Department of Civil Engineering, SRKR Engineering College, India; Centre for Clean and Sustainable Environment, SRKR Engineering College, India.
Department of Civil Engineering, SRKR Engineering College, India; Centre for Clean and Sustainable Environment, SRKR Engineering College, India.
Sci Total Environ. 2024 Jan 1;906:167386. doi: 10.1016/j.scitotenv.2023.167386. Epub 2023 Sep 26.
Water quality surveillance is tough, and a specific timely management is necessary for the inland aquaculture ponds and ecology as well. Real time quality monitoring involves the study of numerous parameters includes physical (turbidity, temperature, and specific conductivity), chemical (pH, calcium, manganese, chlorides, iron, biochemical oxygen demand), and biological (bacteria and algae). It is also crucial to recognize the inter-dependence among the parameters. Alternatively, these relationships can be predicted with statistical and numerical modelling. Organic strength parameter 5-day biochemical oxygen demand (BOD) is a significant parameter to evaluate since its impact is very high on the quality of water, aquatic life, and other biological concerns. This study focuses on the prediction of BOD using six traditional and four boosting algorithms considering ten input physicochemical attributes. The attributes were fine-tuned for highly precise predictions by removing extreme values from the data set using data outlier treatment. The prediction results are compared using performance metrics such as coefficient of determination (R), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The findings revealed that boosting algorithms outperform the results of traditional models with the highest prediction accuracy. Among the boosting algorithms, eXtreme Gradient Boosting algorithm (XGBM) is found highly appropriate for the inland aquaculture waters with R = 0.95, RMSE = 0.31, MSE = 0.09, MAE = 0.1. Finally, this study provides a systematic evaluation of the BOD in the aquaculture waters and has a significant contribution to water management and eco-balance.
水质监测具有挑战性，内陆水产养殖池塘和生态系统也需要进行具体的实时管理。实时水质监测涉及到许多参数的研究，包括物理参数（浊度、温度和比电导率）、化学参数（pH 值、钙、锰、氯化物、铁、生化需氧量）和生物参数（细菌和藻类）。认识到参数之间的相互依存关系也很重要。或者，可以使用统计和数值建模来预测这些关系。五日生化需氧量（BOD）是一个重要的有机强度参数，因为它对水质、水生生物和其他生物问题的影响非常大，因此需要对其进行评估。本研究考虑了十个输入理化属性，使用六种传统算法和四种提升算法来预测 BOD。通过使用数据异常值处理从数据集去除极值，对属性进行微调，以实现高度精确的预测。使用性能指标，如决定系数（R）、均方根误差（RMSE）、均方误差（MSE）和平均绝对误差（MAE）来比较预测结果。研究结果表明，提升算法的预测结果优于传统模型，其中提升算法的预测精度最高。在提升算法中，极端梯度提升算法（XGBM）非常适合内陆水产养殖水，其 R 值为 0.95、RMSE 为 0.31、MSE 为 0.09、MAE 为 0.1。最后，本研究对水产养殖水中的 BOD 进行了系统评估，对水管理和生态平衡有重要贡献。
