Dynamic Real-Time Prediction of Reclaimed Water Volumes Using the Improved Transformer Model and Decomposition Integration Technology

Xiangyu Sun, Lina Zhang, Chao Wang (), Yiyang Yang and Hao Wang
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Xiangyu Sun: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Lina Zhang: School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Chao Wang: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yiyang Yang: College of the Environment and Ecology, Xiamen University, Xiamen 361104, China
Hao Wang: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China

Sustainability, 2024, vol. 16, issue 15, 1-26

Abstract: In recent years, wastewater reuse has become crucial for addressing global freshwater scarcity and promoting sustainable water resource development. Accurate inflow volume predictions are essential for enhancing operational efficiency in water treatment facilities and effective wastewater utilization. Traditional and decomposition integration models often struggle with non-stationary time series, particularly in peak and anomaly sensitivity. To address this challenge, a differential decomposition integration model based on real-time rolling forecasts has been developed. This model uses an initial prediction with a machine learning (ML) model, followed by differential decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). A Time-Aware Outlier-Sensitive Transformer (TS-Transformer) is then applied for integrated predictions. The ML-CEEMDAN-TSTF model demonstrated superior accuracy compared to basic ML models, decomposition integration models, and other Transformer-based models. This hybrid model explicitly incorporates time-scale differentiated information as a feature, improving the model’s adaptability to complex environmental data and predictive performance. The TS-Transformer was designed to make the model more sensitive to anomalies and peaks in time series, addressing issues such as anomalous data, uncertainty in water volume data, and suboptimal forecasting accuracy. The results indicated that: (1) the introduction of time-scale differentiated information significantly enhanced model accuracy; (2) ML-CEEMDAN-TSTF demonstrated higher accuracy compared to ML-CEEMDAN-Transformer; (3) the TS-Transformer-based decomposition integration model consistently outperformed those based on LSTM and eXtreme Gradient Boosting (XGBoost). Consequently, this research provides a precise and robust method for predicting reclaimed water volumes, which holds significant implications for research on clean water and water environment management.

Keywords: reclaimed water volumes; dynamic real-time forecasting; transformer; machine learning; forecast decomposition and ensemble technique (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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