Multivariate prediction of total organic carbon in river water using random forest and deep learning regression algorithms

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Abstract

Total organic carbon (TOC) is used to determine the total amount of organic compounds in water. It has been used to indicate water purity levels for industrial water for decades. Analyzing TOC in water is often time-consuming and an expensive activity, requiring the use of multiple high-precision sensors. Our main aim of this study was to compare the use of the Random Forest (RF) algorithm, one-dimensional convolutional neural network (1D-CNN), and multilayer perceptron (MLP) in predicting TOC using water quality parameters selected based on their strength of correlation. RF was chosen because it can model complex interactions between the various parameters and is resistant to overfitting. 1D-CNN can handle local dependencies and spatial relationships between input features whereas MLP handles independent numerical features in the dataset. The dataset was obtained from analysis done in Puget Sound marine waters around Seattle King County in the USA at the Duwamish River at three sampling locations. Learning curve analysis demonstrated that the dataset size was sufficient for stable training and generalization, while five-fold cross-validation confirmed consistent model performance across data splits. The effects of wet and dry seasons on the parameter levels were done and their impact on RF model accuracy was assessed. The selection of parameters based on Gini importance ranking was done to evaluate their effect on the accuracy of the RF model. Our results indicated that the prediction of TOC using RF regression was the most accurate with a coefficient of determination (R²) of 0.732. The 1D-CNN and MLP had R² of 0.714 and 0.638, respectively. The mean absolute error (MAE) for RF, 1D-CNN, and MLP were 0.120 mg/L, 0.244 mg/L and 0.270 mg/L, respectively. It was concluded that the RF algorithm would be more feasible in predicting TOC in river water than the two deep learning methods.

Keywords

random forests / convolutional neural network / multilayer perceptron / regression / machine learning / total organic carbon

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