Real-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.

实时水质监测是一个复杂的系统，因为它涉及许多要监测的质量参数，这些参数的性质以及它们之间的非线性相互依赖性。在构建智能系统中至关重要的智能算法是构建可靠，便捷的监视系统的良好选择。要分析水质，我们需要通过物联网框架使用不同的在线水质传感器实时了解，建模和监控水污染。但是，由于以下几个原因，许多水质参数无法轻松在线测量：高成本的传感器，低采样率，异质传感器很少的多个处理阶段，频繁清洗和校准的要求以及不同水之间的空间和应用依赖性身体。软传感器是水质监测的一种高效便捷的替代方法。在本文中，我们提出了一种基于机器学习的软传感器模型来估算生物需氧量（BOD），这是一个耗时且具有挑战性的测量过程。我们还提出了一种用于在云层和边缘层上实现软传感器的系统架构，以便边缘设备可以通过监视水质实时做出自适应决策。还针对不同机器学习（ML）算法的预测精度，学习时间和决策时间对边缘和云节点的计算性能进行了比较研究。本文以实际应用为例，建立了BOD软传感器高效，成本较低且合理准确的特点。这里，IBK ML技术被证明是预测BOD的最有效方法。该实验装置使用100个STP水样品的测试读数来评估IBK技术的性能，并以相关系数= 0.9273，MAE = 0.082，RMSE = 0.1994，RAE = 17.20％，RRSE = 37.62％，和边缘响应时间= 0.15 s。