DL E&C, Civil Business Division, Donuimun, D Tower, 134 Tongil-ro, Jongno-gu, Seoul, South Korea; Department of Civil and Environmental Engineering, Hongik University, Mapo-gu, Seoul, South Korea.
Department of Civil and Environmental Engineering, Hongik University, Mapo-gu, Seoul, South Korea.
Environ Pollut. 2024 Aug 15;355:124242. doi: 10.1016/j.envpol.2024.124242. Epub 2024 May 27.
Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water quality parameters in which their accurately in-situ measurements are impossible and face difficulties. Using a rich water quality dataset spanned from 1980 to 2023, we employed four machine learning-based models to estimate the British Colombia WQI (BCWQI) in the Lake Päijänne, Finland, without parameters like chemical oxygen demand (COD) and total phosphorus (TP). Measurement of both COD and TP is time-consuming, needs laboratory equipment and labor costs, and faces sampling-related difficulties. Our results suggest the machine learning-based models successfully estimate the BCWQI in Lake Päijänne when TP and COD are omitted from the dataset. The long-short term memory model is the least sensitive model to exclusion of COD and TP from inputs. This model with the coefficient of determination and root-mean squared error of 0.91 and 0.11, respectively, outperforms the support vector regression, random forest, and neural network models in real-time estimation of the BCWQI in Lake Päijänne. Incorporation of BCWQI with the machine learning-based models could enhance assessment of overall quality of inland-waters with a limited database in a more economical and time-saving way. Our proposed method is an effort to replace the traditional offline water quality assessment tools with a real-time model and improve understanding of decision-makers on the effectiveness of management practices on the changes in lake water quality.
水质指数（WQI）是评估内陆淡水总体质量的一种成熟工具。然而，使用 WQI 实时评估水生态系统的有效性通常受到某些水质参数的影响，这些参数的原位测量是不可能的，并且面临困难。我们利用一个从 1980 年到 2023 年的丰富水质数据集，使用四种基于机器学习的模型，在芬兰的 Päijänne 湖，在不考虑化学需氧量（COD）和总磷（TP）等参数的情况下，估算不列颠哥伦比亚省水质指数（BCWQI）。COD 和 TP 的测量既耗时又需要实验室设备和人工成本，并且面临与采样相关的困难。我们的结果表明，在从数据集中省略 COD 和 TP 的情况下，基于机器学习的模型成功地估算了 Päijänne 湖的 BCWQI。长短期记忆模型对从输入中排除 COD 和 TP 的影响最不敏感。该模型的决定系数和均方根误差分别为 0.91 和 0.11，在实时估算 Päijänne 湖的 BCWQI 方面优于支持向量回归、随机森林和神经网络模型。将基于机器学习的模型与 BCWQI 结合使用，可以以更经济和节省时间的方式，更全面地评估内陆水域的整体质量，而无需大量的数据库。我们提出的方法旨在用实时模型替代传统的离线水质评估工具，并提高决策者对管理实践对湖泊水质变化的有效性的理解。
