A symbolic data-driven technique based on evolutionary polynomial regression

This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulæ with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data

Identification of leakages by calibration of WDS models

Open Access journalCopyright © 2013 The Authors. Published by Elsevier Ltd.12th International Conference on Computing and Control for the Water Industry, CCWI2013Leakage detection is critical for the proper management of water distribution systems (WDS). This paper proposes a leak detection approach based on a Bayesian calibration method. The methodology uses a newly formulated index, μ, which takes into account the variation of roughness in pipes between the calibrated models with and without leaks. Case studies, which use literature networks, are presented to demonstrate how the approach can be used in identifying pipes with losses. The approach starts with a calibration method followed by the analysis of sensitivity matrices. The approach proved to be effective in finding leaks, but the results depend crucially on the number and quality of the observed data.European CommissionEuropean Social FundRegion of Calabri

Model calibration as a tool for leakage identification in WDS: A real case study

16th Water Distribution System Analysis Conference, WDSA2014 — Urban Water Hydroinformatics and Strategic PlanningCopyright © 2014 The Authors. Published by Elsevier Ltd.Water leakage detection is important for a proper management of water distribution systems (WDS). This paper proposes the application of the leak detection approach based on a new Bayesian calibration methodology. The methodology uses a new developed index μ, which takes into account the difference in roughness values in pipes of the calibrated models with and without leaks. The case study is referred to a real network and is presented to demonstrate how the approach can be used in identifying pipes with losses. The approach starts with the UNINET calibration method followed by the analysis of sensitivity matrices. The case study proves that the approach is effective in finding leaks in real networks, but the results depend on the quality of the observed data

Water Distribution Modeling

To effectively use water distribution models, the engineer must be able to link knowledge of basic hydraulic theory and the mechanics of the program with that of the operation of real-world systems. Water Distribution Modeling does just that. Written by industry experts, it provides a practical resource for engineers and modelers that goes well beyond being a how-to guide for typing data into a computer program. It contains straightforward answers to common questions related both to modeling and to distribution systems in general. This textbook walks the practicing engineer or student through the modeling process from start to finish — from data collection and field-testing to using a model for system design and complex operational tasks

Preliminary Investigation of the `Learnable Evolution Model' for Faster/Better Multiobjective Water Systems Design

The design of large scale water distribution systems is a very difficult optimisation problem which invariably requires the use of time-expensive simulations within the fitness function. The need to accelerate optimisation for such problems has not so far been seriously tackled. However, this is a very important issue, since as MOEAs become more and more recognised as the lsquoindustry standardrsquo technique for water system design, the demands placed on such systems (larger and larger water networks) will quickly meet with problems of scaleup. Meanwhile, LEM (Learnable Evolution Modelrsquo) has appeared in the Machine Learning literature, and provides a general approach to integrating machine learning into evolutionary search. Published results using LEM show very great promise in terms of finding near-optimal solutions with significantly reduced numbers of evaluations. Here we introduce LEMMO (Learnable Evolution Model for Multi-Objective optimization), which is a multi-objective adaptation of LEM, and we apply it to certain problems commonly used as benchmarks in the water systems community. Compared with NSGA-II, we find that LEMMO both significantly improves performance, and significantly reduces the number of evaluations needed to reach a given target. We conclude that the general approach used in LEMMO is a promising direction for meeting the scale-up challenges in multiobjective water system design

A web-based platform for water efficient households

16th Water Distribution System Analysis Conference, WDSA2014 — Urban Water Hydroinformatics and Strategic PlanningThe advent of ICT services on water sector offers new perspective towards sustainable water management. This paper presents an innovative web-based platform, targeting primarily the household end-users. The platform enables consumers to monitor and control, on real-time basis, the water and energy consumption of their household providing valuable information and feedback. At the same time, the platform further supports end-users to modify and improve their consumption profile via an interactive educational process that comprises a variety of on-line tools and applications. This paper discusses the rationale, structure and technologies upon which the platform has been developed and presents an early prototype of the various tools, applications and facilities.European Union Seventh Framework Programme (FP7/2007-2013), through the iWIDGET Project (2012-2015

Pressure-driven Demand and Leakage Simulation for Water Distribution Networks

Copyright © 2008 American Society of Civil EngineersIncreasingly, 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

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