Civil Engineering, School of Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland; Ryan Institute, National University of Ireland Galway, Ireland; MaREI Research Centre, National University of Ireland Galway, Ireland.
Civil Engineering, School of Engineering, College of Science and Engineering, National University of Ireland Galway, Ireland; Ryan Institute, National University of Ireland Galway, Ireland; MaREI Research Centre, National University of Ireland Galway, Ireland.
Water Res. 2022 Jul 1;219:118532. doi: 10.1016/j.watres.2022.118532. Epub 2022 May 1.
Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components - selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation - with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R = 0.91, RMSE = 4.4 for summer; R = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R = 0.92, RMSE = 3.2 for summer; R = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.
在这里，我们提出了一种改进的水质指数 (WQI) 模型，用于评估爱尔兰科克港的沿海水质。该模型涉及通常的四个 WQI 组成部分 - 选择包含的水质指标、指标值的子指数化、子指数加权和子指数聚合 - 并进行了改进，以使方法更加客观和数据驱动，并且不易受到掩盖和歧义错误的影响。该模型使用机器学习算法 XGBoost 根据对整体水质状况的相对重要性对水质指标进行排名和选择。在所提供的十个指标中，选择透明度、溶解无机氮、氨氮、BOD、叶绿素、温度和正磷酸盐作为夏季的指标，而总有机氮、溶解无机氮、pH 值、透明度和溶解氧作为冬季的指标。使用国家推荐的沿海水质指南值开发的线性插值函数用于子指数化水质指标，XGBoost 排名与排名顺序质心加权法结合使用，以确定子指数权重值。测试了八个子指数聚合函数 - 五个来自现有的 WQI 模型，三个由作者提出。将计算出的指数与使用多元线性回归 (MLR) 方法获得的指数进行比较，并使用 R 和 RMSE 作为聚合函数性能的指标。加权二次均值函数 (R = 0.91，RMSE = 4.4 夏季；R = 0.97，RMSE = 3.1 冬季) 和作者提出的未加权算术平均值函数 (R = 0.92，RMSE = 3.2 夏季；R = 0.97，RMSE = 3.2 冬季) 被确定为最佳函数，与其他函数相比，减少了掩盖和歧义问题。
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