| 基于缺省因子的BP-ANN土壤墒情预报简化模型 |
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| 引用本文： | 黄令淼,任树梅,杨培岭,税朋勃,曹建武,周嵘.基于缺省因子的BP-ANN土壤墒情预报简化模型[J].中国农业大学学报,2013,18(5):166-172. |
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| 作者姓名： | 黄令淼 任树梅 杨培岭 税朋勃 曹建武 周嵘 |
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| 作者单位： | 中国农业大学 水利与土木工程学院, 北京 100083;中国农业大学 水利与土木工程学院, 北京 100083;中国农业大学 水利与土木工程学院, 北京 100083;北京市水利水电技术中心, 北京 100073;北京市水利水电技术中心, 北京 100073;北京市水利水电技术中心, 北京 100073 |
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| 基金项目： | 北京市科学技术计划资助项目(pxm2009_035324_092070); 北京市干旱风险评估项目 |
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| 摘 要： | 对影响土壤墒情的主要气象要素,平均气温、相对湿度、日照时数、平均风速、蒸降差和前一旬土壤墒情进行分析合并,建立BP-ANN土壤墒情预报6因子模型;通过缺省因子检验法,判断土壤墒情对6个因子敏感程度,简化冗余因子,构建BP-ANN的3因子(相对湿度、日照时数、前一旬土壤相对湿度)墒情预报模型。结果表明:3因子模型均方根误差3.55,具有数据收集和处理量小的优点,基本能够达到所需精度和拟合度。在北京市山区和平原区2个典型站点的模拟检验表明,3因子模型实测值与预测值的拟合关系均达到极显著相关水平,可操作性强的特点。
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| 关 键 词： | 土壤墒情 预测预报 人工神经网络 缺省因子分析法 |
| 收稿时间： | 2013/1/18 0:00:00 |
Soil moisture forecast BP-ANN model and simulation based on sensitivity analysis |
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| HUANG Ling-miao,REN Shu-mei,YANG Pei-ling,SHUI Peng-bo,CAO Jian-wu and ZHOU Rong.Soil moisture forecast BP-ANN model and simulation based on sensitivity analysis[J].Journal of China Agricultural University,2013,18(5):166-172. |
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| Authors: | HUANG Ling-miao REN Shu-mei YANG Pei-ling SHUI Peng-bo CAO Jian-wu and ZHOU Rong |
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| Institution: | College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China;College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China;College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China;Beijing Water Conservancy and Hydropower Center, Beijing 100073, China;Beijing Water Conservancy and Hydropower Center, Beijing 100073, China;Beijing Water Conservancy and Hydropower Center, Beijing 100073, China |
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| Abstract: | Basing on the analysis and merging the major meteorological elements which affect soil moisture,the BP-ANN soil moisture forecast model with six factors was established.The soil moisture sensitivity on six factors was determined by sensitivity analysis.The moisture prediction BP-ANN model was built based on three factors (relative humidity,sunshine hours,average previous ten days of soil moisture).The study showed that the three-factor model root mean square error was 3.55,with the advantage of small data collection and less processing,which could achieve the required accuracy.The test that three-factor model was applied to mountains and the plains of Beijing showed the measured and predicted values reached a very significant level.It presented the strong characteristics of operability. |
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| Keywords: | soil moisture forecast artificial neural network sensitivity analysis method |
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