Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India.
Department of Bioproducts and Biosystems Engineering, University of Minnesota, Twin Cities, Minneapolis, USA.
Environ Sci Pollut Res Int. 2022 Sep;29(43):65259-65275. doi: 10.1007/s11356-022-20385-w. Epub 2022 Apr 29.
River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When data is deficient and noisy, it creates missing and conflicting parameters within their complex inter-relationships. It is also essential to model how climatic variations and river discharge affect water quality. The present study proposes a cloud-based efficient and resourceful machine learning (ML) modeling framework using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and advanced particle swarm optimization (PSO). The framework assesses the sensitivity of five critical water quality parameters namely biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, temperature, and total coliform toward WQI of the River Ganges in India. Monthly datasets of these parameters, river flow, and climate components (rainfall and temperature) for a nine-year (2011-2019) period have been used to build the models. We also propose collecting the data by placing various monitoring sensors in the river and sending the data to the cloud for analysis. This helps in continuous monitoring and analysis. Results indicate that ANN and ANFIS capture the nonlinearity in the relationship among water quality parameters with a root mean square error (RMSE) of 7.5 × 10 (0.002%) and 1.02 × 10 (0.029%), respectively, while the combined ANN-PSO model gives normalized mean square error (NMSE) of 0.0024. The study demonstrates the role of cloud-based machine learning in developing watershed protection and restoration strategies by analyzing the sensitivity of individual water quality parameters while predicting water quality under changing climate and river discharge.
河流水质是各种生物物理化学参数的函数，可以对这些参数进行聚合，以计算水质指数 (WQI)。然而，这些参数的非线性和不确定行为建模具有挑战性。当数据不足且存在噪声时，它们在复杂的相互关系中会产生缺失和冲突的参数。还需要建模气候变化和河流流量如何影响水质。本研究提出了一种基于云的高效且资源丰富的机器学习 (ML) 建模框架，使用人工神经网络 (ANN)、自适应神经模糊推理系统 (ANFIS) 和高级粒子群优化 (PSO)。该框架评估了五个关键水质参数即生化需氧量 (BOD)、溶解氧 (DO)、pH 值、温度和总大肠菌群对印度恒河水质指数 (WQI) 的敏感性。使用了这些参数、河流流量以及气候成分（降雨和温度）的月数据集，时间跨度为九年（2011-2019 年），用于构建模型。我们还建议通过在河流中放置各种监测传感器来收集数据，并将数据发送到云端进行分析。这有助于进行连续监测和分析。结果表明，ANN 和 ANFIS 以均方根误差 (RMSE) 为 7.5×10 (0.002%) 和 1.02×10 (0.029%) 的方式捕捉水质参数之间关系的非线性，而组合的 ANN-PSO 模型给出归一化均方误差 (NMSE) 为 0.0024。该研究通过分析单个水质参数的敏感性并预测气候变化和河流流量下的水质，展示了基于云的机器学习在开发流域保护和恢复策略方面的作用。
