均方误差
含水量
DNS根区域
环境科学
灌溉
土壤科学
卷积神经网络
相关系数
数学
土壤水分
农学
计算机科学
统计
人工智能
工程类
生物
岩土工程
作者
Jingxin Yu,Xin Zhang,Linlin Xu,Jing Dong,Lili Zhangzhong
标识
DOI:10.1016/j.agwat.2020.106649
摘要
Soil water content in maize root zone is the main basis of irrigation decision-making. Therefore, it is important to predict the soil water content at different depths in maize root zone for rational agricultural irrigation. This study proposed a hybrid convolutional neural network-gated recurrent unit (CNN-GRU) integrated deep learning model that combines a CNN with strong feature expression capacity and a GRU neural network with strong memory capacity. The model was trained and tested with the soil water content and meteorological data from five representative sites in key maize producing areas, Shandong Province, China. We designed the model structure and selected the input variables based on a Pearson correlation analysis and soil water content autocorrelation analysis. The results showed that the hybrid CNN-GRU model performed better than the independent CNN or GRU model with respect to prediction accuracy and convergence rate. The average mean squared error (MSE), mean absolute error and root mean squared error of the hybrid CNN-GRU model on day 3 were 0.91, 0.51 and 0.93, respectively. The prediction accuracy of the model improved with increasing soil depth. Extending the forecast period, the prediction accuracy values of the hybrid CNN-GRU model for the soil water content on days 5, 7 and 10 were comparable, with an average MSE of less than 1.0.
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