Abstract：
Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes.
摘要：
水质极端，水质模型经常很难预测，是供水设施的严重关切。现有的大多数水质模型都使用平均误差函数来最大化水质平均值的可预测性。本文介绍了一种复合分位数回归神经网络(CQRNN)模型,它通过最小化复合分位数回归误差函数来同时估计非交叉回归分位数。该方法可以提高极值的预测能力。本文评估了CQRNN预测浊度和总有机碳（TOC）极值的性能，并与分位数回归（QR）进行了比较。线性回归(LR)，和k-最近邻(KNN)在赫奇赫奇区域水系统的应用中，这是旧金山的主要供水，CA.CQRNN优于QR，LR,和KNN用于预测浊度和TOC的平均趋势和极值，特别是对于非高斯浊度数据。在不同的训练样本大小上，CQRNN的性能相对于其他方法是最稳定的。
