A TinyML Approach for Quantification of BOD and COD in Water

Water quality prediction is a crucial process before any consumption of water. Prediction and modeling methods are used for pollutants in water to deal with water pollution control. This work involves the use of a random forest learning algorithm to quantitate BOD and COD using parameter tuning to establish the importance of input variables. It uses minimal sensed quantitative parameters such as Temperature, pH, DO, and Conductivity along with categorical parameters. The trained model shows excellent efficiency compared to other models and is validated using the laboratory test results with a maximum error of 10%. It is computationally low-cost, requires minimal parameters, and is pruned to integrate and implement in an IoT hardware system, reducing the cost of expensive sensors.