models to predict accurately flood magnitude and location are of paramount importance. These models can then be used for urban planning, flood forecasting, flood management (real-time control, raise of flood alerts (emergency services management, etc.) and, ultimately, to estimate flood damage assessment. This paper demonstrates the capability of the Automatic Overland Flow Delineation (AOFD) methodology developed by the authors for flood damage estimation in urban areas. Properties in risk of flood are identified based on a spatial analysis, using the locations of flood - prone areas (ponds) and the location of buildings. The results obtained in this study open new research directions to estimate flood damage with even more detail, and extend flood damage estimation beyond property level, i.e. considering also traffic disruption, health issues and alike.Plavljenja urbanih površina usled jakih pljuskova postaje sve češće i opasnije. Zbog toga je neophodno raspolagati sa kvalitetnim modelom koji može predvideti intenzitet i lokaciju plavljenja. Takav model se može koristiti za urbanistička planiranja, predviđanje poplava i šteta usled poplava, kao i za upozorenja usled očekivanih poplava. U ovom radu se istražuje mogućnost primene metodologije za automatsku delineaciju površinskih tokova za procenu šteta u urbanim površinama. Objekti koji se plave se određuju na osnovu prostorne analize, koristeći rezultate analiza depresija na urbanim površinama. Dobijeni rezultati u ovom radu otvaraju nove oblasti za istraživanje: uticaj bolje prostorne rezolucije na proračuna šteta, i uticaj poplava na saobraćaj, zdravlje ljudi i slično

Enhanced DEM-based flow path delineation methods for urban flood modelling

In order to simulate surface runoff and flooding, one-dimensional (1D) overland flow networks can be automatically delineated using digital elevation models (DEM). The resulting network comprises flow paths and terrain depressions/ponds and is essential to reliably model pluvial (surface) flooding events in urban areas by so-called 1D/1D models. Conventional automatic DEM-based flow path delineation methods have problems in producing realistic overland flow paths when detailed high-resolution DEMs of urban areas are used. The aim of this paper is to present the results of research and development of three enhanced DEM-based overland flow path delineation methods; these methods are triggered when the conventional flow path delineation process stops due to a flow obstacle. Two of the methods, the 'bouncing ball and buildings' and 'bouncing ball and A*' methods, are based on the conventional 'bouncing ball' concept; the third proposed method, the 'sliding ball' method, is based on the physical water accumulation concept. These enhanced methods were tested and their results were compared with results obtained using two conventional flow path delineation methods using a semi-synthetic test DEM. The results showed significant improvements in terms of the reliability of the delineated overland flow paths when using these enhanced methods

