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The discharge of domestic sewage by urban residents has always been a major factor causing water pollution in rivers, and with the continuous improvement of people's living standards, the amount of domestic sewage discharge is also increasing year by year. In order to predict the specific situation of domestic sewage discharge by urban residents in the Pearl River area in the future, this paper establishes an ARIMA model, combined with the existing 21 year domestic sewage discharge data, to make predictions for the next 10 years. During the prediction process, the original data is subjected to ADF test to obtain the optimal difference data, and this data is subjected to ACF and PACF analysis, and the model test table is drawn. The white noise sequence of the data was examined using both ACF and PACF. The analysis concluded that both AR residuals and MA residuals are sequences of white noise, thereby meeting the prerequisites for the ARIMA model. Subsequently, the formula for the ARIMA model was derived, and a model diagram of the time prediction sequence was illustrated.

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Prediction of Domestic Wastewater Discharge in Urban Towns of Pearl River Based on ARIMA

  • 会议论文

  • 发表日期：

    2025年

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  • Lidong Liu,
  • Yantao He,
  • Sijia Liu,
  • Suping Liu,
  • Yongbin Luo,
  • Jiayu Mo,
  • Chunzhen Liu

摘要

The discharge of domestic sewage by urban residents has always been a major factor causing water pollution in rivers, and with the continuous improvement of people's living standards, the amount of domestic sewage discharge is also increasing year by year. In order to predict the specific situation of domestic sewage discharge by urban residents in the Pearl River area in the future, this paper establishes an ARIMA model, combined with the existing 21 year domestic sewage discharge data, to make predictions for the next 10 years. During the prediction process, the original data is subjected to ADF test to obtain the optimal difference data, and this data is subjected to ACF and PACF analysis, and the model test table is drawn. The white noise sequence of the data was examined using both ACF and PACF. The analysis concluded that both AR residuals and MA residuals are sequences of white noise, thereby meeting the prerequisites for the ARIMA model. Subsequently, the formula for the ARIMA model was derived, and a model diagram of the time prediction sequence was illustrated.