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 ▲	Type	Year	Authors	Title	Venue/Journal	DOI	Abstract
publications-4661	article	2000	Callaway, Duncan S. and Callaway, Duncan S. and Newman, M. E. J. and Newman, Mark and Strogatz, Steven H. and Strogatz, Steven H. and Watts, Duncan J. and Watts, Duncan J.	Network Robustness and Fragility: Percolation on Random Graphs	Physical Review Letters	10.1103/physrevlett.85.5468	Recent work on the Internet, social networks, and the power grid has addressed the resilience of these networks to either random or targeted deletion of network nodes or links. Such deletions include, for example, the failure of Internet routers or power transmission lines. Percolation models on random graphs provide a simple representation of this process but have typically been limited to graphs with Poisson degree distribution at their vertices. Such graphs are quite unlike real-world networks, which often possess power-law or other highly skewed degree distributions. In this paper we study percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolation, bond percolation, and models in which occupation probabilities depend on vertex degree. We discuss the application of our theory to the understanding of network resilience.
publications-4662	article	2001	Newman, M. E. J. and Newman, Mark	Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality	Physical Review E	10.1103/physreve.64.016132	Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they worked on those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question, "Who is the best connected scientist?"
publications-4663	article	2001	Jain, Ashu and Jain, Ashu and Varshney, Ashish Kumar and Varshney, Ashish Kumar and Joshi, U. and Joshi, Umesh Chandra	Short-Term Water Demand Forecast Modelling at IIT Kanpur Using Artificial Neural Networks	Water Resources Management	10.1023/a:1014415503476	The efficient operation and management of an existing water supply system require short-term water demand forecasts as inputs. Conventionally, regression and time series analysis have been employed in modelling short-term water demand forecasts. The relatively new technique of artificial neural networks has been proposed as an efficient tool for modelling and forecasting in recent years. The primary objective of this study is to investigate the relatively new technique of artificial neural networks for use in forecasting short-term water demand at the Indian Institute of Technology, Kanpur. Other techniques investigated in this study include regression and time series analysis for comparison purposes. The secondary objective of this study is to investigate the validity of the following two hypotheses: 1) the short-term water demand process at the Indian Institute of Technology, Kanpur campus is a dynamic process mainly driven by the maximum air temperature and interrupted by rainfall occurrences, and 2) occurrence of rainfall is a more significant variable than the rainfall amount itself in modelling the short-term water demand forecasts. The data employed in this study consist of weekly water demand at the Indian Institute of Technology, Kanpur campus, and total weekly rainfall and weekly average maximum air temperature from the City of Kanpur, India. Six different artificial neural network models, five regression models, and two time series models have been developed and compared. The artificial neural network models consistently outperformed the regression and time series models developed in this study. An average absolute error in forecasting of 2.41\% was achieved from the best artificial neural network model, which also showed the best correlation between the modelled and targeted water demands. It has been found that the water demand at the Indian Institute of Technology, Kanpur campus is better correlated with the rainfall occurrence rather than the amount of rainfall itself.
publications-4664	article	2003	Kapelan, Zoran and Kapelan, Zoran and SaviΔ‡, Dragan and Savic, Dragan and Walters, Godfrey A. and Walters, Godfrey A.	A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks	Journal of Hydraulic Research	10.1080/00221680309499993	Leakage detection and calibration of hydraulic models are important issues for the management of water and other distribution networks. An inverse transient model based on a hybrid search technique is presented here. The inverse model is developed mainly for the detection of leaks in water distribution networks. The inverse transient procedure is formulated as a constrained optimisation problem of weighted least-squares type. Two optimisation techniques are tested: the genetic algorithm (GA) and the Levenberg-Marquardt (LM) method. After examining their performance, a new hybrid genetic algorithm (HGA) is developed to exploit the advantages of combining the two methods. The resulting HGA-based inverse transient model is compared with the GA and LM-based inverse transient models using two case studies. The HGA-based inverse transient model proved to be more stable than the LM-based model and it is more accurate and much faster than the GA-based inverse transient model.
publications-4665	article	2003	Mounce, S. R. and Mounce, Stephen R. and Khan, Asar and Khan, Asar and Wood, Alastair S. and Wood, Alastair S. and Day, Andrew J. and Day, Andrew J. and Widdop, Peter D. and Widdop, Peter D. and Machell, John and Machell, John	Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system	Information Fusion	10.1016/s1566-2535(03)00034-4
publications-4666	article	2004	MartΓnezβ€EspiΓ±eira, Roberto and MartΓnez-EspiΓ±eira, Roberto and Nauges, CΓ©line and Nauges, CΓ©line	Is all domestic water consumption sensitive to price control	Applied Economics	10.1080/0003684042000218570	This paper presents a model of residential water demand based on the Stone-Geary utility function, which explicitly considers a threshold of water that is insensitive to price and a quantity that can adapt instantaneously to price changes. First, the threshold is assumed constant, being then allowed to vary according to past levels of consumption, a proxy for households' water-using equipment and habits. A measure of the depreciation rate of habits is derived and the effectiveness of price and non-price conservation measures are compared. The results provide useful policy recommendations for the studied case of Seville (Spain).
