| 基于时延神经网络的多深度土壤含水率预测 |
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| 引用本文： | 冀荣华,李鑫,张舒蕾,郑立华.基于时延神经网络的多深度土壤含水率预测[J].农业工程学报,2017,33(Z1):132-136. |
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| 作者姓名： | 冀荣华 李鑫 张舒蕾 郑立华 |
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| 作者单位： | 中国农业大学信息与电气工程学院,北京,100083 |
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| 基金项目： | 国家自然基金项目（31471409、31101609） |
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| 摘 要： | 为精准预测大田土壤含水率,并掌握不同深度土壤含水率分布规律,针对大田土壤含水率时序数据的非线性特点及相邻深度土壤含水率间具有较强关联关系,该文建立3层时延神经网络大田多深度土壤含水率预测模型,用以实现对6个不同深度(10、20、30、40、50和70 cm)土壤含水率预测。利用试验法确定预测模型的隐含层节点个数、训练学习算法和训练集样本量。试验结果表明:隐含层使用10个节点,采用L-M(Levenberg-Marquardt)训练算法,采用45%样本集数据作为训练样本,55%作为测试样本集,对所建预测模型进行预测,10和20 cm的预测相对误差小于7%,而30、40、50和70 cm的预测相对误差小于4.5%。因此利用基于时延神经网络的多深度土壤含水率预测模型,可为掌握土壤含水率分布动态变化规律提出一种解决方案。
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| 关 键 词： | 土壤含水率 预测 测量 多深度 时延神经网络 训练 验证 |
| 收稿时间： | 2016/12/12 0:00:00 |
| 修稿时间： | 2016/12/29 0:00:00 |
Prediction of soil moisture in multiple depth based on time delay neural network |
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| Ji Ronghu,Li Xin,Zhang Shulei and Zheng Lihua.Prediction of soil moisture in multiple depth based on time delay neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(Z1):132-136. |
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| Authors: | Ji Ronghu Li Xin Zhang Shulei and Zheng Lihua |
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| Institution: | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China,College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China and College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
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| Abstract: | Abstract: It is very important to improve water use efficiency and achieve precision irrigation that soil moisture content is predicted accurately. The spatial and temporal variability of soil moisture content is complex because soil moisture content can be affected by various factors, such as soil properties, plant, and environment. The time-series field soil moisture data were nonlinear. There was strong relationship of soil moisture content between the adjacent depths. The work presented in this paper aimed to contribute to predicting soil moisture content in different depths by proposing a time-delay neural network (TDNN). TDNN is an artificial neural network model in which all the neuron-like units (nodes) are fully connected by directed connections. Each unit has a time-varying real-valued activation and each connection has a modifiable real-valued weight. It has 3 layers: input layer, hidden layer and output layer. In this paper, a prediction model based on the TDNN was presented to predict soil moisture content in 6 field depths (10, 20, 30, 40, 50 and 70 cm). The framework of the prediction model based on the TDNN included input layer with 6 units, hidden layer with 10 units and output layer with 6 units. Three training algorithms, which were Levenberg-Marquardt (L-M) method, conjugate gradient method and momentum increase method, were tested. The simulation results show that the L-M method was the best, followed by the conjugate gradient method, and the momentum increase method was the worst. The original experimental data were acquired from maize field in Shangzhuang experiment station, China Agricultural University in Beijing. The sample data set of soil moisture content prediction model based on the TDNN was generated from the original data set, which was calibrated by drying method and preprocessing algorithm. The prediction accuracy of the soil moisture content prediction model was influenced greatly by the training sample''s size. The experiment results showed that the best training accuracy could be gotten when the number of training samples was more than 40% of that of all samples. The prediction model would over fit when the number of training samples was more than 50%. In this paper, every 20 sample data were divided into a set and 9 data were set as training data and other 11 data as test data. The sample data set of soil moisture content prediction model was divided into training set and test set. The results showed that the relative error of soil moisture content prediction for 10 and 20 cm was less than 7%. As for 30, 40, 50 and 70 cm, the relative error was less than 4.5%. The prediction model based on the TDNN presented in this paper can be used to predict the soil moisture content in different depth and can be treated as a solution to grasp the distribution of soil moisture content |
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| Keywords: | soil moisture forecasting measurements multiple depth time delay artificial neural network training verification |
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