Department of Environmental Sciences, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 Serdang, Selangur Darul Ehsan, Malaysia.
Mar Pollut Bull. 2012 Nov;64(11):2409-20. doi: 10.1016/j.marpolbul.2012.08.005. Epub 2012 Aug 25.
This article describes design and application of feed-forward, fully-connected, three-layer perceptron neural network model for computing the water quality index (WQI)(1) for Kinta River (Malaysia). The modeling efforts showed that the optimal network architecture was 23-34-1 and that the best WQI predictions were associated with the quick propagation (QP) training algorithm; a learning rate of 0.06; and a QP coefficient of 1.75. The WQI predictions of this model had significant, positive, very high correlation (r=0.977, p<0.01) with the measured WQI values, implying that the model predictions explain around 95.4% of the variation in the measured WQI values. The approach presented in this article offers useful and powerful alternative to WQI computation and prediction, especially in the case of WQI calculation methods which involve lengthy computations and use of various sub-index formulae for each value, or range of values, of the constituent water quality variables.
本文描述了前馈、全连接、三层感知器神经网络模型的设计和应用，用于计算金马伦河（马来西亚）的水质指数（WQI）(1)。建模工作表明，最佳的网络架构是 23-34-1，与快速传播（QP）训练算法相关的最佳 WQI 预测；学习率为 0.06；QP 系数为 1.75。该模型的 WQI 预测与实测 WQI 值具有显著的正相关（r=0.977，p<0.01），表明模型预测解释了实测 WQI 值变化的 95.4%左右。本文提出的方法为 WQI 计算和预测提供了有用且强大的替代方法，特别是在涉及冗长计算和为水质变量的每个值或值范围使用各种子指数公式的 WQI 计算方法的情况下。
