Using Multi-Objective Optimization To Maximize Multiple Benefits For Sustainable Drainage Design

For many years, drainage design was mainly about providing sufficient network capacity. This traditional approach had been successful with the aid of computer software and technical guidance. However, the drainage design criteria had been evolving due to rapid population growth, urbanisation, climate change and increasing sustainability awareness. Sustainable drainage systems that bring benefits in addition to water management have been recommended as better alternatives to conventional pipes and storages. Although the concepts and good practice guidance had already been communicated to decision makers and public for years, network capacity still remains a key design focus in many circumstances while the additional benefits are generally considered secondary only. Yet, the picture is changing. The industry begins to realise that delivering multiple benefits should be given the top priority while the drainage service can be considered a secondary benefit instead. The shift in focus means the industry has to adapt to new design challenges. New guidance and computer software are needed to assist decision makers. For this purpose, we developed a new decision support system. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. Users can systematically quantify the performance, life-cycle costs and benefits of different drainage systems using the evaluation framework. The optimisation tool can assist users to determine combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will focus on the optimisation component of the decision support framework. The optimisation problem formation, parameters and general configuration will be discussed. We will also look at the sensitivity of individual variables and the benchmark results obtained using common multi-objective optimisation algorithms. The work described here is the output of an EngD project funded by EPSRC and XP Solutions.

From Smart Meters To Smart Decisions: Web-Based Support For The Water Efficient Household

Smart water metering technologies for residential buildings offer, in principle, great opportunities for sustainable urban water management. However, much of this potential is as yet unrealized. Despite that several ICT solutions have already been deployed aiming at optimum operations on the water utilities side (e.g. real time control for water networks, dynamic pump scheduling etc.), little work has been done to date on the consumer side. This paper presents a web-based platform targeting primarily the household end user. The platform enables consumers to monitor, on a real-time basis, the water demand of their household, providing feedback not only on the total water consumption and relevant costs but also on the efficiency (or otherwise) of specific indoor and outdoor uses. Targeting the reduction of consumption, the provided feedback is combined with notifications about possible leakages\bursts, and customised suggestions to improve the efficiency of existing household uses. It also enables various comparisons, with past consumption or even with that of similar households, aiming to motivate further the householder to become an active player in the water efficiency challenge. The issue of enhancing the platform’s functionality with energy timeseries is also discussed in view of recent advances in smart metering and the concept of “smart cities”. The paper presents a prototype of this web-based application and critically discusses first testing results and insights. It also presents the way in which the platform communicates with central databases, at the water utility level. It is suggested that such developments are closing the gap between technology availability and usefulness to end users and could help both the uptake of smart metering and awareness raising leading, potentially, to significant reductions of urban water consumption. The work has received funding from the European Union FP7 Programme through the iWIDGET Project, under grant agreement no318272.

Comparison Of Demand Pattern Calibration In Water Distribution Networks With Geographic And Non-Geographic Parameterization

Demands are one of the most uncertain parameters in a water distribution network model. A good calibration of the model demands leads to better solutions when using the model for any purpose. A demand pattern calibration methodology that uses a priori information has been developed for calibrating the behaviour of demand groups. Generally, the behaviours of demands in cities are mixed all over the network, contrary to smaller villages where demands are clearly sectorised in residential neighbourhoods, commercial zones and industrial sectors. Demand pattern calibration has a final use for leakage detection and isolation. Detecting a leakage in a pattern that covers nodes spread all over the network makes the isolation unfeasible. Besides, demands in the same zone may be more similar due to the common pressure of the area rather than for the type of contract. For this reason, the demand pattern calibration methodology is applied to a real network with synthetic non-geographic demands for calibrating geographic demand patterns. The results are compared with a previous work where the calibrated patterns were also non-geographic.

SESAME: Finding Ways Of Promoting SME Adaptation To Flood Risk

Small and medium-sized companies and other enterprises (SMEs) around the world are exposed to flood risk and many of the 4.5 million in the UK are at risk. As SMEs represent almost half of total business turnover in the UK, their protection is a vital part of the drive for greater climate change resilience. However, few have measures in place to ensure the continuity of their activities during a flood and its aftermath. The SESAME project aims to develop tools that encourage businesses to discover ways of becoming more resilient to floods and to appreciate how much better off they will be once they have adapted to the ongoing risk. By taking some of the mystery out of flooding and flood risk, it aims to make it susceptible to the same business acumen that enables the UK’s SMEs to deal with the many other challenges they face. In this paper we will report on the different aspects of the research in the project Understanding behaviour Changing behaviour Modelling impacts Economic impacts Through the above the project will advise government, local authorities and other public bodies on how to improve their responses to floods and will enable them to recommend ways to improve the guidelines provided to SMEs in flood risk areas.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Analysis Of Semi-Distributed And Global Hydrological Models In The Central Tropical Basins Of The Gulf Of Mexico To The Effects Of Extreme Hydrometeorological Phenomena

In the last years extreme hydrometeorological phenomena have increased in number and intensity affecting the inhabitants of various regions, an example of these effects are the central basins of the Gulf of Mexico (CBGM) that they have been affected by 55.2% with floods and especially the state of Veracruz (1999-2013), leaving economic, social and environmental losses. Mexico currently lacks sufficient hydrological studies for the measurement of volumes in rivers, since is convenient to create a hydrological model (HM) suited to the quality and quantity of the geographic and climatic information that is reliable and affordable. Therefore this research compares the semi-distributed hydrological model (SHM) and the global hydrological model (GHM), with respect to the volumes of runoff and achieve to predict flood areas, furthermore, were analyzed extreme hydrometeorological phenomena in the CBGM, by modeling the Hydrologic Modeling System (HEC-HMS) which is a SHM and the Modèle Hydrologique Simplifié à I'Extrême (MOHYSE) which is a GHM, to evaluate the results and compare which model is suitable for tropical conditions to propose public policies for integrated basins management and flood prevention. Thus it was determined the temporal and spatial framework of the analyzed basins according to hurricanes and floods. It were developed the SHM and GHM models, which were calibrated, validated and compared the results to identify the sensitivity to the real model. It was concluded that both models conform to tropical conditions of the CBGM, having MOHYSE further approximation to the real model. Worth mentioning that in Mexico there is not enough information, besides there are no records of MOHYSE use in Mexico, so it can be a useful tool for determining runoff volumes. Finally, with the SHM and the GHM were generated climate change scenarios to develop risk studies creating a risk map for urban planning, agro-hydrological and territorial organization.