An extensive reef system at the Amazon River mouth

Large rivers create major gaps in reef distribution along tropical shelves. The Amazon River represents 20% of the global riverine discharge to the ocean, generating up to a 1.3 x 10(6)-km(2) plume, and extensive muddy bottoms in the equatorial margin of South America. As a result, a wide area of the tropical North Atlantic is heavily affected in terms of salinity, pH, light penetration, and sedimentation. Such unfavorable conditions were thought to imprint a major gap in Western Atlantic reefs. We present an extensive carbonate system off the Amazon mouth, underneath the river plume. Significant carbonate sedimentation occurred during lowstand sea level, and still occurs in the outer shelf, resulting in complex hard-bottom topography. A permanent near-bottom wedge of ocean water, together with the seasonal nature of the plume's eastward retroflection, conditions the existence of this extensive (similar to 9500 km(2)) hard-bottom mosaic. The Amazon reefs transition from accretive to erosional structures and encompass extensive rhodolith beds. Carbonate structures function as a connectivity corridor for wide depth-ranging reef-associated species, being heavily colonized by large sponges and other structure-forming filter feeders that dwell under low light and high levels of particulates. The oxycline between the plume and subplume is associated with chemoautotrophic and anaerobic microbial metabolisms. The system described here provides several insights about the responses of tropical reefs to suboptimal and marginal reef-building conditions, which are accelerating worldwide due to global changes.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Coordenadoria de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERS)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)BrasoilMCTIBrazilian NavyU.S. NSFGordon and Betty Moore Foundation (GBMF)Univ Fed Rio de Janeiro UFRJ, Inst Biol, BR-21941599 Rio De Janeiro, RJ, BrazilUniv Fed Rio de Janeiro, COPPE, Inst Alberto Luiz Coimbra Posgrad & Pesquisa Engn, Lab Sistemas Avancados Gestao Prod, BR-21941972 Rio de Janeiro, RJ, BrazilInst Pesquisas Jardim Bot Rio de Janeiro, BR-22460030 Rio De Janeiro, RJ, BrazilUniv Sao Paulo, Inst Oceanog, BR-05508120 Sao Paulo, SP, BrazilUniv Fed Espirito Santo, Dept Oceanog, BR-29199970 Vitoria, ES, BrazilUniv Estadual Norte Fluminense, Lab Ciencias Ambientais, Ctr Biociencias & Biotecnol, BR-28013602 Campos Dos Goytacazes, RJ, BrazilUniv Fed Fluminense, Inst Geociencias, BR-24210346 Niteroi, RJ, BrazilUniv Fed Fluminense, Inst Biol, BR-24210130 Niteroi, RJ, BrazilUniv Fed Rio de Janeiro, Museo Nacl, BR-20940040 Rio De Janeiro, RJ, BrazilFed Univ Para, Inst Estudos Costeiros, BR-68600000 Braganca, PA, BrazilUniv Fed Sao Paulo, Dept Ciencias Mar, BR-11070100 Santos, SP, BrazilUniv Fed Pernambuco, Dept Oceanog, BR-50670901 Recife, PE, BrazilUniv Georgia, Dept Marine Sci, Athens, GA 30602 USAUniv Fed Paraiba, BR-58297000 Rio Tinto, PB, BrazilUniv Estadual Santa Cruz, Dept Ciencias Biol, BR-45650000 Ilheus, BA, BrazilUniv Fed Sao Paulo, Dept Ciencias Mar, BR-11070100 Santos, SP, BrazilU.S. NSF: OCE-0934095GBMF: 2293GBMF: 2928Web of Scienc

Plasma glial fibrillary acidic protein is raised in progranulin-associated frontotemporal dementia

Background There are few validated fluid biomarkers in frontotemporal dementia (FTD). Glial fibrillary acidic protein (GFAP) is a measure of astrogliosis, a known pathological process of FTD, but has yet to be explored as potential biomarker. Methods Plasma GFAP and neurofilament light chain (NfL) concentration were measured in 469 individuals enrolled in the Genetic FTD Initiative: 114 C9orf72 expansion carriers (74 presymptomatic, 40 symptomatic), 119 GRN mutation carriers (88 presymptomatic, 31 symptomatic), 53 MAPT mutation carriers (34 presymptomatic, 19 symptomatic) and 183 non-carrier controls. Biomarker measures were compared between groups using linear regression models adjusted for age and sex with family membership included as random effect. Participants underwent standardised clinical assessments including the Mini-Mental State Examination (MMSE), Frontotemporal Lobar Degeneration-C linical Dementia Rating scale and MRI. Spearman's correlation coefficient was used to investigate the relationship of plasma GFAP to clinical and imaging measures. Results Plasma GFAP concentration was significantly increased in symptomatic GRN mutation carriers (adjusted mean difference from controls 192.3 pg/mL, 95% CI 126.5 to 445.6), but not in those with C9orf72 expansions (9.0, -61.3 to 54.6), MAPT mutations (12.7, -33.3 to 90.4) or the presymptomatic groups. GFAP concentration was significantly positively correlated with age in both controls and the majority of the disease groups, as well as with NfL concentration. In the presymptomatic period, higher GFAP concentrations were correlated with a lower cognitive score (MMSE) and lower brain volume, while in the symptomatic period, higher concentrations were associated with faster rates of atrophy in the temporal lobe. Conclusions Raised GFAP concentrations appear to be unique to GRN-related FTD, with levels potentially increasing just prior to symptom onset, suggesting that GFAP may be an important marker of proximity to onset, and helpful for forthcoming therapeutic prevention trials