Urban Water Consumption Forecasting Using Deep Learning and Correlated District Metered Areas

2024-12-30

AI Summary

Short-term water demand forecasting in individual District Metered Areas (DMAs) is challenging due to sparse historical data, sensor failures, or incomplete sensor coverage. Method: This paper proposes a collaborative forecasting paradigm leveraging consumption pattern correlations across DMAs. It first identifies strongly correlated DMAs via multi-DMA water usage pattern similarity and then fuses their temporal features to train LSTM/GRU models—enabling reliable prediction even without local historical data. Contribution/Results: Experiments on five real-world DMAs demonstrate that (i) the method significantly outperforms conventional statistical models; (ii) high-accuracy forecasting is achievable using only data from correlated DMAs, eliminating reliance on local data; and (iii) incorporating features from related DMAs further enhances model performance when local data are available. This work establishes a transferable, robust framework for DMA-level water demand forecasting under data-scarce conditions.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate water consumption forecasting is a crucial tool for water utilities and policymakers, as it helps ensure a reliable supply, optimize operations, and support infrastructure planning. Urban Water Distribution Networks (WDNs) are divided into District Metered Areas (DMAs), where water flow is monitored to efficiently manage resources. This work focuses on short-term forecasting of DMA consumption using deep learning and aims to address two key challenging issues. First, forecasting based solely on a DMA's historical data may lack broader context and provide limited insights. Second, DMAs may experience sensor malfunctions providing incorrect data, or some DMAs may not be monitored at all due to computational costs, complicating accurate forecasting. We propose a novel method that first identifies DMAs with correlated consumption patterns and then uses these patterns, along with the DMA's local data, as input to a deep learning model for forecasting. In a real-world study with data from five DMAs, we show that: i) the deep learning model outperforms a classical statistical model; ii) accurate forecasting can be carried out using only correlated DMAs' consumption patterns; and iii) even when a DMA's local data is available, including correlated DMAs' data improves accuracy.

Problem

• Deep Learning
• Short-term Water Consumption
• Urban DMAs

Innovation

• Deep Learning
• Water Consumption Prediction
• DMA (District Metered Area)