Paper Info
Title
Improving soil moisture prediction using a novel encoder-decoder model with residual learning
Abstract
The skillful prediction of soil moisture can provide much help for many practical applications including ecosystem management and precision agriculture. It presents great challenges because the future variation of soil moisture has much uncertainty. Therefore, a novel encoder-decoder deep learning model with residual learning based on Long Short-Term Memory (EDT-LSTM) is developed in this study as an alternative data-intelligence tool. This model includes two layers: the encoder-decoder LSTM layer and LSTM with a fully connected layer, which is used to enhance the prediction ability by considering the intermediate time-series data between the input time step and the predictive time step. We tested EDT-LSTM for soil moisture prediction at the lead time of 1, 3, 5, 7 and 10 days by using data from FLUXNET sites. The result shows that the improvements brought by EDT-LSTM were about 7.95% (1 day), 10.10% (3 days), 12.68% (5 days), 15.49% (7 days) and 19.71% (10 days) in average according to the R-2 taking LSTM as the baseline. Furthermore, the predictability of soil moisture over various conditions (i.e., different hyper-parameters in EDT-LSTM, different predictive models, different climate regions and different sites) has been widely discussed for the understanding of models' behavior in this paper. The proposed EDT-LSTM offered a new tool to predict soil moisture better. The code of EDT-LSTM is publicly available at https://github.com/ljz1228/CLM-LSTM-soil-moisture-prediction.
Year
DOI
Venue
2022
10.1016/j.compag.2022.106816
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Keywords
DocType
Volume
Soil moisture, Machine learning, LSTM, Encoder-decoder, Residual learning
Journal
195
ISSN
Citations 
PageRank 
0168-1699
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Qingliang Li101.01
Zhongyan Li200.34
Wei Shangguan300.68
Xuezhi Wang400.68
L. Li578.13
Fanhua Yu600.68