| 城市需水量预测方法比较 |
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| 引用本文： | 刘春成,曾智,庞颖,陆红飞,白芳芳,高峰. 城市需水量预测方法比较[J]. 水资源保护, 2015, 31(6): 179-183 |
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| 作者姓名： | 刘春成 曾智 庞颖 陆红飞 白芳芳 高峰 |
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| 作者单位： | 中国农业科学院农田灌溉研究所, 河南 新乡 453002; 河南新乡农业水土环境野外科学观测试验站, 河南 新乡 453002,江西省水利规划设计院, 江西 南昌 330000,中国农业科学院农田灌溉研究所, 河南 新乡 453002; 河南新乡农业水土环境野外科学观测试验站, 河南 新乡 453002,中国农业科学院农田灌溉研究所, 河南 新乡 453002; 河南新乡农业水土环境野外科学观测试验站, 河南 新乡 453002,中国农业科学院农田灌溉研究所, 河南 新乡 453002; 河南新乡农业水土环境野外科学观测试验站, 河南 新乡 453002,中国农业科学院农田灌溉研究所, 河南 新乡 453002; 河南新乡农业水土环境野外科学观测试验站, 河南 新乡 453002 |
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| 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。
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Comparison of urban water demand forecasting methods |
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| LIU Chuncheng,ZENG Zhi,PANG Ying,LU Hongfei,BAI Fangfang and GAO Feng. Comparison of urban water demand forecasting methods[J]. Water Resources Protection, 2015, 31(6): 179-183 |
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| Authors: | LIU Chuncheng ZENG Zhi PANG Ying LU Hongfei BAI Fangfang GAO Feng |
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| Affiliation: | Farmland Irrigation Research Institute, Chinese Academy of Agricult ural Science, Xinxiang 453002, China; Agriculture Water and Soil Environmental Field Science Research Station of Xinxiang City Henan Province, Xinxiang 453002, China,Jiangxi Provincial Water Conservancy Planning and Designing Institute, Nanchang 330000, China,Farmland Irrigation Research Institute, Chinese Academy of Agricult ural Science, Xinxiang 453002, China; Agriculture Water and Soil Environmental Field Science Research Station of Xinxiang City Henan Province, Xinxiang 453002, China,Farmland Irrigation Research Institute, Chinese Academy of Agricult ural Science, Xinxiang 453002, China; Agriculture Water and Soil Environmental Field Science Research Station of Xinxiang City Henan Province, Xinxiang 453002, China,Farmland Irrigation Research Institute, Chinese Academy of Agricult ural Science, Xinxiang 453002, China; Agriculture Water and Soil Environmental Field Science Research Station of Xinxiang City Henan Province, Xinxiang 453002, China and Farmland Irrigation Research Institute, Chinese Academy of Agricult ural Science, Xinxiang 453002, China; Agriculture Water and Soil Environmental Field Science Research Station of Xinxiang City Henan Province, Xinxiang 453002, China |
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| Abstract: | 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-neural-trend forecasting model modified by Markov chain has smaller errors and higher precision accuracy. |
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