Improving soil moisture prediction using a novel encoder-decoder model with residual learning

•Skillful prediction of soil moisture (SM) is useful but great challenges exist.•A novel encoder-decoder LSTM model with residual learning (EDR-LSTM) is developed.•EDR-LSTM improved about 20% in SM prediction at the lead time of 3, 5 and 10 days.•Both encoder-decoder and residual learning are useful...

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Bibliographic Details
Published in:	Computers and electronics in agriculture Vol. 195; p. 106816
Main Authors:	Li, Qingliang, Li, Zhongyan, Shangguan, Wei, Wang, Xuezhi, Li, Lu, Yu, Fanhua
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.04.2022 Elsevier BV
Subjects:	agriculture Climate models Coders Deep learning ecosystem management electronics Encoder-decoder Encoders-Decoders Lead time LSTM Machine learning Moisture effects neural networks precision agriculture prediction Prediction models Predictions Residual learning Soil conditions Soil improvement Soil moisture soil water time series analysis uncertainty LSTM Residual learning Encoder-decoder Soil moisture Machine learning
ISSN:	0168-1699, 1872-7107
Online Access:	Get full text
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