Machine learning based soft sensor model for BOD estimation using intelligence at edge

Abstract

AbstractReal-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.