Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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SES 3-2-1: Artificial Intelligence machine learning 1 Location: HSB0 Session Chair: Eran Friedler Session Chair: Namrata Karki | |
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11:15am - 11:30am
Machine-learning forecast model for predicting annual water consumption in budget estimation for urban drainage system management 1: Innsbrucker Kommunalbetriebe (IKB), Salurner Straße 11, 6020 Innsbruck, Austria; 2: Unit of Environmental Engineering, Department of Infrastructure Engineering, Faculty of Engineering Sciences, Universität Innsbruck, Technikerstraße 13, 6020 Innsbruck, Austria Commonly, the usable budet for operation of the urban drainage network is calculated at the end of the year based on the billed drinking water consumption at costumer sites. To estimate the available budget, the network operator uses a simple forecast of the annual water consumption using the average of the last four years. To further improve this process, different machine-learning based forecasting model were developed with the aim to quarterly predict the annual water consumption by integrating also actual weather and system states measurements with higher temporal resolution. As the results show, Support Vector Machine achieved the highest accuracy over the quarterly forecasting time points, followed by Linear Regression. Therefore, Linear Regression with Bayes Statistics was selected for the machine-learning based forecasting model, providing the network operator also an uncertainty assessment of the forecasting value. | |