Showing papers by "Slobodan Djordjević published in 2013"

Formulation of a fast 2D urban pluvial flood model using a cellular automata approach

Abstract: With the increase in frequency and severity of flash flood events in major cities around the world, the infrastructure and people living in those urban areas are exposed continuously to high risk levels of pluvial flooding. The situation is likely to be exacerbated by the potential impact of future climate change. A fast flood model could be very useful for flood risk analysis. One-dimensional (1D) models provide limited information about the flow dynamics whereas two-dimensional (2D) models require substantial computational time and cost, a factor that limits their use. This paper presents an alternative approach using cellular automata (CA) for 2D modelling. The model uses regular grid cells as a discrete space for the CA setup and applies generic rules to local neighbourhood cells to simulate the spatio-temporal evolution of pluvial flooding. The proposed CA model is applied to a hypothetical terrain and a real urban area. The synchronous state updating rule and inherent nature of the proposed model contributes to a great reduction in computational time. The results are compared with a hydraulic model and good agreement is found between the two models.

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

Abstract: Machine Learning in Water Systems symposium: part of AISB Annual Convention 2013, University of Exeter, UK, 3-5 April 2013

Intelligent Urban Water Infrastructure Management

Abstract: Urban population growth together with other pressures, such as climate change, create enormous challenges to provision of urban infrastructure services, including gas, electricity, transport, water, etc. Smart-grid technology is viewed as the way forward to ensure that infrastructure networks are flexible, accessible, reliable and economical. “Intelligent water networks” take advantage of the latest information and communication technologies to gather and act on information to minimise waste and deliver more sustainable water services. The effective management of water distribution, urban drainage and sewerage infrastructure is likely to require increasingly sophisticated computational techniques to keep pace with the level of data that is collected from measurement instruments in the field. This paper describes two examples of intelligent systems developed to utilise this increasingly available real-time sensed information in the urban water environment. The first deals with the failure-management decision-support system for water distribution networks, NEPTUNE, that takes advantage of intelligent computational methods and tools applied to near real-time logger data providing pressures, flows and tank levels at selected points throughout the system. The second, called RAPIDS, deals with urban drainage systems and the utilisation of rainfall data to predict flooding of urban areas in near real-time. The two systems have the potential to provide early warning and scenario testing for decision makers within reasonable time, this being a key requirement of such systems. Computational methods that require hours or days to run will not be able to keep pace with fast-changing situations such as pipe bursts or manhole flooding and thus the systems developed are able to react in close to real time.

Machine Learning-Based Early Warning System for Urban Flood Management

Abstract: With the growth in urban population and other pressures, such as climate change, the impact and severity of urban flood events are likely to continue to increase. “Intelligent water networks” are viewed as the way forward to ensure that infrastructure services are flexible, safe, reliable and economical. Reduction of flood-risk from urban drainage and sewerage infrastructure is likely to require increasingly sophisticated computational techniques to keep pace with the level of data that is collected both from meteorological and online water monitoring systems in the field. This paper describes and characterises an example of an Early Warning System (EWS), designated "RAPIDS" (RAdar Pluvial flooding Identification for Drainage System) that deals with urban drainage systems and the utilisation of rainfall data concurrently to predict flooding of multiple urban areas in near realtime using a single multi-output Artificial Neural Network (ANN). The system has the potential to provide early warning for decision makers within reasonable time, this being a key requirement determining the operational usefulness of such systems. Computational methods that require hours or days to run will not be able to keep pace with fast-changing situations such as manhole flooding or Combined Sewer Overflow (CSO) spills and thus the system developed is able to react in close to real time. This paper includes a sensitivity analysis and demonstrates that the predictive capability of such a system based on actual rainfall is limited to a maximum of the Time of Concentration (ToC) of each node being modelled. To achieve operationally useful prediction times, predictions of rainfall as input signals are likely to be needed for most urban drainage networks.

Implications of rising flood-risk for employment location: a GMM spatial model with agglomeration and endogenous house price effects

Abstract: The impact of flood-risk on local employment has been almost entirely neglected in the empirical urban economics literature. This omission is particularly anomalous in the context of climate change. We extend the literature in four ways. First, we argue that competition for land between firms and households will generate an endogenous role for house prices, which we estimate using a generalised method of moments two-stage least squares spatial econometric model. Second, we model interaction effects between agglomeration and flood-risk using a gravity-based agglomeration measure. Third, we utilise a high-resolution flood-risk measure which incorporates both flood frequency and severity. Fourth, we use a high-resolution measure of employment to capture local effects. We find that agglomeration economies have a significant mitigating effect on flood-risk. This is potentially important because it suggests that flood-risk may have a more deleterious effect on employment in areas where economic agglomeration is...

Sampling rainfall events: a novel approach to generate large correlated samples

Abstract: It is essential that the correlation between variables is considered properly when using sampling-based methods. Modeling rainfall events is of great interest because the rainfall is usually the major driving force of hydrosystems. A novel method for generating correlated samples is introduced providing that the marginal distributions of variables as well as their correlations between them are known. The basic idea of the method is to adjust the correlations between samples by rearranging the positions inside marginal samples after each marginal sample is generated according to its distribution. The group method is developed in order to facilitate an efficient generation of correlated samples of large sizes. The theoretical precision associated with the group method is derived. There is a trade off between the computational efficiency of the algorithm and the precision that can be achieved when using different numbers of groups. The method is successfully applied to two cases of rainfall sample generation problems. The effectiveness of the group method is studied. Large group numbers are recommended in practical use as the samples distribute more broadly regardless of computational efficiency.

Fast Simulation of Sewer Flow using Cellular Automata

Abstract: As the climate changes and urbanisation occurs the risk of flooding is increasing. Numerical modelling can help to indicate the location and degree of flooding under given conditions. This aids engineers to improve the design of sewer systems at critical locations. Modelling also aids the planning process and real time management to mitigate the flooding impact. To be able to do this in real time low computation times are required. Fast computation can be difficult to obtain using traditional modelling methods, such as the Saint Venant Equations, due to their complexity. Thus there have been a number of attempts to obtain faster but still highly accurate conceptual models. A number of fast models have been developed, however, their accuracy is often lower than those which use the Saint Venant Equations. In this paper a Cellular Automata sewer simulator will be presented which is both fast and accurate. The new model has been tested using a small theoretical network that has been designed to fully test the model’s capabilities.

Flood impact assessment under climate change scenarios in central Taipei area, Taiwan

Abstract: The work is supported by the National Science Council, Taiwan (NSC 99-2915-I-002-120) and the CORFU project, funded by the European Commission through Framework Programme 7, Grant Number 244047.

Back to the future: Assessing the impact of the 2004 flood in Dhaka in 2050 (conference paper)

Abstract: Planning to make a city flood resilient needs proper assessment of the future conditions. Urban growth models are being used as a planning tool for City development. Within the CORFU project flood management strategies will be developed suitable for cities with varied geographic and socioeconomic conditions. This paper studies the application of Urban Growth Model (UDM) to determine the future condition of Dhaka City, which a rapidly developing capital of Bangladesh. Bangladesh lies in the delta of the Himalayan Mountain range and experiences frequent flooding. In 2004 an extreme flood event occurred in the country, which caused major damage to Dhaka City. If the same event occurs in 2050, it can be expected that the damage would increase significantly. Through application of urban growth model, urban flood model and damage model the damage that can be expected to happen in 2050 was determined in this paper. The paper also describes the key factors that are important to determine this impact and the associated uncertainties.