ASSESSMENT OF CRITICAL METEOROLOGICAL AND EDAPHIC DATA FOR DROUGHT FORECASTING

Climate changes and ecological sustainability in agriculture and food production in Serbia, the region and Southeastern Europe : proceedings, (pp. 308-314)

AUTHOR(S) / АУТОР(И): Marija Stevanović1, Miloš Milovančević2

1High School of Health and Sanitation of Vocational Studies „Visan“, Belgrade, Serbia; 2Faculty of Mechanical Engineering, University of Niš, Serbia

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DOI: 10.46793/MAK2025.308S

ABSTRACT / САЖЕТАК:

Drought is a complex and expensive natural hazard, and the identification of essential drought components is vital for modelling and predicting droughts, hence facilitating the development of mitigation strategies in both spatial and temporal contexts. This project aims to use an adaptable neural fuzzy inference system (ANFIS) to categorise meteorological and soil data for drought forecasting. Accurate forecasting of droughts is essential for sustainable water management and for preventing significant harm to agricultural productivity and the economy of an area. The gradual emergence of droughts complicates their detection, although it simultaneously provides several chances for forecasting before to, during, and after an occurrence. Specific humidity at 2 meters above the ground has the greatest effect on drought measurement variability. The combination of specific humidity at 2 meters and surface temperature exhibits the lowest training error, hence exerting the greatest effect on the assessment of drought severity. The findings from this research may provide valuable insights for early agricultural drought alerts.

KEYWORDS / КЉУЧНЕ РЕЧИ:

Drought, Soil, Forecasting, ANFIS

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