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Keywords

ARIMA model, Drin C software, Standard precipitation index, Random Forest

Document Type

Research Paper

Abstract

Drought is a natural disaster characterized by its intensity, duration, and spatial extent. This research investigates meteorological drought in Babylon Province, Iraq, highlighting its significance in the local context, particularly given the region's vulnerability to climatic changes. Employing the Drought Index Calculator (Drin C), we evaluate drought indices, namely the Reconnaissance Drought Index (RDI) and the Standard Precipitation Index (SPI), from 1991 to 2021. This study underscores the imperative of assessing the accuracy of commonly used drought monitoring techniques due to their inherent uncertainties. This work highlights the importance of integrating advanced modelling tools, such as the integration of advanced modeling tools, such as Random Forest and ARIMA, alongside comprehensive meteorological assessments to enhance drought preparedness and response strategies. The project aims to deepen the understanding of drought conditions in Babylon Province by employing sophisticated analytical models and evaluating their efficacy in forecasting drought indicators, while providing data-driven recommendations for efficient water resource management. The model utilizes monthly precipitation data from six sites to calculate SPI and RDI values, with R-squared values of 0.95 for SPI12 and 0.818 for RDI12, clearly attributing these values to the ARIMA model. The ARIMA model enhances predictive accuracy with increasing time scales, while the Random Forest model offers complementary insights into drought patterns. A hybrid model for drought forecasting is created, combining linear and nonlinear approaches, which progressively enhances precision and provides significant insights into local climate variability, thereby facilitating effective decision-making and resource management.

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Highlights

Drin C was used to analyze drought using the Reconnaissance Drought Index (RDI) and Standard Precipitation Index (SPI) A hybrid model was proposed to improve drought prediction accuracy over traditional linear and nonlinear methods Drought duration, intensity, and accumulative deficit were evaluated for each station ARIMA improved SPI and RDI prediction, reaching R² values of 0.95 for SPI12 and 0.818 for RDI12

DOI

10.30684/etj.2025.159196.1943

First Page

905

Last Page

919

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