Biochemical oxygen demand prediction: development of hybrid wavelet-random forest and M5 model tree approach using feature selection algorithms

Supplying adequate water to individuals and maintaining water supplies to support human life, particularly to rapidly urbanizing communities, are of paramount importance in the development of urban areas in each country worldwide. In turn, maintaining water resource quality and avoiding permanent damage as a consequence of environmental pollution and unsustainable off-take from sources such as rivers and aquifers should be considered as important as the water supply quantity. In this study, random forest (RF) and M5 model tree (M5) models were used to predict water biochemical oxygen demand (BOD). Having decomposed the input variables by wavelet transform, based on the feature selection algorithms (FS) (relief (RA), correlation (CA), principal component analysis (PCA), and ant colony optimization (ACO) algorithms), the important components were recognized and inserted into the RF and M5 models. The proposed approach was applied to Karun River in Ahvaz station on a monthly basis from 2006 to 2018. The results showed that the RF model had better performance with R = 0.872, MAE = 0.0312, and RMSE = 0.0332 values for the variable of BOD compared with the M5 model with R = 0.751, MAE = 0.0377, and RMSE = 0.0468 values. In addition, comparing RF and hybrid models, the purposed hybrid models were considered as viable options to improve the prediction accuracy of BOD. The findings also showed that, among the hybrid models, the WRF-PCA model with R = 0.927, MAE = 0.0198, and RMSE = 0.0241 values was the best model for the prediction of BOD values.