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Water supply forecasting methods can be categorized into two primary types: traditional forecasting methods and machine learning methods. Traditional methods have limited forecasting accuracy for daily water supply, while machine learning methods have better model description ability and can find data details that are difficult to capture by traditional algorithms. In this paper, a water supply forecasting method based on the Long Short-Term Memory (LSTM) network is proposed. The water supply data of Xinmin Water Plant in Dianjiang County from March 1, 2024, to March 30, 2024, are used to verify the method. The error analysis of the error results shows that the RMSE is 0.051718, and the NSE is 0.960363. The forecasting of rural water supply based on LSTM has high forecasting accuracy and stability, which is an effective forecasting method.
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