基于多粒度特征和XGBoost模型的城市日供水量预测

城市日供水量预测对供水部门具有十分重要的现实意义。为提高城市日供水量预测精度,以某市历史用水数据为基础,构建多粒度特征,并利用Pearson相关系数进行特征的筛选,基于XGBoost模型构建城市日供水量预测模型。本模型通过在训练集上进行训练和学习,在测试集上的平均绝对误差为70 571 t/d,平均相对误差为1.4%;传统的回归预测方法如随机森林法和支持向量机法,平均绝对误差分别为84 366 t/d和88 848 t/d。本模型预测精度更高,说明此模型可行、有效,具有一定的应用价值。

Predicting Urban Daily Water Supply Based on Multi-granularityFeature and XGBoost Integrated Model

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.