Real-time Burst Detection in Water Distribution Systems Using a Bayesian Demand Forecasting Methodology

AbstractThe negative consequences of non-revenue water losses from Water Distribution Systems (WDS) can be reduced through the successful and prompt identification of bursts and abnormal conditions. Here we present a preliminary investigation into the application of a probabilistic demand forecasting approach to identify pipe bursts. The method produces a probabilistic forecast of future demand under normal conditions. This, in turn, quantifies the probability that a future observation is abnormal. The method, when tested using synthetic bursts applied to a demand time-series for a UK WDS, performed well in detecting bursts, particularly those >5% of mean daily flow at night time

Short-term demand forecast using a bank of neural network models trained using genetic algorithms for the optimal management of drinking water networks

Efficient management of a drinking water network reduces the economic costs related to water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economic costs. Therefore, short-term forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon demand forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of forecasting water demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using demand data from the Barcelona water network.Peer ReviewedPostprint (author's final draft

Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks

PublishedThis paper presents an artificial neural network-based model of domestic water consumption. The model is based on real-world data collected from smart meters, and represents a step toward being able to model real-time smart meter data. A range of input schemas are examined, including real meter readings and summary statistics derived from readings, and it is found that the models can predict some consumption but struggle to accurately match in cases of peak usage

Water demand forecasting accuracy and influencing factors at different spatial scales using a Gradient Boosting Machine

Understanding, comparing, and accurately predicting water demand at different spatial scales is an important goal that will allow effective targeting of the appropriate operational and conservation efforts under an uncertain future. This study uses data relating to water consumption available at the household level, as well as postcode locations, household characteristics, and weather data in order to identify the relationships between spatial scale, influencing factors, and forecasting accuracy. For this purpose, a Gradient Boosting Machine (GBM) is used to predict water demand 1–7 days into the future. Results show an exponential decay in prediction accuracy from a Mean Absolute Percentage Error (MAPE) of 3.2% to 17%, for a reduction in group size from 600 to 5 households. Adding explanatory variables to the forecasting model reduces the MAPE up to 20% for the peak days and smaller household groups (20–56 households), whereas for larger aggregations of properties (100–804 households), the range of improvement is much smaller (up to 1.2%). Results also show that certain types of input variables (past consumption and household characteristics) become more important for smaller aggregations of properties, whereas others (weather data) become less important.Sanitary Engineerin

41st annual hydrology days (2021) - online proceedings

The 41st Annual AGU Hydrology Days event at Colorado State University was hosted online March 30-31, 2021.Includes the schedule and presentation abstracts only. The 41st Annual American Geophysical Union Hydrology Days meeting provides a unique opportunity for students, faculty, staff and practitioners to engage in wide range of water-related interdisciplinary research topics. Unfortunately, the global pandemic has left students with limited opportunities to share their research and satisfy graduation requirements. This year the spotlight focused on students to highlight the interconnections of water and linked systems. The 2021 Student Showcase provides an opportunity for students to exchange ideas, present their research findings and refine their science communication skills

Agrometeorological forecasting

Agrometeorological forecasting covers all aspects of forecasting in agrometeorology. Therefore, the scope of agrometeorological forecasting very largely coincides with the scope of agrometeorology itself. All on-farm and regional agrometeorological planning implies some form of impact forecasting, at least implicitly, so that decision-support tools and forecasting tools largely overlap. In the current chapter, the focus is on crops, but attention is also be paid to sectors that are often neglected by the agrometeorologist, such as those occurring in plant and animal protection. In addition, the borders between meteorological forecasts for agriculture and agrometeorological forecasts are not always clear. Examples include the use of weather forecasts for farm operations such as spraying pesticides or deciding on trafficability in relation to adverse weather. Many forecast issues by various national institutions (weather, but also commodity prices or flood warnings) are vital to the farming community, but they do not constitute agrometeorological forecasts. (Modified From the introduction of the chapter: Scope of agrometeorological forecasting)JRC.H.4-Monitoring Agricultural Resource