| 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。 |
| 关键词: 土壤墒情 预测预报 人工神经网络 缺省因子分析法 |
| DOI：10.11841/j.issn.1007-4333.2013.05.25 |
| 投稿时间：2013-01-18 |
| 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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| Soil moisture forecast BP-ANN model and simulation based on sensitivity analysis |
|
HUANG Ling-miao1, REN Shu-mei1, YANG Pei-ling1, SHUI Peng-bo2, CAO Jian-wu2, ZHOU Rong2
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| (1.College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China;2.Beijing Water Conservancy and Hydropower Center, Beijing 100073, China) |
| 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. |
| Key words: soil moisture forecast artificial neural network sensitivity analysis method |
