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干旱区科学  2016, Vol. 8 Issue (5): 734-748    DOI: 10.1007/s40333-016-0049-0
  学术论文 本期目录 | 过刊浏览 | 高级检索 |
Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model
SONG Xiaodong, ZHANG Ganlin*, LIU Feng, LI Decheng, ZHAO Yuguo, YANG Jinling
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model
SONG Xiaodong, ZHANG Ganlin*, LIU Feng, LI Decheng, ZHAO Yuguo, YANG Jinling
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
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摘要 Soil moisture content (SMC) is a key hydrological parameter in agriculture, meteorology and climate change, and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling. However, the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC. At present, deep learning wins numerous contests in machine learning and hence deep belief network (DBN), a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes. In this study, we used a novel macroscopic cellular automata (MCA) model by combining DBN to predict the SMC over an irrigated corn field (an area of 22 km2) in the Zhangye oasis, Northwest China. Static and dynamic environmental variables were prepared with regard to the complex hydrological processes. The widely used neural network, multi-layer perceptron (MLP), was utilized for comparison to DBN. The hybrid models (MLP-MCA and DBN-MCA) were calibrated and validated on SMC data within four months, i.e. June to September 2012, which were automatically observed by a wireless sensor network (WSN). Compared with MLP-MCA, the DBN-MCA model led to a decrease in root mean squared error (RMSE) by 18%. Thus, the differences of prediction errors increased due to the propagating errors of variables, difficulties of knowing soil properties and recording irrigation amount in practice. The sequential Gaussian simulation (sGs) was performed to assess the uncertainty of soil moisture estimations. Calculated with a threshold of SMC for each grid cell, the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods. The current results showed that the DBN-MCA model performs better than the MLP-MCA model, and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms. Moreover, because modeling soil moisture by using environmental variables is gaining increasing popularity, DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.
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SONG Xiaodong
ZHANG Ganlin
LIU Feng
LI Decheng
ZHAO Yuguo
YANG Jinling
关键词:  wind velocity  monotonic trend  step trend  aeolian desertification  northern China    
Abstract: Soil moisture content (SMC) is a key hydrological parameter in agriculture, meteorology and climate change, and understanding of spatio-temporal distributions of SMC in farmlands is important to address the precise irrigation scheduling. However, the hybrid interaction of static and dynamic environmental parameters makes it particularly difficult to accurately and reliably model the distribution of SMC. At present, deep learning wins numerous contests in machine learning and hence deep belief network (DBN), a breakthrough in deep learning is trained to extract the transition functions for the simulation of the cell state changes. In this study, we used a novel macroscopic cellular automata (MCA) model by combining DBN to predict the SMC over an irrigated corn field (an area of 22 km2) in the Zhangye oasis, Northwest China. Static and dynamic environmental variables were prepared with regard to the complex hydrological processes. The widely used neural network, multi-layer perceptron (MLP), was utilized for comparison to DBN. The hybrid models (MLP-MCA and DBN-MCA) were calibrated and validated on SMC data within four months, i.e. June to September 2012, which were automatically observed by a wireless sensor network (WSN). Compared with MLP-MCA, the DBN-MCA model led to a decrease in root mean squared error (RMSE) by 18%. Thus, the differences of prediction errors increased due to the propagating errors of variables, difficulties of knowing soil properties and recording irrigation amount in practice. The sequential Gaussian simulation (sGs) was performed to assess the uncertainty of soil moisture estimations. Calculated with a threshold of SMC for each grid cell, the local uncertainty of simulated results in the post processing suggested that the probability of SMC less than 25% will be difference in different areas at different time periods. The current results showed that the DBN-MCA model performs better than the MLP-MCA model, and the DBN-MCA model provides a powerful tool for predicting SMC in highly non-linear forms. Moreover, because modeling soil moisture by using environmental variables is gaining increasing popularity, DBN techniques could contribute a lot to enhancing the calibration of MCA-based SMC estimations and hence provide an alternative approach for SMC monitoring in irrigation systems on the basis of canals.
Key words:  wind velocity    monotonic trend    step trend    aeolian desertification    northern China
收稿日期:  2015-10-13      修回日期:  2016-01-14           出版日期:  2016-06-15      发布日期:  2016-03-03      期的出版日期:  2016-06-15
基金资助: 

This study was supported by the National Natural Science Foundation of China (41130530, 91325301, 41401237, 41571212, 41371224), the Jiangsu Province Science Foundation for Youths (BK20141053) and the Field Frontier Program of the Institute of Soil Science, Chinese Academy of Sciences (ISSASIP1624).

通讯作者:  ZHANG Ganlin    E-mail:  glzhang@issas.ac.cn
引用本文:    
SONG Xiaodong, ZHANG Ganlin, LIU Feng, LI Decheng, ZHAO Yuguo, YANG Jinling. Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model[J]. 干旱区科学, 2016, 8(5): 734-748.
SONG Xiaodong, ZHANG Ganlin, LIU Feng, LI Decheng, ZHAO Yuguo, YANG Jinling. Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land, 2016, 8(5): 734-748.
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http://jal.xjegi.com/CN/10.1007/s40333-016-0049-0  或          http://jal.xjegi.com/CN/Y2016/V8/I5/734
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