An Ensemble 3D Convolutional Neural Network for Spatiotemporal Soil Temperature Forecasting

Fanhua Yu, Huibowen Hao and Qingliang Li
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Fanhua Yu: College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
Huibowen Hao: College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
Qingliang Li: College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China

Sustainability, 2021, vol. 13, issue 16, 1-16

Abstract: Soil temperature (ST) plays an important role in agriculture and other fields, and has a close relationship with plant growth and development. Therefore, accurate ST prediction methods are widely needed. Deep learning (DL) models have been widely applied for ST prediction. However, the traditional DL models may fail to capture the spatiotemporal relationship due to its complex dependency under different related hydrologic variables. Hence, the DL models with Ensemble Empirical Mode Decomposition (EEMD) are proposed in this study. The proposed models can capture more complex spatiotemporal relationship after decomposing the ST into different intrinsic mode functions. Therefore, the performance of models is further improved. The results show that the performance of DL models with EEMD are better than that of corresponding DL models without EEMD. Moreover, EEMD-Conv3d has the best performance among all the experimental models. It has the highest R2 ranging from 0.9826 to 0.9893, the lowest RMSE ranging from 1.3096 to 1.6497 and the lowest MAE ranging from 0.9656 to 1.2056 in predicting ST at the lead time from one to five days. In addition, the lines between predicted ST and observed ST are closer to the ideal line (y = x) than other DL models. The results show that our EEMD-Conv3D can better capture spatiotemporal correlation and is an applicable method for predicting spatiotemporal ST.

Keywords: spatiotemporal soil temperature; ensemble empirical mode decomposition; convolutional neural network; EEMD-Conv3D (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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