ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons

Jiawen Ye, Xulai Meng, Haiying Wang (), Qingdao Zhou, Siwei An, Tong An, Pooria Ghorbani Bam and Diego Rosso
Additional contact information
Jiawen Ye: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Xulai Meng: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Haiying Wang: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Qingdao Zhou: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Siwei An: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Tong An: School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Pooria Ghorbani Bam: Department of Civil & Environmental Engineering, University of California, Irvine, CA 92697-2175, USA
Diego Rosso: Department of Civil & Environmental Engineering, University of California, Irvine, CA 92697-2175, USA

Mathematics, 2025, vol. 13, issue 13, 1-24

Abstract: Improving urban wastewater treatment efficiency and quality is urgent for most cities. The accurate wastewater flowrate forecast of a wastewater treatment plant (WWTP) is crucial for cutting energy use and reducing pollution. In this study, two hybrid models are proposed: ARIMA–Markov and ARIMA–LSTM–Transformer. Using 5 min-interval inlet flowrate data from a WWTP in 2024, the two models were verified and compared. Forecasts for 1 day, 7 days, and 2 months ahead were made, and model accuracies were compared. Ten repetitions with the same dataset assess stability, and ARIMA–LSTM–Transformer, with better performance, were selected. Then, the Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) algorithm, and Sparrow Search Algorithm (SSA) were used for optimization, with the WOA excelling in accuracy and stability. Experimental results show that compared to the single model Transformer, WOA–ARIMA–LSTM–Transformer did better in forecasting wastewater flowrate. The combined model enables efficient and accurate wastewater flowrate forecasting, highlighting the combined model’s application potential.

Keywords: autoregressive integrated moving average; long short-term memory; Markov; Transformer; wastewater flowrate forecast; wastewater treatment plant (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/13/2098/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/13/2098/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:13:p:2098-:d:1688063

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().