Modelling water quality parameters using model tree, random forest, and non-linear regression for Mula-Mutha River, Pune, India.

Evaluation of vital water-quality indicators, especially biological and chemical demand of oxygen (BOD and COD), is important for environmental factors, human health, and agricultural output. In the recent past, data-driven techniques (DDT) offer the ability to automate water quality assessment with more reliable and rapid evaluation. The present study thus aims to utilize various DDTs: random forest (RF), model tree (MT), and non-linear-regression (NLR) to predict vital water quality indicators such as BOD and COD for the three stretches of Mula-Mutha River, Pune, India. Since the river has three stretches: Mutha, Mula, and Mula-Mutha respectively, BOD-COD models have been developed separately for each using MT, RF, and NLR. Data analysis using a violin diagram is done to understand the data characteristics. Further, the models developed were developed using the appropriate input parameters for predicting BOD and COD. Error measures including coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the constructed models. The Taylor diagram, scatter plot, and hydrograph were also used for visual performance analysis. The findings suggest that the MT and RF techniques exhibit a stronger connection between the actual and anticipated levels of BOD and COD, with NLR following closely behind. Practical acceptance of these approaches is increased by RF in the form of trees, MT with an output in the form of a sequence of equations, and NLR with a single equation. These findings help us gain insight into DDT's water quality assessment model, which will also help future researchers and water quality professionals make decisions.