关键词: 城市日供水量, 多粒度特征, Pearson相关系数, XGBoost模型, 预测精度

Abstract: Predicting the quantity of urban daily water supply is of great significance to water supply department in practice. To ameliorate the accuracy of predicting urban water supply, an XGBoost (eXtreme Gradient Boosting) integrated model of predicting the urban water supply is built based on the historical data of water supply with multi-granular features. Pearson correlation coefficient is used to select the optimal factor combination. Through training and learning on the training set, the results show that the average absolute error of this model is 70 571 t/d, and the average relative error is 1.4% on test set. Compared with traditional regression prediction methods such as random forest and support vector machine with the average absolute error amounting to 84 366 t/d and 88 848 t/d, respectively, the present method has higher prediction accuracy, indicating that the model is feasible and effective.

Key words: urban daily water supply, multi-granularity feature, Pearson correlation coefficient, XGBoost model, prediction accuracy