A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean

• DOI: 10.1016/j.compag.2022.106925
• Publication Date: 2022-04-10T13:37:01Z
• Authors: Safwan Mohammed; Ahmed Elbeltagi; Bashar Bashir; Karam Alsafadi; Firas Alsilibe; Abdullah Alsalman; Mojtaba Zeraatpisheh; Adrienn Széles; Endre Harsányi
• Keywords: 01 natural sciences; 0105 earth and related environmental sciences

Drought is a natural hazard which affects ecosystems in the eastern Mediterranean. However, limited historical data for drought monitoring and forecasting are available Thus, implementing machine learning (ML) algorithms could allow prediction of future events. In this context, main goals research were to capture agricultural hydrological trends by using Standardized Precipitation Index (SPI) assess applicability four ML (bagging (BG), random subspace (RSS), tree (RT), forest (RF)) predicting events Mediterranean based on SPI-3 SPI-12. The results reveal that (SPI-12, −24) was more severe over study area, where most stations showed significant (p < 0.05) negative trend. accuracy varied relation implementation stage. training stage, RT outperformed other (Root mean square error (RMSE) = 0.3, Correlation Coefficient (r) 0.97); performance can be ranked as follows: > RF BG RSS both testing had highest correlation r (observed vs. predicted) (0.58–0.64) lowest RMSE (0.68–0.88). contrast, (0.3–0.41) (0.94–1.10) calculated algorithm. dynamic capturing, with correlation. validation satisfactory (RMSE 0.62–0.83, 0.58–0.79). output will help decision-makers mitigation plans new algorithms.