Daily soil moisture prediction during winter wheat growth season using an SCSSA-CNN-BiLSTM model

• Published in: Guanʻgai paishui xuebao Vol. 44; no. 8; pp. 1 - 8
• Main Authors: CUI Song, WU Jin, ZHANG Naifeng, LIU Meng, HU Yongsheng, HE Yanan, GU Yue, LONG Xinya, WANG Zhenlong
• Format: Journal Article
• Language: Chinese
• Published: Science Press 01.08.2025
• Subjects: neural network; sparrow optimization algorithm; soil moisture prediction; winter wheat
• ISSN: 1672-3317
• DOI: 10.13522/j.cnki.ggps.2025183

【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