Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008, Romania; REXDAN Research Infrastructure, "Dunarea de Jos" University of Galati, 98 George Cosbuc Street, 800385 Galati, Romania.
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008 Galati, Romania; The Modelling & Simulation Laboratory SMlab, "Dunarea de Jos" University of Galati, 47 Domneasca Street, 800008 Galati, Romania.
Sci Total Environ. 2023 Jun 25;879:162998. doi: 10.1016/j.scitotenv.2023.162998. Epub 2023 Mar 24.
The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.
多瑙河生态系统的健康和质量受到营养物质负荷（氮和磷）、有害物质和耗氧物质污染程度、微生物污染以及河流流量模式和泥沙输移机制变化的强烈影响。水质指数（WQI）是描述多瑙河生态系统健康和质量的重要动态属性。WQI 得分并不能反映水质的实际状况。我们提出了一种基于以下定性类别（极好（0-25）、良好（26-50）、较差（51-75）、很差（76-100）和极严重污染/不可饮用（>100）的新水质预测方案。利用人工智能（AI）进行水质预测是保护公众健康的一种有意义的方法，因为它有可能对有害水污染提供预警。本研究的主要目的是基于水物理、化学和流量状态参数以及相关的 WQ 指数得分，对 WQI 时间序列数据进行预测。前馈网络（CFN）模型与径向基函数网络（RBF）作为基准模型一起开发，使用 2011 年至 2017 年的数据，对 2018 年至 2019 年所有站点的 WQI 进行预测。19 个输入水质特征代表初始数据集。此外，随机森林（RF）算法通过选择被认为最相关的八个特征来改进初始数据集。两个数据集都用于构建预测模型。根据评估结果，CFN 模型产生了更好的结果（第一季度/第四季度的均方误差分别为 0.083/0.319，R 值分别为 0.940/0.911），优于 RBF 模型。此外，结果表明，当使用八个最相关的特征作为输入变量时，CFN 和 RBF 模型都可以有效地预测水质时间序列数据。此外，CFN 提供了最准确的短期预测曲线，可再现第一季度和第四季度（冬季）的 WQI。第二季度和第三季度的准确性略低。报告的结果清楚地表明，CFN 成功地预测了短期 WQI，因为它们可以学习历史模式并确定输入和输出变量之间的非线性关系。
