Accurate streamflow forecasting is very useful in water resources management, design of hydraulic structures, and almost all issues related to the use of water and water resources, especially in arid regions that have increased in recent years. Since water is the source of all life and the most important basic element for humanity to continue its life, streamflow prediction studies increase its importance daily. This research combined the boosted tree (BT) model with robust empirical mode decomposition, empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, empirical wavelet transforms and variational mode decomposition for predicting daily average streamflow data. While historical streamflow data was input in the model's setup, one-day lead-time streamflow data was used as the target. 70% of the data is reserved for training and the rest for testing. 5-fold cross-validation technique was used to solve the over-fitting problem. The coefficient of determination, mean squared error, Nash-Sutcliffe efficiency and percent bias statistical criteria and Taylor diagrams, polar plot, scattering diagram, and violin plot were used to determine the algorithm's success. At the end of the study, it was found that the most successful streamflow predictions were made with the variational mode decomposition-based BT hybrid approach.

准确的水流预测对于水资源管理、水工建筑物设计以及几乎所有与水和水资源利用有关的问题都非常有用，特别是在近年来增加的干旱地区。水是一切生命之源，是人类延续生命最重要的基本元素，水流预测研究的重要性与日俱增。本研究将提升树（BT）模型与鲁棒经验模态分解、经验模态分解、带自适应噪声的完全集合经验模态分解、经验小波变换和变分模态分解相结合，用于预测日平均水流数据。在模型设置中输入历史流量数据时，一日前置时间流量数据被用作目标。70% 的数据保留用于训练，其余的用于测试。使用5折交叉验证技术来解决过拟合问题。确定系数、均方误差、Nash-Sutcliffe 效率和百分比偏差统计标准以及泰勒图、极坐标图、散射图和小提琴图用于确定算法的成功。研究结束时发现，最成功的水流预测是采用基于变分模态分解的 BT 混合方法进行的。和小提琴图用于确定算法的成功。研究结束时发现，最成功的水流预测是采用基于变分模态分解的 BT 混合方法进行的。和小提琴图用于确定算法的成功。研究结束时发现，最成功的水流预测是采用基于变分模态分解的 BT 混合方法进行的。