Using multiple linear regression for biochemical oxygen demand prediction in water

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Abstract

Biochemical oxygen demand (BOD) is an important water quality measurement but takes five days or more to obtain. This may result in delays in taking corrective action in water treatment. Our goal was to develop a BOD predictive model that uses other water quality measurements that are quicker than BOD to obtain; namely pH, temperature, nitrogen, conductivity, dissolved oxygen, fecal coliform, and total coliform. Principal component analysis showed that the data spread was in the direction of the BOD eigenvector. The vectors for pH, temperature, and fecal coliform contributed the greatest to data variation, and dissolved oxygen negatively correlated to BOD. K-means clustering suggested three clusters, and t-distributed stochastic neighbor embedding showed that BOD had a strong influence on variation in the data. Pearson correlation coefficients indicated that the strongest positive correlations were between BOD, and fecal and total coliform, as well as nitrogen. The largest negative correlation was between dissolved oxygen, and BOD. Multiple linear regression (MLR) using fecal, and total coliform, dissolved oxygen, and nitrogen to predict BOD, and training/test data of 80%/20% and 90%/10% had performance indices of RMSE=2.21 mg/L, r=0.48 and accuracy of 50.1%, and RMSE=2.18 mg/L, r=0.54 and an accuracy of 55.5%, respectively. BOD prediction was better than previous MLR models. Increasing the percentage of the training set above 80% improved the model accuracy but did not significantly impact its prediction. Thus, MLR can be used successfully to estimate BOD in water using other water quality measurements that are quicker to obtain.

Keywords

machine learning / BOD / multiple linear regression / water treatment / contamination

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