Prediction of biochemical oxygen demand (BOD) as the main pollution indicators of organic pollution in freshwater resources is necessary. In the present work, a hybrid wavelet-genetic programming (WGP) method was implemented to improve prediction of BOD. The Shannon entropy was used to identify the optimal input combinations of WGP. In addition, an investigation was done to find which functions of wavelet and decomposition levels have better results in conjunction with genetic programming (GP). For comparison of WGP efficiency, five machine learning methods consisting of WANN (wavelet-artificial neural network), ANN (artificial neural network), GP, DT (decision tree) and BN (Bayesian network) were considered. Experiments on wavelet-processed data revealed that the best results were obtained when the models WGP and WANN were calibrated at three levels of decomposition using the Dmey mother wavelet function. The WGP model created rational forecasts for the peak BOD values. The results show that the use of Shannon entropy is suitable for determining the optimal composition of inputs to machine learning methods. Comparison of the results indicate that the WGP model is superior to the GP, ANN, DT, BN and WANN models based on data from the Varian Hotel and Dam Input stations.

预测生化需氧量（BOD）作为淡水资源有机污染的主要污染指标是必要的。在当前工作中，实现了一种混合小波遗传规划（WGP）方法，以提高对BOD的预测。香农熵被用来识别WGP的最佳输入组合。此外，还进行了一项调查，以发现与遗传程序设计（GP）结合使用时，小波和分解级别的函数具有更好的结果。为了比较WGP效率，考虑了五种机器学习方法，包括WANN（小波-人工神经网络），ANN（人工神经网络），GP，DT（决策树）和BN（贝叶斯网络）。小波处理数据的实验表明，使用Dmey母小波函数在三个分解级别上对模型WGP和WANN进行校准时，可获得最佳结果。WGP模型为BOD峰值创建了合理的预测。结果表明，使用Shannon熵适合确定机器学习方法的最佳输入组成。结果比较表明，基于来自瓦里安酒店和大坝输入站的数据，WGP模型优于GP，ANN，DT，BN和WANN模型。