Determining the most appropriate drought index using the random forest algorithm with an emphasis on agricultural drought

Abdol Rassoul Zarei (), Mohammad Reza Mahmoudi () and Mohammad Mehdi Moghimi ()
Additional contact information
Abdol Rassoul Zarei: Fasa University
Mohammad Reza Mahmoudi: Fasa University
Mohammad Mehdi Moghimi: Fasa University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 115, issue 1, No 35, 923-946

Abstract: Abstract Since drought can mainly affect agriculture, especially rain-fed agriculture with high dependence on rainwater, and consequently jeopardize food security and social protection, it is absolutely essential to determine an appropriate drought index to assess the agricultural drought more accurately. In this paper, the random forest algorithm (RF) was employed to compare the capabilities of six more commonly used drought indices in agricultural drought assessment, namely standardized precipitation evapotranspiration index (SPEI), modified SPEI (M-SPEI), reconnaissance drought index (RDI), modified RDI (M-RDI), standardized precipitation index (SPI) and modified SPI (M-SPI) based on the correlation between the drought indices (i.e., independent variables) and yield of rain-fed barley (i.e., response variable). In this study, the data series of 10 stations with appropriate climate variety from 1968 to 2017 were used on 1-, 3-, 6-, and 12-month time scales (27 time periods in total). The results indicated that the linear regression between the simulated and predicted yield of rain-fed barley using the AquaCrop model and the RF algorithm (respectively) had no difference with a perfect reliable line (Y = X) in 0.05 or 0.01 significant levels. The R2 between simulated and predicted barley yield (YB) was also significant at 0.01 levels in all stations, indicating the appropriate capability of the RF algorithm in YB prediction. The results also showed that some indices had better outcomes than others in stations; for example, the SPEI index in Arak, Mashhad, Shahre Kord, and Shiraz stations, the M-SPEI index in Babolsar, Gazvin, and Ramsar, the SPEI and M-SPEI indices in Esfahan and Gorgan stations, and the RDI and SPI indices in Rasht stations had the best outcome. Finally, it was recommended to use the SPEI and M-SPEI indices for agricultural drought assessment.

Keywords: RF algorithm; Drought indices; Yield; Rain-fed barley; Agricultural drought (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-022-05579-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:115:y:2023:i:1:d:10.1007_s11069-022-05579-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-022-05579-2

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().