Predicting agricultural drought indicators: ML approaches across wide-ranging climate and land use conditions

Three data arrangement methods (multi-features, temporal, and spatial) were used and compared in combination with seven ML/deep learning (DL) models (random forest (RF), decision tree, multivariate linear regression, support vector regression, autoregressive integrated moving average (AMIRA), artificial neural network, and convolutional neural network). Seven investigated features, obtained from Google Earth Engine, were used in the ML/DL modeling (soil moisture, PDSI, precipitation, evapotranspiration, elevation, slope and soil texture). The temporal ARIMA model (found most suitable for local scale prediction) and the multi-features RF model (more suitable for national-scale prediction) emerged as best performing for soil moisture prediction (with MAE of 9.1 and 11.95, and R² of 0.79 and 0.59, respectively). All models generally performed better in predicting the soil moisture than the PDSI indicator of drought. For drought indicator prediction and mapping, previous-year average monthly soil moisture emerged as the most important feature, combined with the four additional corresponding features of PDSI, precipitation, evapotranspiration and elevation.