Kadhum, Z., Alhameedawi, A., Hamoodi, M. (2025). Prediction of reconnaissance drought index and standardized precipitation index for drought analysis. , 43(11), 905-919. doi: 10.30684/etj.2025.159196.1943
Zahraa M. Kadhum; Amjed N. Mohsin Alhameedawi; Mustafa N. Hamoodi. "Prediction of reconnaissance drought index and standardized precipitation index for drought analysis". , 43, 11, 2025, 905-919. doi: 10.30684/etj.2025.159196.1943
Kadhum, Z., Alhameedawi, A., Hamoodi, M. (2025). 'Prediction of reconnaissance drought index and standardized precipitation index for drought analysis', , 43(11), pp. 905-919. doi: 10.30684/etj.2025.159196.1943
Kadhum, Z., Alhameedawi, A., Hamoodi, M. Prediction of reconnaissance drought index and standardized precipitation index for drought analysis. , 2025; 43(11): 905-919. doi: 10.30684/etj.2025.159196.1943

Prediction of reconnaissance drought index and standardized precipitation index for drought analysis

Engineering and Technology Journal
Volume 43, Issue 11, November 2025, Pages 905-919    PDF (1.33 M)
Document Type: Research Paper
DOI: 10.30684/etj.2025.159196.1943
Authors
Zahraa M. Kadhum* 1; Amjed N. Mohsin Alhameedawi2; Mustafa N. Hamoodi2
1Technical Institute of Babylon, Al-Furati Al-Awsat Technical University, Babylon, Iraq. Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
2Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
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.

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
Keywords
ARIMA model; Drin C software; Standard precipitation index; Random forest
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