城市需水量预测方法比较  

Comparison of urban water demand forecasting methods

作　　者：刘春成[1,2] 曾智[3] 庞颖[1,2] 陆红飞[1,2] 白芳芳[1,2] 高峰[1,2] 

机构地区：[1]中国农业科学院农田灌溉研究所,河南新乡453002 [2]河南新乡农业水土环境野外科学观测试验站,河南新乡453002 [3]江西省水利规划设计院,江西南昌330000

出　　处：《水资源保护》2015年第6期179-183,共5页Water Resources Protection

摘　　要：为了提高城市需水量预测的精度,基于北京市2000—2011年的实际用水量数据,对比分析了BP神经网络预测模型、灰色GM(1,1)模型、非线性趋势模型和灰色-神经-趋势组合预测模型及其基于马尔科夫修正的各单项模型需水量预测结果。结果表明:组合预测模型优于各单项模型,基于马尔科夫修正的各模型优于各未修正预测模型。基于马尔科夫修正的灰色-神经-趋势组合预测模型预测精度最高、效果最好。Based on the actual water demands of Beijing city from 2000 to 2011,the forecasting results of BP neural network model,grey GM（ 1,1） model,nonlinear model and grey-neural-trend forecasting model and their corresponding model modified by Markov chain were contrasted and analyzed in order to improve the predicting precision of urban water demand. The results showed that the corresponding forecasting model was better than single models,and models modified by Markov chain were better than the unmodified models. In summary,grey-neuraltrend forecasting model modified by Markov chain has smaller errors and higher precision accuracy.

关 键 词：城市需水量 需水量预测 BP神经网络 灰色模型 非线性趋势模型 灰色-神经-趋势组合预测模型 马尔科夫修正模型 预测精度 

分 类 号：TV213.4[水利工程—水文学及水资源]