| 吴春晓,行鸿彦,张漪俊.基于BP神经网络的地温推演模型[J].电子测量与仪器学报,2017,31(10):1561-1567 |
| 基于BP神经网络的地温推演模型 |
| Ground temperature deduction model based on BP neural network |
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| DOI：10.13382/j.jemi.2017.10.005 |
| 中文关键词: 推演模型 地温 BP神经网络 |
| 英文关键词:deduction model ground temperature BP neural network |
| 基金项目:国家自然科学基金（61671248）、江苏省高校自然科学研究重大项目（15KJA460008）、江苏省“六大人才高峰”计划和江苏省“信息与通信工程”优势学科资助 |
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| Author | Institution |
| Wu Chunxiao | 1.School of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Lianshui Meteorological Bureau, Huai’an 223400, China |
| Xing Hongyan | School of Electronic & Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China |
| Zhang Yijun | Jiashan Meteorological Bureau, Jiaxing 314100, China |
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| 全文下载次数: 14664 |
| 中文摘要: |
| 针对地温观测中数据缺测和国家一般气象站无深层地温观测的问题，提出了采用BP神经网络建立的实时地温推演模型和深层地温推演模型（40～160 cm地温模型和320 cm地温模型）。前者可用于整点地温观测数据缺测的填补，后者用于无深层地温观测地区的地温估算。以样本站的小部分数据训练网络，用样本站全部数据测试，反复调试神经网络参数，筛选出误差性能好的地温模型，再用对比站数据测试地温模型的输出误差。实时地温模型样本站推演正确率为77.705%，对比站推演正确率为66.168%；40～160 cm地温模型72%以上的输出误差不大于0.5℃；320 cm地温模型83%以上的输出误差不大于1℃。实验结果表明，该方法建立的地温推演模型具有较高的精度和实用性。 |
| 英文摘要: |
| According to the missing data problem in the ground temperature observation, as well as national ordinary meteorological observing Station has no deep ground temperature observation service, real time ground temperature deduction models and deep ground temperature deduction models (40～160 cm ground temperature deduction model & 320 cm ground temperature deduction model) by the Back Propagation(BP) neural network is proposed. The former can be used to fill in the missing data of ground temperature observation data, and the latter can be used to estimate the deep ground temperature data in the area without no deep ground temperature observation. The BP neural network is trained by using a small number of samples and tested with all the data of the sample station. The neural network parameters are adjusted repeatedly, and the models with good error performance are selected. And then the output error of the ground temperature model is tested by using the contrast station data. The accuracy rate of real time ground temperature deduction model in the sample station is 77.705% as well as in the contrast station is 66.168%. More than 72% of the output error of the 40～160 cm ground temperature deduction model is less than 0.5 ℃, and more than 83% of the output error of the 320 cm ground temperature deduction model is less than 1℃. The experimental results show that the temperature deduction model established by this method has high precision and practicability. |
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