ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons
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...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/13/2098 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850118985109995520 |
|---|---|
| author | Jiawen Ye Xulai Meng Haiying Wang Qingdao Zhou Siwei An Tong An Pooria Ghorbani Bam Diego Rosso |
| author_facet | Jiawen Ye Xulai Meng Haiying Wang Qingdao Zhou Siwei An Tong An Pooria Ghorbani Bam Diego Rosso |
| author_sort | Jiawen Ye |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-30e81f4061fa4576b810165f9e2561ed |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-30e81f4061fa4576b810165f9e2561ed2025-08-20T02:35:44ZengMDPI AGMathematics2227-73902025-06-011313209810.3390/math13132098ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time HorizonsJiawen Ye0Xulai Meng1Haiying Wang2Qingdao Zhou3Siwei An4Tong An5Pooria Ghorbani Bam6Diego Rosso7School of Science, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Science, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Science, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Science, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Science, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Science, China University of Geosciences (Beijing), Beijing 100083, ChinaDepartment of Civil & Environmental Engineering, University of California, Irvine, CA 92697-2175, USADepartment of Civil & Environmental Engineering, University of California, Irvine, CA 92697-2175, USAImproving 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.https://www.mdpi.com/2227-7390/13/13/2098autoregressive integrated moving averagelong short-term memoryMarkovTransformerwastewater flowrate forecastwastewater treatment plant |
| spellingShingle | Jiawen Ye Xulai Meng Haiying Wang Qingdao Zhou Siwei An Tong An Pooria Ghorbani Bam Diego Rosso ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons Mathematics autoregressive integrated moving average long short-term memory Markov Transformer wastewater flowrate forecast wastewater treatment plant |
| title | ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons |
| title_full | ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons |
| title_fullStr | ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons |
| title_full_unstemmed | ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons |
| title_short | ARIMA-Based Forecasting of Wastewater Flow Across Short to Long Time Horizons |
| title_sort | arima based forecasting of wastewater flow across short to long time horizons |
| topic | autoregressive integrated moving average long short-term memory Markov Transformer wastewater flowrate forecast wastewater treatment plant |
| url | https://www.mdpi.com/2227-7390/13/13/2098 |
| work_keys_str_mv | AT jiawenye arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT xulaimeng arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT haiyingwang arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT qingdaozhou arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT siweian arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT tongan arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT pooriaghorbanibam arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons AT diegorosso arimabasedforecastingofwastewaterflowacrossshorttolongtimehorizons |