Prediction of 5-day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms.

Abstract

Biochemical oxygen demand (BOD) is one of the most important variables indicating stream pollution with a severe condition of organic loading and maintaining aquatic life in ecosystems. Advanced monitoring techniques such as machine learning (ML) methods have been developed for an accurate, reliable, and cost-effective prediction of BOD. This study investigated the effectiveness of four stand-alone ML algorithms, namely, artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM), and six novel hybrid algorithms, namely, RF-SVM, ANN-SVM, GBM-SVM, RF-ANN, GBM-ANN, and RF-GBM, in predicting BOD of the Buriganga river system of Bangladesh. The feature importance analysis of RF algorithm indicated that chemical oxygen demand (COD), total dissolved solids (TDS), conductivity, total solids (TS), suspended solids (SS), and turbidity are the most influential parameters for predicting BOD5 . The significance of this study is the application of the novel hybrid models that resulted in higher prediction success; RF-SVM with the highest R2 value (0.908). The employed novel hybrid ML models can be particularly useful for efficient and systematic data management, water pollution control, and prevention in developing countries such as Bangladesh. PRACTITIONER POINTS: Investigated the efficiency of four stand-alone and six novel hybrid ML models for predicting BOD in a river of Bangladesh. The significance of this study is the application of the six novel hybrid models that resulted in higher prediction success. The best three prediction models were RF-SVM, ANN-SVM, and GBM-SVM with a prediction success of 91%, 89.6%, and 88.8% respectively. ML models indicated COD, conductivity, TDS, TS, SS, and turbidity as the most influential variables for predicting BOD. The novel hybrid models can be useful for developing countries for efficient systematic data management, pollution control, and prevention strategies.