Early Warning System for Bathing Water Quality

Poster describing use of Artificial Neural Networks (ANN) in a Receiver-Operating Characteristic (ROC) scenario to predict bathing water quality exceedances at beaches in the SW UK.Environment Agency (SW

Rain Gauge and Radar Rainfall Information for Urban Flash Flood Analysis

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

The urban inundation model with bidirectional flow interaction between 2D overland surface and 1D sewer networks

8th international conference, Novatech 2013, Lyon, France, 23 – 27 June 2013An integrated numerical model is developed in the study for simulating the runoff processes in urban areas. A 1D model is used for calculating the rainfall-runoff hydrographs and the flow conditions in drainage networks. A 2D model is employed for routing flow on overland surface. Both models are solved by different numerical schemes and using different time steps with the flow through manholes adopted as model connections. The effluents and influents via manholes are determined by the weir or the orifice equations. Timing synchronisation between both models is taken into account to guarantee suitable model linkages.EPSRC: Engineering and Physical Sciences Research Counci

An analysis of the combined consequences of pluvial and fluvial flooding

Copyright © IWA Publishing 2010. The definitive peer-reviewed and edited version of this article is published in Water Science & Technology Vol. 62 No. 7 pp 1491–1498 (2010), DOI: 10.2166/wst.2010.486, and is available at www.iwapublishing.comIntense rainfall in urban areas often generates both pluvial flooding due to the limited capacity of drainage systems, as well as fluvial flooding caused by deluges from river channels. The concurrence of pluvial and fluvial flooding can aggravate their (individual) potential damages. To analyse the impact caused by individual and composite type of flooding, the SIPSON/UIM model, an integrated 1D sewer and 2D overland flow was applied to numerical modelling. An event matrix of possible pluvial scenarios was combined with hypothetic overtopping and breaching situations to estimate the surface flooding consequences in the Stockbridge area, Keighley (Bradford, UK). The modelling results identified different flooding drivers in different parts of the study area and showed that the worst scenarios resulted from synthesised events.Engineering and Physical Sciences Research Council (EPSRC

Multi-layered coarse grid modelling in 2D urban flood simulations

Copyright © 2012 Elsevier. NOTICE: This is the author’s version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology Vol. 470-471, DOI: 10.1016/j.jhydrol.2012.06.022Regular grids are commonly used in 2D flood modelling due to wide availability of terrain models and low pre-processing required for input preparation. Despite advances in both computing software and hardware, high resolution flood modelling remains computationally demanding when applied to a large study area when the available time and resources are limited. Traditional grid coarsening approach may reduce not only the computing demands, but also the accuracy of results due to the loss of detailed information. To keep key features that affect flow propagation within coarse grid, the approach proposed and tested in this paper adopts multiple layers in flood modelling to reflect individual flow paths separated by buildings within a coarse grid cell. The cell in each layer has its own parameters (elevation, roughness, building coverage ratio, and conveyance reduction factors) to describe itself and the conditions at boundaries with neighbourhood cells. Results of tests on the synthetic case study and the real world urban area show that the proposed multi-layered approach greatly improves the accuracy of coarse grid modelling with an insignificant additional computing cost. The proposed approach has been tested in conjunction with the UIM model by taking the high resolution results as the benchmark. The implementation of the proposed multi-layered methodology to any regular grid based 2D model would be straightforward

Application of cellular automata approach for fast flood simulation

CCWI 2011: Computing and Control for the Water Industry, 5-7 September 2011, University of Exeter, UKThe increasing pluvial flooding in many urban areas of the world has caused tremendous damage to societies and has drawn the attention of researchers to the development of a fast flood inundation model. Most available models are based on solving a set of partial differential equations that require a huge computational effort. Researchers are increasingly interested in an alternative grid-based approach called Cellular Automata (CA), due to its computational efficiency (both with respect to time and computational cost) and inherent parallel nature. This paper deals with the computational experiment with a new CA method for modelling 2D pluvial flood propagation. A Digital Elevation Model (DEM) comprising square grids forms the discrete space for the CA setup. Local rules are applied in the von Neumann Neighbourhood for the spatio-temporal evolution of the flow field. The proposed model is applied to a hypothetical terrain to assess its performance. The results from the CA model are compared with those of a physically based 2D urban inundation model (UIM). The CA model results are comparable with the results from UIM model. The advantages of low computational cost of CA and its ability to mimic realistic fluid movement are combined in a novel and fast flood simulation model

