灌溉排水学报2017，Vol.36Issue(11)：122-128,7.DOI:10.13522/j.cnki.ggps.2017.11.020
NAR神经网络的应用与检验 ——以城市居民生活需水定额为例
Application and Validation of Nonlinear Auto-regressive Neural Network Model:Taking Water Supply to Residential Area as an Example
摘要
Abstract
Nonlinear auto regressive (NAR) neural network with feedback and memory has many advantages in analysis of time series. Taking water supply to urban residential areas as an example, we proposed a NAR neural network model in this paper to analyze the domestic demand of Guizhou Province for water. The performance of the model was validated and the temporal variation in water requirement was then calculated. The predicted re-sults are summarized as follows.①The NAR neural network model was efficient and accurate, and the correla-tion coefficient and Nash coefficient of efficiency predicted by the model were 0.97 and 0.87 respectively. The results from the LBQ test showed that there was no autocorrelation among the predicted results. The AUC in the ROC curves of the predicted results was 0.938 (i.e., in the first level, and a higher accuracy).②In the calculat-ed rational water requirement, the predicted average water requirement per person in 2020 and 2030 was 137.72 liter per day and 132.94 liter per day respectively, meeting the requirements set in the design code of GB50013-2006. In summary, the NAR network is suitable for predicting the time series of water requirement and provides a reference for analyzing future demand for water.关键词
NAR神经网络模型/留一法交叉验证/Ljung-BoxQ检验/ROC曲线/定额预测Key words
NAR Neural Network/leave one out cross validation/Ljung-Box Q test/ROC curve/quota forecast分类
建筑与水利引用本文复制引用
李析男,王宁,梅亚东,赵先进..NAR神经网络的应用与检验 ——以城市居民生活需水定额为例[J].灌溉排水学报,2017,36(11):122-128,7.基金项目
国家自然科学基金面上项目(51479140) （51479140）
贵州省科技计划项目(黔科合SY字[2015]3006 （）
黔科合[2016]支撑2903 （）
黔科合重大专项字[2012] 6013号 （）
