基于决策树模型的黄河水沙变化预测

With the development of water conservancy informatization, a large amount of data resources have been accumulated in the field of Yellow River water and sediment management. Compared with the past, the processing methods of these data have made many new breakthroughs, declaring that water and sediment management has entered the era of big data. Based on the actual monitoring data of water flow and sediment concentration at a hydrological station on the Yellow River downstream of the Xiaolangdi Reservoir from 2016 to 2021, seasonal and periodic visual analysis of water flow and sediment concentration is conducted. Subsequently, three machine learning regression models, namely random forest, decision tree, and extreme gradient boosting, were established to fit the trends of water flow and sediment concentration, and the goodness of fit and mean square error of the three models were compared. The results show that the water and sediment changes of the Yellow River exhibit obvious seasonality and periodicity, and the performance of the decision tree algorithm in predicting and analyzing water and sediment changes is significantly better than that of the random forest and extreme gradient boosting algorithm. It can effectively fit the trend of water and sediment changes and has certain reference value for future water and sediment management in the Yellow River Basin.