RAPIDS: Early Warning System for Urban Flooding and Water Quality Hazards

Machine Learning in Water Systems symposium: part of AISB Annual Convention 2013, University of Exeter, UK, 3-5 April 2013Convention organised by the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), www.aisb.org.uk/This paper describes the application of Artificial Neural Networks (ANNs) as Data Driven Models (DDMs) to predict urban flooding in real-time based on weather radar and/or raingauge rainfall data. A time-lagged ANN is configured for prediction of flooding at sewerage nodes and outfalls based on input parameters including rainfall. In the absence of observed flood data, a hydrodynamic simulator may be used to predict flooding surcharge levels at nodes of interest in sewer networks and thus provide the target data for training and testing the ANN. The model, once trained, acts as a rapid surrogate for the hydrodynamic simulator and can thus be used as part of an urban flooding Early Warning System (EWS). Predicted rainfall over the catchment is required as input, to extend prediction times to operationally useful levels. Both flood-level analogue and flood-severity classification schemes are implemented. An initial case study using Keighley, W Yorks, UK demonstrated proof-of-concept. Three further case studies for UK cities of different sizes explore issues of soil-moisture, early operation of pumps as flood-mitigation/prevention strategy and spatially variable rainfall. We investigate the use of ANNs for nowcasting of rainfall based on the relationship between radar data and recorded rainfall history; a feature extraction scheme is described. This would allow the two ANNs to be cascaded to predict flooding in real-time based on current weather radar Quantitative Precipitation Estimates (QPE). We also briefly describe the extension of this methodology to Bathing Water Quality (BWQ) prediction

Urban flood prediction in real-time from weather radar and rainfall data using artificial neural networks

WRAH 2011: Weather Radar and Hydrology International Symposium, 18-21 April 2011, University of Exeter, UKThis paper describes the application of ANNs (Artificial Neural Networks) as DDMs (Data Driven Models) to predict urban flooding in real-time based on BADC weather radar and/or rainfall data. A 123-manhole combined sewer sub-network from Keighley, West Yorkshire, UK is used to demonstrate the methodology. An ANN is configured for prediction of flooding at manholes based on rainfall. In the absence of actual flood data, the 3DNet / SIPSON simulator, which uses a conventional fluid-dynamic approach to predict flooding surcharge levels in sewer networks, is employed to provide the target data for training the ANN. Artificial rainfall profiles derived from observed data provide the input. Both flood-level analogue and flood-severity classification schemes are implemented. We also investigate the use of an ANN for nowcasting of rainfall based on the relationship between radar data and recorded rainfall history. This allows the two ANNs to be cascaded to predict flooding in real-time based on weather radar

Comparison of machine learning classifier models for bathing water quality exceedances in UK

The revised Bathing Water Directive (rBWD) (2006/7/EC) of the European Parliament requires monitoring of bathing water quality and, if early-warnings are provided to the public, it is permissible to discount a percentage of exceedance events from the monitoring process. This paper describes the development and implementation of both Decision Tree (DT) and Artificial Neural Network (ANN) based machine learning models for 8 beaches in south-west England, UK, as bases for early warning systems (EWS) and compares their performance for one beach. Weekly bacteria-count samples were gathered by the Environment Agency of England (EA) over a 12-year period from 2000-2011 during the 20-week bathing season and this data is used to calibrate and test the models. Daily sampling data were also collected at 5 of the beaches during the 2012 season to provide more robust validation of the models. As a benchmark, models are also compared with use of simple thresholds of antecedent rainfall to classify water quality exceedances. Evolutionary Algorithm-based optimisation of the ANN models is employed using single-objective approach using area under the Receiver Operating Characteristic (ROC) curve as fitness function. The optimum operating point is established using a weighting factor for the relative importance placed on false positives (passes) and false negatives (exceedances). The models use a number of input factors, including antecedent rainfall for the catchment adjacent to each bathing beach. A possible technique for automating selection of inputs is also discussed.Environment Agency (SW

Design of a graphical framework for simple prototyping of pluvial flooding cellular automata algorithms

CCWI 2011: Computing and Control for the Water Industry, 5-7 September 2011, University of Exeter, UKCellular automata (CA) algorithms can be used for quickly describing models of complex systems using simple rules. CADDIES is a new EPSRC and industry-sponsored project that aims to use the computational speed of CA algorithms to produce operationally useful real/near-real time pluvial urban flood models for both 1D-sewer and 2D-surface (dual-drainage) flows. In this paper, the design of a graphical software framework for the CADDIES project is presented. This is intended to simplify the development, testing and use of CA algorithms, and to facilitate the handling of the peripheral tasks of data management and display; allowing the research users to focus on the central tasks of optimisation of CA models and algorithms themselves