为解决XGBoost模型参数调整复杂和预测水质数据准确率低的问题,设计了一种基于灰狼算法(GWO)优化XGBoost的预测算法。首先利用灰狼优化算法全局寻参收敛能力强的优点,使用GWO优化XGBoost的超参数(最大生成树数目、学习率以及树的最大深度)进行优化,获取XGBoost的最佳预测表现。其次通过对水质关键数据的预处理提高数据的可靠性,使用多种算法进行对比分析实验。结果表明:与LSTM、未经GWO优化参数的XGBoost相比,采用灰狼算法优化后的XGBoost模型具有较好的非线性预测能力 ...
牛景辉
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