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Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China

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  • Jun Shu
  • Xinyu Xia
  • Suyue Han
  • Zuli He
  • Ke Pan
  • Bin Liu

Abstract

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Suggested Citation

  • Jun Shu & Xinyu Xia & Suyue Han & Zuli He & Ke Pan & Bin Liu, 2024. "Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0302558
    DOI: 10.1371/journal.pone.0302558
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