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| 基于自由搜索人工神经网络的坡地入渗量预测 | |
| 引用本文： | 李新虎,张展羽,杨 洁,张国华,王 斌,王 超.基于自由搜索人工神经网络的坡地入渗量预测[J].农业工程学报,2009,25(12):193-197. |
| 作者姓名： | 李新虎 张展羽 杨 洁 张国华 王 斌 王 超 |
| 作者单位： | 1. 河海大学水利水电学院,南京,210098 2. 江西水土保持科学研究所,南昌,330000 3. 中国灌溉排水发展中心,北京,100054 4. 东北农业大学水利与建筑学院,哈尔滨,150030 |
| 基金项目： | 江苏省高校研究生创新计划（CX09B_168Z）；水利部公益性行业科研专项经费项目（200801048） |
| 摘 要： | 该文应用基于自由搜索算法的BP（backpropagation）网络模型对自然降雨条件下不同处理措施的红壤坡地入渗规律进行了预测，选择降雨量、最大降雨强度、降雨历时、土壤初始含水率、土壤体积质量、通气孔度和下垫面状况7项指标作为网络输入，土壤入渗量单项指标作为网络输出，结果表明：基于自由搜索算法的BP网络模型可以有效地预测自然降雨条件下不同处理措施坡地入渗规律，预测的平均相对误差为11.08%，经t检验和回归分析表明预测值和实测值相差不大，具有较好的一致性，决定系数为0.9715，并和传统的BP网络进行了比较，结果显示基于自由搜索算法的BP网络预测优于传统的BP网络，模型具有较高的精度和稳定性。 |
| 关 键 词： | 降雨，入渗，反向传播，自由搜索算法，坡地，人工神经网络 |
| 收稿时间： | 3/2/2009 12:00:00 AM |
| 修稿时间： | 9/6/2009 12:00:00 AM |
Prediction of slope infiltration based on artificial neural networks by free search |
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| Li Xinhu,Zhang Zhanyu,Yang Jie,Zhang Guohu,Wang Bin and Wang Chao.Prediction of slope infiltration based on artificial neural networks by free search[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(12):193-197. | |
| Authors: | Li Xinhu Zhang Zhanyu Yang Jie Zhang Guohu Wang Bin and Wang Chao |
| Institution: | 1. College of Water Conservancy and Hydropower Engineering, Hehai University, Nanjing 210098, China,1. College of Water Conservancy and Hydropower Engineering, Hehai University, Nanjing 210098, China,2. Soil and Water Conservation Research Institute of Jiangxi Province, Nanchang 330000, China,3. China Irrigation and Drainage Development Center, Beijing 100054, China,4. College of Water Conservancy and Building Engineering, Northeast Agricultural University, Harbin 150030, China and 1. College of Water Conservancy and Hydropower Engineering, Hehai University, Nanjing 210098, China |
| Abstract: | Attempts of using FSBP were made to predict infiltration of natural rainfall on the slop surface of red soil under different land use patterns. Seven indexes such as precipitation, maximum rainfall intensity, rainfall duration, initial soil water content, soil bulk density, soil porosity and underlaying surface were selected as input variable, and the amount of infiltration as output variable. Results show that the mean relative error of the prediction is 11.08%, and t test and regression analysis indicates that the predicted value differs just slightly from the observed value and their correlation coefficient was 0.9715. The model is quite high in accuracy and stability, and serves as useful tool in further research on prediction of infiltration of nature rainfall on slopes. |
| Keywords: | rain infiltration backpropagation free search slope artificial neural networks |
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