Soil Moisture Prediction Based on LSTM with a Sliding Window Strategy

Abstract

: Soil moisture is a key indicator of land surface hydrological processes and plays an essential role in agricultural irrigation management and ecological monitoring. However, traditional monitoring approaches mainly rely on fixed sensors and lack the capability to predict future soil moisture dynamics. To address this issue, this study proposes a soil moisture time series prediction method that integrates a sliding window strategy with a Long Short-Term Memory (LSTM) network. Daily soil moisture data from a single observation station are preprocessed through data cleaning and min – max normalization, and then converted into supervised learning samples using a 14-day sliding window. The LSTM model is constructed to capture nonlinear temporal dependencies in the time series, and its performance is evaluated using RMSE and MAE. Experimental results show that the proposed model achieves an RMSE of 0.0476 and an MAE of 0.0369 on the validation set, demonstrating satisfactory prediction accuracy and stability.