Mohammed S., Elbeltagi A., Bashir B., Alsafadi K., Alsilibe F., Alsalman A., Zeraatpisheh M., Széles A., Harsányi E. (2022). A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean. Computers and Electronics in Agriculture, 01/06/2022, vol. 197, p. 1-19.
https://doi.org/10.1016/j.compag.2022.106925
https://doi.org/10.1016/j.compag.2022.106925
Titre : | A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean (2022) |
Auteurs : | S. Mohammed ; A. Elbeltagi ; B. Bashir ; K. Alsafadi ; F. Alsilibe ; A. Alsalman ; M. Zeraatpisheh ; A. Széles ; E. Harsányi |
Type de document : | Article |
Dans : | Computers and Electronics in Agriculture (vol. 197, June 2022) |
Article en page(s) : | p. 1-19 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 07 - ENVIRONNEMENT ; 7.5 - Dégradation : Impact, DésertificationThésaurus IAMM SECHERESSE ; PREVISION ; AGRICULTURE ; ANALYSE DE DONNEES ; TRAITEMENT DES DONNEES ; APPRENTISSAGE ; REGION MEDITERRANEENNE ; SYRIE |
Mots-clés: | APPRENTISSAGE AUTOMATIQUE |
Résumé : | Drought is a natural hazard which affects ecosystems in the eastern Mediterranean. However, limited historical data for drought monitoring and forecasting are available in the eastern Mediterranean. Thus, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this context, the main goals of this research were to capture agricultural and hydrological drought trends by using the Standardized Precipitation Index (SPI) and to assess the applicability of four ML algorithms (bagging (BG), random subspace (RSS), random tree (RT), and random forest (RF)) in predicting drought events in the eastern Mediterranean based on SPI-3 and SPI-12. The results reveal that hydrological drought (SPI-12, −24) was more severe over the study area, where most stations showed a significant (p RF > BG > RSS for both SPI-3 and SPI-12. In the testing stage, both the BG and RF algorithms had the highest correlation r (observed vs. predicted) (0.580.64) and lowest RMSE (0.680.88). In contrast, the lowest correlation r (observed vs. predicted) (0.30.41) and highest RMSE (0.941.10) was calculated for the RT algorithm. However, BG was more dynamic in drought capturing, with the lowest RMSE and highest correlation. In the validation stage, the BG performance was satisfactory (RMSE = 0.620.83, r = 0.580.79). The output of this research will help decision-makers with drought mitigation plans by using the new four machine learning algorithms. |
Cote : | En ligne |
URL / DOI : | https://doi.org/10.1016/j.compag.2022.106925 |