Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.

In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.     

Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Water Resources Management - Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based...     

Risk- and robustness-based solutions to a multi-objective water distribution system rehabilitation problem under uncertainty .

The water distribution system (WDS) rehabilitation problem is defined here as a multi-objective optimisation problem under uncertainty. Two alternative problem formulations are considered. The first objective in both approaches is to minimise the total rehabilitation cost. The second objective is to either maximise the overall WDS robustness or to minimise the total WDS risk. The WDS robustness is defined as the probability of simultaneously satisfying minimum pressure head constraints at all nodes in the network. Total risk is defined as the sum of nodal risks, where nodal risk is defined as the product of the probability of pressure failure at that node and consequence of such failure. Decision variables are the alternative rehabilitation options for each pipe in the network. The only source of uncertainty is the future water consumption. Uncertain demands are modelled using any probability density functions (PDFs) assigned in the problem formulation phase. The corresponding PDFs of the analysed nodal heads are calculated using the Latin Hypercube sampling technique. The optimal rehabilitation problem is solved using the newly developed rNSGAII method which is a modification of the well-known NSGAII optimisation algorithm. In rNSGAII a small number of demand samples are used for each fitness evaluation leading to significant computational savings when compared to the full sampling approach. The two alternative approaches are tested, verified and their performance compared on the New York tunnels case study. The results obtained demonstrate that both new methodologies are capable of identifying the robust (near) Pareto optimal fronts while making significant computational savings.     

A Resilience-Based Methodology for Improved Water Resources Adaptation Planning under Deep Uncertainty with Real World Application

Resilience of a water resource system in terms of water supply meeting future demand under climate change and other uncertainties is a prominent issue worldwide. This paper presents an alternative methodology to the conventional engineering practice in the UK for identifying long-term adaptation planning strategies in the context of resilience. More specifically, a resilience-based multi-objective optimization method is proposed that identifies Pareto optimal future adaptation strategies by maximizing a water supply system’s resilience (calculated as the maximum recorded duration of a water deficit period over a given planning horizon) and minimizing total associated costs, subject to meeting target system robustness to uncertain projections (scenarios) of future supply and demand. The method is applied to a real-world case study for Bristol Water’s water resource zone and the results are compared with those derived using a more conventional engineering practice in the UK, utilizing a least-cost optimization analysis constrained to a target reliability level. The results obtained reveal that the strategy solution derived using the current practice methodology produce a less resilient system than the similar costing solutions identified using the proposed resilience driven methodology. At the same time, resilience driven strategies are only slightly less reliable suggesting that trade-off exists between the two. Further examination of intervention strategies selected shows that the conventional methodology encourages implementation of more lower cost intervention options early in the planning horizon (to achieve higher system reliability) whereas the resilience-based methodology encourages more uniform intervention options sequenced over the planning horizon (to achieve higher system resilience).     

Pressure-Driven Demand and Leakage Simulation for Water Distribution Networks

Increasingly, water loss via leakage is acknowledged as one of the main challenges facing water distribution system operations. The consideration of water loss over time, as systems age, physical networks grow, and consumption patterns mature, should form an integral part of effective asset management, rendering any simulation model capable of quantifying pressure-driven leakage indispensable. To this end, a novel steady-state network simulation model that fully integrates into a classical hydraulic representation, pressure-driven demand and leakage at the pipe level is developed and presented here. After presenting a brief literature review about leakage modeling, the importance of a more realistic simulation model allowing for leakage analysis is demonstrated. The algorithm is then tested from a numerical standpoint and subjected to a convergence analysis. These analyses are performed on a case study involving two networks derived from real systems. Experimentally observed convergence/error statistics demonstrate the high robustness of the proposed pressure-driven demand and leakage simulation model.     

Assessing the Impact of Climate Change on Future Water Demand using Weather Data

Water Resources Management - Assessing the impact of climate change on water demand is a challenging task. This paper proposes a novel methodology that quantifies this impact by establishing a link...     

Using Information-Gap Decision Theory for Water Resources Planning Under Severe Uncertainty

Water resource managers are required to develop comprehensive water resources plans based on severely uncertain information of the effects of climate change on local hydrology and future socio-economic changes on localised demand. In England and Wales, current water resources planning methodologies include a headroom estimation process separate from water resource simulation modelling. This process quantifies uncertainty based on only one point of an assumed range of deviations from the expected climate and projected demand 25 years into the future. This paper utilises an integrated method based on Information-Gap decision theory to quantitatively assess the robustness of various supply side and demand side management options over a broad range of plausible futures. Findings show that beyond the uncertainty range explored with the headroom method, a preference reversal can occur, i.e. some management options that underperform at lower uncertainties, outperform at higher levels of uncertainty. This study also shows that when 50 % or more of the population adopts demand side management, efficiency related measures and innovative options such as rainwater collection can perform equally well or better than some supply side options The additional use of Multi-Criteria Decision Analysis shifts the focus away from reservoir expansion options, that perform best in regards to water availability, to combined strategies that include innovative demand side management actions of rainwater collection and greywater reuse as well efficiency measures and additional regional transfers. This paper illustrates how an Information-Gap based approach can offer a comprehensive picture of potential supply/demand futures and a rich variety of information to support adaptive management of water systems under severe uncertainty.     

Formation of a functional endothelium on vascular grafts

The lack of a functional endothelial cell lining on artificial polymeric vascular grafts severely reduces their effectiveness in replacing small caliber (< 6 mm) blood vessels. Techniques have now been developed to transplant autologous endothelial cells from one site in the body onto the surface of grafts prior to implantation. Pre-clinical animal trials provide evidence that grafts sodded with autologous, fat-derived, microvessel endothelial cells exhibit a stable, antithrombogenic lining of endothelium. The new endothelial cell lining exhibits morphologies identical with endothelium on native blood vessels. The effectiveness of endothelial cell sodding techniques in pre-clinical animal trials provides support for expanded clinical trials.     

A Committee Evolutionary Neural Network for the Prediction of Combined Sewer Overflows

Water Resources Management - Combined Sewer Overflows (CSOs) are a major source of pollution and urban flooding, spilling untreated wastewater directly into water bodies and the surrounding...