A new long short-term memory based approach for soil moisture prediction

Abstract

Water scarcity is becoming more severe around the world as a result of suboptimal irrigation practices. Effective irrigation scheduling necessitates an estimation of future soil moisture content. This study presents deep learning models such as CNN-LSTM, a hybrid Deep Learning model that predicts future soil moisture using climate and soil information, including past soil moisture content. The study also investigates the appropriate number of observations and data sampling rate required to predict the next day’s soil moisture value. In terms of MSE, MAE, RMSE, and ${\mathit{R}}^2$ , the hybrid CNN-LSTM model is compared to standalone LSTM and Bi-LSTM models. The LSTM model achieved an MSE of 0.2471, MAE of 0.1978, RMSE of 0.4971, and ${\mathit{R}}^2$ of 0.9714. The LSTM model outperformed the Bi-LSTM model, which had an MSE of 0.3036, MAE of 0.3248, RMSE of 0.5510, and ${\mathit{R}}^2$ of 0.9614. With an MSE of 0.1348, MAE of 0.1868, RMSE of 0.3672, and ${\mathit{R}}^2$ of 0.9838, the hybrid CNN-LSTM model outperformed the LSTM. Our findings suggest that deep learning models, particularly the Convolutional LSTM, hold great potential for predicting soil moisture accurately. The Convolutional LSTM model’s superior performance can be attributed to its ability to capture spatial dependencies in soil moisture data. Furthermore, the results show that for better prediction, sub-hourly data samples from the previous three days should be considered.