publications-4667	article	2005	Pasha, M. Fayzul K. and Pasha, M. F K and Lansey, Kevin and Lansey, Kevin E	Analysis of uncertainty on water distribution hydraulics and water quality		10.1061/40792(173)10	System modeling can be used as a basis of planning and operation decisions. However, model accuracy and uncertainty will impact the confidence in, thus the conservatism built into, model based decisions. Model prediction uncertainty results from uncertainty in model parameters that are determined through calibration or are based upon modeler judgment. Uncertainty in pressure head has been evaluated from the perspective of system reliability (Xu and Goulter (1999) and Bao and Mays (1990) and in design. However, the relative impact of alternative data has not been compared to assess the need for estimating parameters. In addition, water quality within a distribution is strongly affected by the selection of water quality model parameters such as bulk and wall decay coefficients. In addition, since water quality is dominated by advective transport, hydraulic parameters and conditions also impact water quality. This is most clearly seen in the effects of tanks due to flows to and from the storage facility. The hydraulic system representation will also affect the flow distribution and travel times within the system. Many modelers suggest that all pipe models are required to adequately represent the true flow patterns. To date, only anecdotal evidence through analysis of simple networks under deterministic conditions has been presented justifying this conclusion. However, as more pipes are introduced, more uncertain parameters must be defined or calibrated for those components. This paper begins to examine the impact of alternative sources of uncertainty on water quality predictions by examining steady state conditions for a well-documented mid-sized system. The objective is to study the effect of alternative uncertain
publications-4668	article	2007	Matthews, Robin and Matthews, Robin and Gilbert, Nigel and Gilbert, Nigel and Roach, Alan and Roach, Alan and Polhill, Gary and Polhill, J. Gary and Gotts, Nick and Gotts, Nicholas Mark	Agent-based land-use models: a review of applications	Landscape Ecology	10.1007/s10980-007-9135-1	Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appearβ€”it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.
publications-4669	article	2008	Klise, Katherine A. and McKenna, Sean A. and McKenna, Sean Andrew and Klise, Katherine A.	MULTIVARIATE APPLICATIONS FOR DETECTING ANOMALOUS WATER QUALITY		10.1061/40941(247)130	The ability to detect deliberate or accidental contamination of a water distribution system is of real concern to the safety and security of our nationβ€™s drinking water. To address these concerns, increased attention has been placed on sophisticated monitoring of water distribution systems and the use of robust statistical analysis. Using existing data from in-situ water quality sensors, this paper explores the ability to detect anomalies in water quality using multivariate techniques. The algorithm developed in this study uses a multivariate distance measure between the current water quality measurement and the closest observation in multivariate space within a moving window of previous observations. To discriminate between normal and anomalous water quality, the distance measure is compared to a constant threshold. To test the algorithm, we utilize both simulated anomalous events and laboratory based events that correspond to real contaminants. These events are superimposed onto in-situ water quality recorded at four different locations within a single utility network. Measured water quality parameters include free chlorine, pH, temperature and electrical conductivity. Robust discrimination methods have a high probability of detecting anomalies with a low false alarm rate. Here, receiver operating characteristic (ROC) curves are used to test the ability of the multivariate classification algorithm to detect anomalous water quality while keeping false alarms low. This analysis explores the false alarm rate associated with detecting a range of anomalous water quality observations.
publications-4670	article	2009	Kappel, Karin and Kappel, Karin and Grechenig, Thomas and Grechenig, Thomas	"show-me": water consumption at a glance to promote water conservation in the shower		10.1145/1541948.1541984	Water is a scarce resource worldwide. Yet, we have many opportunities to conserve it. One particular opportunity for water conservation is the shower, because depending on the shower head and shower habits, an individual can save many liters of fresh water each day. Feedback proved to be an effective method to promote sustainable behavior. Therefore, in this paper we suggest to promote water conservation by providing feedback in form of an ambient display that can easily be integrated in current shower types. We built a prototype to study the potential of such a feedback device. These shower water meter (show-me) display the amount of water, that is used during one shower in form of LEDs assembled on a stick. Thus, an increasing water level is visualized. The user study revealed two groups. The subjects who considered themselves as ecologically conscious changed their behavior and turned the water down or off while soaping. Also, they are willing to pursue this behavior. Other subjects who did not have the goal to act more sustainable, were surprised about their water consumption and tried to reduce it. However, after the removal of the show-me device they did not maintain their behavior and fell back into their previous habit.