Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as 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 Environmental Modelling and Software Vol. 62 (2014), DOI: 10.1016/j.envsoft.2014.09.013The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Abstract not availableH.R. Maier, Z. Kapelan, Kasprzyk, J. Kollat, L.S. Matott, M.C. Cunha, G.C. Dandy, M.S. Gibbs, E. Keedwell, A. Marchi, A. Ostfeld, D. Savic, D.P. Solomatine, J.A. Vrugt, A.C. Zecchin, B.S. Minsker, E.J. Barbour, G. Kuczera, F. Pasha, A. Castelletti, M. Giuliani, P.M. Ree

Modeling and Control of Pipeline Networks Supplied by Automated Irrigation Channels

Abstract not availableBojan Mavkov, Timm Strecker, Aaron C. Zecchin, and Michael Canton

Exploratory scenario analysis for disaster risk reduction: considering alternative pathways in disaster risk assessment

Disaster risk is a combination of natural hazards, along with society's exposure and vulnerability to them. Therefore, to ensure effective, long-term disaster risk reduction we must consider the dynamics of each of these components and how they change over extended periods due to population, economic and climatic drivers, as well as policy and individual decisions. This paper provides a methodology to capture these factors within exploratory scenarios designed to test the effectiveness of policy responses to reduce disaster losses. The scenarios developed and subsequent analysis of them combine knowledge and insight from stakeholders and experts, and make use of simulation modelling to enable scenarios with qualitative and quantitative elements to be integrated within risk assessment processes and contribute to strategic risk treatments. The methodology was applied to a case-study in Greater Adelaide, Australia, and used to assess how disaster risk for earthquakes, bushfire and coastal inundation changes from 2016 to 2050 under five exploratory scenarios for the future of the region. This analysis can be applied more broadly to consider how future risks impact on regional viability, and suitability for investment related to the need to gain a better understanding of governmental and organisational exposure to physical risks.Graeme A. Riddell, Hedwig van Delden, Holger R. Maier, Aaron C. Zecchi

Sensor placement strategy for pipeline condition assessment using inverse transient analysis

Inverse transient analysis (ITA) has been recognized as a useful technique for pipeline condition assessment, such as leak detection and pipe wall thickness estimation. The effectiveness and accuracy of the inverse analysis are dependent on the sensor placement design; however, previous research on this topic is limited. This paper investigates how the number and location of pressure sensors affects the identifiability of pipeline parameters in the ITA approach. An analytical analysis demonstrates that infinite pipe parameter combinations can produce almost the same pressure responses at specific observation locations, which means that the identifiability of the pipe parameters will be poor if sensors are installed at these locations. Numerical sensitivity studies and multiple ITA case studies are conducted to investigate the relationship between the sensor locations and the parameter identifiability. It is found that at least three sensors are needed, and given the first two sensors are N reaches apart (i.e. N pipe segments in the inverse model), the third sensor should not be placed at nodes that are separated from any of the first two sensors by an integer multiple of N reaches.Chi Zhang, Jinzhe Gong, Martin F. Lambert, Angus R. Simpson, Aaron C. Zecchi

Reconstructing Extended Irregular Anomalies in Pipelines Using Layer-Peeling with Optimization

Pipe wall condition assessment is critical for targeted maintenance and failure prevention in water distribution systems. This paper proposes a spatially distributed pipeline condition assessment technique using persistent hydraulic transient waves of a small magnitude (microtransient waves), with a focus on the detection and reconstruction of extended and irregular pipe wall anomalies (e.g., nonuniform blockages and internal or external corrosion that is distributed along a short extent of the pipe). For an extended and irregular anomaly, a pipe’s response to any incident waves will be complex and impose challenges in interpretation. To identify the complex response patterns, an optimization technique has been developed using a differential evolution algorithm to separate the directional impulse response functions (IRFs) and then to differentiate the anomaly-induced response in a directional IRF from noise. A layer-peeling method is then applied to the directional IRF to reconstruct the pipe impedances, which are related to the localized wave speed and pipe wall thickness. Numerical verifications have been conducted on a pipe with a deteriorated section that is assumed to have a constant internal diameter but varying wave speeds along its length (simulating a section with nonuniform external corrosion and wall thinning). The results show that the nonuniformly deteriorated section can be successfully detected and accurately reconstructed using the techniques proposed in this paper.Wei Zeng, Jinzhe Gong, Aaron C. Zecchin, Martin F. Lambert, A.M.ASCE, Benjamin S. Cazzolato, and Angus R. Simpso

Optimal Pipe Network Sensor Layout Design for Hydraulic Transient Event Detection and Localization

Excessive hydraulic transients in a water distribution system (WDS) can instantaneously damage equipment and infrastructure, while long-term pressure oscillations can contribute to pipe structural deterioration and eventually pipe bursts. It is therefore important to monitor hydraulic transient events in WDSs and locate the source in a timely manner; however, there is a lack of a theoretical basis for optimal sensor placement with regard to real-time transient event detection. This paper investigates the criteria for optimal sensor deployment of high-speed pressure loggers in a WDS and develops a technique for determining the optimal sensor locations. The proposed criteria focus on maximizing the spatial extent of the network within which transient events can be detected and located. A key concept in this work is the locatability of an event, which is defined based on the combination of hydraulic wave propagation theory in networks and the adoption of graphing theoretic concepts, and is based on the existence of unique wave propagation paths from the event to two or more sensors. Two case studies are considered, where the first is a small network that is used to explain the steps of the method, and the second serves as the basis for an extensive numerical study, where it is observed that the proposed method outperforms other approaches and is able to provide the optimum sensor layout for a given number of sensors.A. C. Zecchin, N. Do, J. Gong, M. Leonard, M. F. Lambert, M.ASCE, and M. L. Stephen

Inverse wave reflectometry method for hydraulic transient-based pipeline condition assessment

Abstract not available.Wei Zeng, Aaron C. Zecchin, Jinzhe Gong, Martin F. Lambert, Angus R. Simpson and Benjamin S. Cazzolat

Bayesian inverse transient analysis for pipeline condition assessment: parameter estimation and uncertainty quantification

Strategic pipeline asset management requires accurate and up-to-date information on pipeline condition. As a tool for pipeline condition assessment, inverse transient analysis (ITA - a pipeline model calibration approach) is typically formulated as a deterministic problem, and optimization methods are used for searching a single best solution. The uncertainty associated with the single best solution is rarely assessed. In this paper, the pipeline model calibration problem is formulated as a Bayesian inverse problem, and a Markov Chain Monte Carlo (MCMC) based method is used to construct the estimated posterior probability density function (PDF) of the calibration parameters. The MCMC based method is able to achieve parameter estimation and uncertainty assessment in a single run, which is confirmed by numerical experiments. The proposed technique is also validated using measured hydraulic transient response data from an experimental laboratory pipeline system. Two thinner-walled pipe sections (simulating extended deterioration) are successfully identified with an assessment of the parameter uncertainty. The results also suggest that proper sensor placement can reduce parameter uncertainty and significantly enhance system identifiability.Chi Zhang, Martin F. Lambert, Jinzhe Gong, Aaron C. Zecchin, Angus R. Simpson,Mark L. Stephen