Civil Department, Oriental College of Technology (OCT), Bhopal, India.
Civil Department, Vishwakarma Institute of Information Technology (VIIT), Pune, India.
Environ Monit Assess. 2024 Oct 12;196(11):1047. doi: 10.1007/s10661-024-13206-9.
Evaluation of vital water-quality indicators, especially biological and chemical demand of oxygen (BOD and COD), is important for environmental factors, human health, and agricultural output. In the recent past, data-driven techniques (DDT) offer the ability to automate water quality assessment with more reliable and rapid evaluation. The present study thus aims to utilize various DDTs: random forest (RF), model tree (MT), and non-linear-regression (NLR) to predict vital water quality indicators such as BOD and COD for the three stretches of Mula-Mutha River, Pune, India. Since the river has three stretches: Mutha, Mula, and Mula-Mutha respectively, BOD-COD models have been developed separately for each using MT, RF, and NLR. Data analysis using a violin diagram is done to understand the data characteristics. Further, the models developed were developed using the appropriate input parameters for predicting BOD and COD. Error measures including coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE) were used to evaluate the constructed models. The Taylor diagram, scatter plot, and hydrograph were also used for visual performance analysis. The findings suggest that the MT and RF techniques exhibit a stronger connection between the actual and anticipated levels of BOD and COD, with NLR following closely behind. Practical acceptance of these approaches is increased by RF in the form of trees, MT with an output in the form of a sequence of equations, and NLR with a single equation. These findings help us gain insight into DDT's water quality assessment model, which will also help future researchers and water quality professionals make decisions.
评价重要的水质指标，特别是生物需氧量和化学需氧量，对于环境因素、人类健康和农业产出都很重要。在最近的一段时间里，数据驱动技术（DDT）提供了更可靠、更快速的水质评估自动化能力。因此，本研究旨在利用各种 DDT 技术：随机森林（RF）、模型树（MT）和非线性回归（NLR）来预测印度浦那的穆拉-穆塔河的三个河段的重要水质指标，如 BOD 和 COD。由于这条河有三个河段：穆塔、穆拉和穆拉-穆塔，因此分别使用 MT、RF 和 NLR 为每个河段开发了 BOD-COD 模型。使用小提琴图进行数据分析，以了解数据特征。此外，为了预测 BOD 和 COD，使用了适当的输入参数来开发所开发的模型。使用相关系数（R）、平均绝对误差（MAE）和均方根误差（RMSE）等误差度量来评估构建的模型。还使用泰勒图、散点图和水位图进行了可视化性能分析。研究结果表明，MT 和 RF 技术在 BOD 和 COD 的实际和预期水平之间表现出更强的联系，而 NLR 紧随其后。RF 以树的形式增加了对这些方法的实际接受度，MT 以方程序列的形式输出，NLR 则以单个方程的形式输出。这些发现有助于我们深入了解 DDT 的水质评估模型，这也将有助于未来的研究人员和水质专业人员做出决策。
