International Conference: Beyond Databases, Architectures and Structures

BDAS 2015: Beyond Databases, Architectures and Structures pp 333-342

Forecasting Daily Urban Water Demand Using Dynamic Gaussian Bayesian Network

Conference paper

DOI: 10.1007/978-3-319-18422-7_30

Volume 521 of the book series Communications in Computer and Information Science (CCIS)
Cite this paper as:
Froelich W. (2015) Forecasting Daily Urban Water Demand Using Dynamic Gaussian Bayesian Network. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. BDAS 2015. Communications in Computer and Information Science, vol 521. Springer, Cham

Abstract

The objective of the presented research is to create effective forecasting system for daily urban water demand. The addressed problem is crucial for cost-effective, sustainable management and optimization of water distribution systems. In this paper, a dynamic Gaussian Bayesian network (DGBN) predictive model is proposed to be applied for the forecasting of a hydrological time series. Different types of DGBNs are compared with respect to their structure and the corresponding effectiveness of prediction. First, it has been found that models based on the automatic learning of network structure are not the most effective, and they are outperformed by models with the designed structure. Second, this paper proposes a simple but effective structure of DGBN. The presented comparative experiments provide evidence for the superiority of the designed model, which outperforms not only other DGBNs but also other state-of-the-art forecasting models.

Keywords

Forecasting time series Water demand Bayesian networks 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland