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 (Guan'gai paishui xuebao)
【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.