Chinese Water Demand Forecast Based on iTransformer Model

Abstract

This paper presents a novel deep learning-based model for forecasting water demand Specifically, a transformer network architecture-based iTransformer model is introduced to forecast total water demand at both country and province levels over the medium term. Comparative evaluations with Transformer, PatchTST, and LSTM models are conducted across various forecasting lengths, with hyperparameter optimization performed through grid search. The optimal model and parameters are then applied to historical water demand data from 2000 to 2023, yielding forecasts for subsequent years. Results demonstrate that the iTransformer model achieves the lowest RMSE (92.72/1.39/22.71/21.69/9.16), MAE (68.65/1.11/17.42/13.38/5.85), and MAPE (0.01/0.28/0.03/0.08/0.01) in forecasting water demand for China, Beijing, Jiangsu, Zhejiang, and Guangdong respectively. The study emphasizes the importance of considering population size and economic activity in managing socio-economic water demand in China, advocating for a balanced approach to water resource utilization. While the research offers valuable insights for water management authorities, challenges remain in quantifying future water allocations and refining prediction methodologies for enhanced accuracy. Nonetheless, the study paves the way for future research in advancing water demand forecasting methodologies.

Keywords

• Deep learning
• water demand forecasting
• iTransformer