Predicting agricultural drought indicators

This research paper evaluates the effectiveness of various machine learning and deep learning models in predicting agricultural drought indicators, specifically soil moisture and the Palmer drought severity index (PDSI), across Sweden.

• The study compared seven machine learning and deep learning models—random forest (RF), decision tree, multivariate linear regression, support vector regression, autoregressive integrated moving average (AMIRA), artificial neural network, and convolutional neural network—using three data arrangement methods (multi-features, temporal, and spatial).
• For soil moisture prediction, the temporal ARIMA model was most suitable for local scale prediction (MAE of 9.1, R2 of 0.79), while the multi-features RF model was more suitable for national-scale prediction (MAE of 11.95, R2 of 0.59). Overall, models performed better at predicting soil moisture than the PDSI indicator.
• The most important feature for predicting and mapping drought indicators was the previous-year average monthly soil moisture, which was most effective when combined with elevation, precipitation, evapotranspiration, and PDSI.

APA

Stockholm Environment Institute (2023). Predicting agricultural drought indicators. https://www.sei.org/publications/predicting-agricultural-drought-indicators/

Chicago

Stockholm Environment Institute. Predicting agricultural drought indicators. 2023. https://www.sei.org/publications/predicting-agricultural-drought-indicators/.

Wikipedia

{{cite report |author=Stockholm Environment Institute |title=Predicting agricultural drought indicators |date=27 September 2023 |url=https://www.sei.org/publications/predicting-agricultural-drought-indicators/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{stockholmenvironmentinstitute2023predicting, author = {{Stockholm Environment Institute}}, title = {{Predicting agricultural drought indicators}}, institution = {Stockholm Environment Institute}, year = {2023}, month = sep, url = {https://www.sei.org/publications/predicting-agricultural-drought-indicators/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }