Boruta-AttLSTM: A Novel Deep Learning Architecture for Soil Moisture Prediction

Water scarcity is worsening due to poor water management in irrigated areas, which directly impacts global food safety. Furthermore, effective irrigation scheduling necessitates predicting future soil moisture content, representing soil water availability. For this purpose, the current study proposes a novel data-driven architecture based on deep learning algorithms to predict soil volumetric water content. The proposed architecture combines the time-processing ability of Long Short-Term Memory with the attention mechanism’s ability to process long sequences. The suggested architecture’s resulting model is compared to a 2-layer LSTM in terms of MSE, MAE, RMSE, and R2 score. This study also examines the relationships between various climate and soil parameters and targets soil moisture. The relevance of input features is considered by the feature selection strategy using their computed shapley values. The findings of this study suggest that attention mechanisms can increase the performance and generalizability of regular LSTMs