Water Supply Network Flow Rate Prediction for Short-Duration by STL-LSTM

DOI

10.2991/978-94-6239-672-2_40How to use a DOI?

Keywords

Hybrid Models; LSTM; STL Decomposition; Time Series Predition Models; Water Supply Network

Abstract

Accurate prediction of flow in water distribution networks is essential for improving the operational efficiency of urban water supply systems and optimizing resource allocation. To address this need, this paper presents a short-duration flow prediction model for user nodes within a water distribution network. The proposed approach integrates seasonal-trend decomposition (STL) of flow sequences with a long short-duration memory (LSTM) network enhanced by multi-feature inputs. The modeling process begins with linear interpolation to preprocess the flow sequence, ensuring data continuity. The STL method is then employed to decompose the sequence into trend, seasonal, and residual components. Each component is modeled separately using dedicated LSTM networks to capture its unique temporal characteristics. The predicted components are subsequently aggregated to reconstruct the final short-term flow forecast. To evaluate model performance, residual analysis is conducted, confirming the robustness of the proposed approach. Furthermore, comparative experiments are performed against benchmark models including CNN, LSTM, and STL-CNN. Results show that the STL-LSTM model achieves a reduction in forecasting error of over 3% on half-hourly data, and it is verified that STL is not suitable for combining with CNN.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yiming Zhang
AU  - Changtao Wang
AU  - Xiaoming Han
AU  - Yetian Tian
AU  - Xinxin Wang
PY  - 2026
DA  - 2026/05/12
TI  - Water Supply Network Flow Rate Prediction for Short-Duration by STL-LSTM
BT  - Proceedings of the 2026 3rd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2026)
PB  - Atlantis Press
SP  - 420
EP  - 427
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-672-2_40
DO  - 10.2991/978-94-6239-672-2_40
ID  - Zhang2026
ER  -