Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-4591 article 2006 Brooks, David B. and Brooks, David B. An Operational Definition of Water Demand Management International Journal of Water Resources Development 10.1080/07900620600779699 An operational definition of water demand management is proposed with five components: (1) reducing the quantity or quality of water required to accomplish a specific task; (2) adjusting the nature of the task so it can be accomplished with less water or lower quality water; (3) reducing losses in movement from source through use to disposal; (4) shifting time of use to off-peak periods; and (5) increasing the ability of the system to operate during droughts. This definition brings out the drivers of water saving and permits the tracking of gains by the source of the saving. It is applicable to nations at different stages of economic development. It also shows how goals of greater water use efficiency are linked to those of equity, environmental protection and public participation. Taken together, these goals make water demand management less a set of techniques than a concept of governance.
publications-4592 article 2008 Adamowski, Jan and Adamowski, Jan Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks Journal of Water Resources Planning and Management 10.1061/(asce)0733-9496(2008)134:2(119) Peak daily water demand forecasts are required for the cost-effective and sustainable management and expansion of urban water supply infrastructure. This paper compares multiple linear regression, time series analysis, and artificial neural networks (ANNs) as techniques for peak daily summer water demand forecast modeling. Analysis was performed on 10 years of peak daily water demand data and meteorological variables (maximum daily temperature and daily rainfall) for the summer months of May to August of each year for an area of high outdoor water usage in the city of Ottawa, Canada. Thirty-nine multiple linear regression models, nine time series models, and 39 ANN models were developed and their relative performance was compared. The artificial neural network approach is shown to provide a better prediction of peak daily summer water demand than multiple linear regression and time series analysis. The best results were obtained when peak water demand from the previous day, maximum temperature from the current and previous day, and the occurrence/nonoccurrence of rainfall from five days before, were used as input data. It was also found that the peak daily summer water demand is better correlated with the rainfall occurrence rather than the amount of rainfall itself, and that assigning a weighting system to the antecedent days of no rainfall does not result in more accurate models.
publications-4593 article 2006 Grayman, Walter M. and Ostfeld, Avi and Salomons, Elad Locating Monitors in Water Distribution Systems: Red Team–Blue Team Exercise Journal of Water Resources Planning and Management 10.1061/(asce)0733-9496(2006)132:4(300) Red team–blue team exercises are methods of evaluating security by creating a β€_x009c_gameβ€_x009d_ where one team (the red team) attempts to β€_x009c_attackβ€_x009d_ a target and the other team (the blue team) tries to defend it. This paper describes a computer exercise where the red team simulates the contamination of a water distribution system and the blue team defends the system by installing monitors to detect the presence of the contaminant. This exercise was developed and has been used as part of several demonstrations on the effectiveness of contamination monitoring systems for distribution systems. For comparison, a mathematical model is applied to the same network to select monitor locations that provide the optimal solution in terms of a set of objectives.
publications-4594 article 2007 Olmstead, Sheila M. and Olmstead, Sheila M. and Hanemann, W. Michael and Hanemann, W. Michael and Stavins, Robert N. and Stavins, Robert N. Water Demand Under Alternative Price Structures Journal of Environmental Economics and Management 10.1016/j.jeem.2007.03.002 We estimate the price elasticity of water demand with household-level data, structurally modeling the piecewise-linear budget constraints imposed by increasing block pricing. We develop a mathematical expression for the unconditional price elasticity of demand under increasing block prices and compare conditional and unconditional elasticities analytically and empirically. We test the hypothesis that price elasticity may depend on price structure, beyond technical differences in elasticity concepts. Due to the possibility of endogenous utility price structure choice, observed differences in elasticity across price structures may be due either to a behavioral response to price structure, or to underlying heterogeneity among water utility service areas.
publications-4595 article 2008 Propato, Marco and Piller, Olivier Battle of the Water Sensor Networks 10.1061/40941(247)112 A mixed-integer linear program is used to design the sensor location problem to detect potentially harmful contaminations in drinking water distribution systems. The size of data report time step is investigated as a mean to simplify problem formulation and solution identification. The uncertainty on the cost function value due to the size of the report time step can be explicitly calculated. This allows the designer (1) to estimate the theoretical cost function lower bound and thus to decide if a design is acceptable and (2) to establish a rational criteria for determining a meaningful size of the contamination event ensemble.
