Gazzaz Nabeel M, Yusoff Mohd Kamil, Ramli Mohammad Firuz, Juahir Hafizan, Aris Ahmad Zaharin
Water Environ Res. 2015 Feb;87(2):99-112. doi: 10.2175/106143014x14062131179276.
This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.
本文描述了一种人工神经网络（ANN）模型的设计，该模型以土地利用面积作为预测因子来预测水质指数（WQI）。建模过程中采用了马来西亚金塔河十年的土地利用统计数据和水质数据记录。使用网络架构7-23-1、反向传播训练算法以及0.02的学习率，获得了最准确的WQI预测结果。该模型的WQI预测与实测WQI值具有显著（p < 0.01）、正相关且非常高的相关性（ρs = 0.882）。敏感性分析表明，土地利用类别对WQI预测的相对重要性顺序为：采矿>橡胶>森林>伐木>城市地区>农业>油棕。这些结果表明，人工神经网络是将水质与土地利用相关联的高度可靠手段，从而将土地利用开发与河流水质管理相结合。
