Abstract
Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the contradiction between water supply and demand in the basin, with water demand for economic growth as the main target, a hybrid moving autoregressive and deep neural network model (ARMA-DNN) was developed in this study, and four commonly used statistical indicators (MAE, RMSE, MSE, and R2) were selected to evaluate the performance of the model. Finally, the validity and practicality of the model were verified by taking the Minjiang River basin in China as an example. The results show that (a) the model can predict future water demand more accurately under the conditions of actual water consumption changes, (b) the ideal agricultural production in the Minjiang River Basin is predicted to be reached 2.26 × 109t in 2021, and (c) the highest industrial economic efficiency in Chengdu is 1.51 × 109yuan, while water satisfaction reaches 102%. This means that effective water demand forecasting can alleviate water demand conflicts under climate change conditions to a certain extent. At the same time, watershed managers can develop different water allocation schemes based on the prediction results of the hybrid ARMA-DNN model.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant numbers [71771157]) and the Fundamental Research Funds for the System Science and Enterprise Development Research Center of Sichuan Key Research Base of Social Sciences (Grant number [Xq21B11]).
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Guangze Liu: conceptualization, methodology, validation, data curation, and methodology and writing–original draft. Mingkang Yuan: conceptualization, methodology, validation, revision of the manuscript, editing, and software. Xudong Chen: conceptualization and software. Xiaokun Lin: validation, data curation, and writing–original draft; Qingqing Jiang: data curation, supervision, and writing–review and editing. The authors read and approved the final manuscript.
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The experimental scheme was formulated by the water resources allocation guidelines of Dujiangyan Management Committee and obtained personal written informed consent.
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Highlights
(1) A hybrid ARMA-DNN model is constructed to forecast water consumption.
(2) Analyzed the water demand plans of the watershed water use sectors.
(3) Assessed water allocation schemes corresponding to future water demand.
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Liu, G., Yuan, M., Chen, X. et al. Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks. Environ Sci Pollut Res 30, 11946–11958 (2023). https://doi.org/10.1007/s11356-022-22943-8
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DOI: https://doi.org/10.1007/s11356-022-22943-8