publications-4596 article 2008 Ostfeld, Avi and Asce, Member and Salomons, Elad Sensor Network Design Proposal for the Battle of the Water Sensor Networks (BWSN) 10.1061/40941(247)108 This study presents a multiobjective solution approach to the Battle of the Water Sensor Networks (BWSN) initiative (Ostfeld et al., 2006). The developed methodology tailors the algorithm of Ostfeld and Salomons (2005) for optimally placing sensors in a water distribution system with the NSGA-II multiobjective genetic algorithm of Deb et al. (2002). Pareto optimal fronts are shown and discussed for the two BWSN Networks, for selected BWSN cases. Introduction During the last decade there has been an increasing interest in development of sensor networks to cope with both deliberate and accidental hazards intrusions into water distribution systems. Optimization models and solution algorithms have been developed for sensors locations using various algorithms and objectives. These optimization models have made simplifying assumptions about design objectives, network contaminant transport, sensor response, event detection, emergency response, installation and maintenance costs, etc. Little is known about how these design algorithms compare to the efforts of human designers, and thus what advantages they propose for practical design of sensor networks. To explore these issues the Battle of the Water Sensor Networks (BWSN) initiative was called upon (Ostfeld et al., 2006) with the purpose of objectively comparing the performance of contributed sensor network designs of different teams, as applied on two water distribution system examples. This manuscript summarizes such an effort.
publications-4597 article 2008 McKenna, Sean Andrew and McKenna, Sean A. and Klise, Katherine A. and Klise, Katherine A. and Wilson, M. P. and Wilson, Mark and Wilson, M. and Wilson, M. TESTING WATER QUALITY CHANGE DETECTION ALGORITHMS. 10.1061/40941(247)132 Rapid detection of anomalous operating conditions within a water distribution network is desirable for the protection of the network against both accidental and malevolent contamination events. In the absence of a suite of in-situ, real-time sensors that can accurately identify a wide range of contaminants, we focus on detecting changes in water quality through analysis of existing data streams from in-situ water quality sensors. Three different change detection algorithms are tested: time series increments, linear filter and multivariate distance. Each of these three algorithms uses previous observations of the water quality to predict future water quality values. Large deviations between the predicted or previously measured values and observed values at future times indicate a change in the expected water quality. The definition of what constitutes a large deviation is quantified by a threshold value applied to the observed differences. Both simulated time series of water quality as well as measured chlorine residual values from two different locations within a distribution network are used as the background water quality values. The simulated time series are created specifically to challenge the change detection algorithms with bimodally distributed water quality values having a square wave and sin wave time series, with and without correlated noise. Additionally, a simulated time series resembling observed water quality time series is created with different levels of variability. The algorithms are tested in two different ways. First, background water quality without any anomalous events are used to test the ability of each algorithm to identify the water quality value at the next time step. Summary statistics on the prediction errors as well as the number of false positive detections quantify the ability of each algorithm to predict the background water quality. The performance of the algorithms with respect to limiting false positives is also compared against a simpler β€_x009c_set pointβ€_x009d_ approach to detecting water quality changes. The second mode of testing employs events in the form of square waves superimposed on top of modeled/measured background water quality data. Three different event strengths are examined and the event detection capabilities of each algorithm are evaluated through the use of receiver operating characteristic (ROC) curves. The area under the ROC curve provides a quantitative basis of comparison across the three algorithms. Results show that the multivariate algorithm produces the lowest prediction errors for all cases of background water quality. A comparison of the number of false positives reported from the change detection algorithms and a set point approach highlights the efficiency of the change detection algorithms. Across all three algorithms, most prediction errors are within one standard deviation of the mean water quality. The event detection results show that the best performing algorithm varies across different background water quality models and simulated event strength.
publications-4598 article 2009 Bithell, Mike and Bithell, Mike and Brasington, James and Brasington, James Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution Environmental Modelling and Software 10.1016/j.envsoft.2008.06.016
publications-4599 article 2010 Atzori, Luigi and Atzori, Luigi and Iera, Antonio and Iera, Antonio and Morabito, Giacomo and Morabito, Giacomo The Internet of Things: A survey Computer Networks 10.1016/j.comnet.2010.05.010
publications-4600 article 2014 Romano, Michele and Romano, Michele and Kapelan, Zoran and Kapelan, Zoran and Savić, Dragan and Savic, Dragan Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems Journal of Water Resources Planning and Management 10.1061/(asce)wr.1943-5452.0000339 AbstractThis paper presents a new methodology for the automated near-real-time detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g.,unauthorized consumptions) at the district metered area (DMA) level. The new methodology makes synergistic use of several self-learning artificial intelligence (AI) techniques and statistical data analysis tools, including wavelets for denoising of the recorded pressure/flow signals, artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values, statistical process control (SPC) techniques for short- and long-term analysis of the pipe burst/other event-induced pressure/flow variations, and Bayesian inference systems (BISs) for inferring the probability of a pipe burst/other event occurrence and raising corresponding detection alarms. The methodology presented here is tested and verified on a case study involving several DMAs in the United Kingdom (U.K.) with both real-life pipe burst/other eve...