Abstract：
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, BOD5, 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 R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer; R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer; R2 = 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.
摘要：
这里,我们提出了一种改进的水质指数（WQI）模型，用于使用科克港评估沿海水质，爱尔兰,作为案例研究。该模型涉及通常的四个WQI组件-选择包含的水质指标，指标值的子索引，子索引加权和子索引聚合-进行改进，以使该方法更加客观和数据驱动，并且不太容易出现日蚀和歧义错误。该模型使用机器学习算法，XGBoost,根据对总体水质状况的相对重要性，对水质指标进行排序和选择。在有数据的十项指标中，透明度，溶解的无机氮，氨态氮，BOD5，叶绿素，夏季选择温度和正磷酸盐，而总有机氮，溶解的无机氮，pH值,透明度和溶解氧选择冬季。使用国家推荐的沿海水质指导值开发的线性插值函数用于水质指标的子索引，并将XGBoost排名与排名顺序质心加权方法结合使用以确定子指标权重值。测试了八个子索引聚合函数-五个来自现有的WQI模型，三个由作者提出。将计算出的指标与使用多元线性回归（MLR）方法获得的指标进行比较，并将R2和RMSE用作聚集函数性能的指标。作者提出的加权二次均值函数（夏季R2=0.91，RMSE=4.4；冬季R2=0.97，RMSE=3.1）和未加权算术平均函数（夏季R2=0.92，RMSE=3.2；冬季R2=0.97，RMSE=3.2）被确定为最佳函数，与其他函数相比，显示出减少的日食和歧义问题。
