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

作者：Xiangyu Sun, Lina Zhang, Chao Wang, Yiyang Yang, Hao Wang · 发表于：Sustainability · 年份：2024 · DOI：10.3390/su16156598 · 被引用次数：10 · 研究领域：Hydrological Forecasting Using AI、Energy Load and Power Forecasting、Flood Risk Assessment and Management

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...