Daily soil moisture prediction during winter wheat growth season using an SCSSA-CNN-BiLSTM model
CUI Song
WU Jin
ZHANG Naifeng
LIU Meng
HU Yongsheng
HE Yanan
GU Yue
LONG Xinya
WANG Zhenlong
Abstract：[Objective]Accurate prediction of field soil moisture is crucial for managing agricultural production and water-saving irrigation.This paper proposes a new method to predict soil moisture changes.[Method]The hybrid deep learning model,SCSSA-CNN-BiLSTM,was integrated with Sine Cosine Cauchy Sparrow Search Algorithm(SCSSA)for hyperparameter optimization.It was then combined with Convolutional Neural Networks(CNN)for spatial feature extraction and Bidirectional Long Short-Term Memory(BiLSTM)networks for temporal sequence learning.The model was trained using meteorological data and soil moisture measured at three depths-10,30 and 50 cm-at the Wudaogou Experimental Station between October 2022 and June 2023.It was then used to predict soil moisture in the 0-20 cm root zone during the winter wheat growing season.[Result]① The optimized model accurately captured the spatiotemporal variation in soil moisture,with the SCSSA enhancement reducing RMSE by 44.5％from 1.394 to 0.774.② The proposed model was superior to other methods;its statistical metrics are R2=0.960,RMSE=0.774,MSE=0.599,MAE=0.528 and MAPE=1.84％.The predicted results agreed well with observed data.③ Comparative analysis showed that SCSSA optimization significantly outperformed GA,PSO and SSA in hyperparameter tuning.[Conclusion]The SCSSA-CNN-BiLSTM model is accurate for predicting soil moisture in the 0-20 cm root zone of winter wheat.It can be used for real-time irrigation management.
Keywords：neural networksparrow optimization algorithmsoil moisture predictionwinter wheat
Publication Date：2025-08-30
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：8( 1-8 )