Urban Water Flow and Water Level Prediction Based on Deep Learning

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 10536; pp. 317 - 329
• Main Authors: Assem, Haytham, Ghariba, Salem, Makrai, Gabor, Johnston, Paul, Gill, Laurence, Pilla, Francesco
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Convolutional neural networks; Deep learning; Urban computing; Water management
• ISBN: 9783319712727; 3319712721
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-71273-4_26

The future planning, management and prediction of water demand and usage should be preceded by long-term variation analysis for related parameters in order to enhance the process of developing new scenarios whether for surface-water or ground-water resources. This paper aims to provide an appropriate methodology for long-term prediction for the water flow and water level parameters of the Shannon river in Ireland over a 30-year period from 1983–2013 through a framework that is composed of three phases: city wide scale analytics, data fusion, and domain knowledge data analytics phase which is the main focus of the paper that employs a machine learning model based on deep convolutional neural networks (DeepCNNs). We test our proposed deep learning model on three different water stations across the Shannon river and show it out-performs four well-known time-series forecasting models. We finally show how the proposed model simulate the predicted water flow and water level from 2013–2080. Our proposed solution can be very useful for the water authorities for better planning the future allocation of water resources among competing users such as agriculture, demotic and power stations. In addition, it can be used for capturing abnormalities by setting and comparing thresholds to the predicted water flow and water level.