ArticleScientific reports2026
Medium -term monitoring and machine learning-based forecasting of drought dynamics in Iran.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A global dataset of spatiotemporal drought events from reanalysis and hydrological model data for 1980-2024.Scientific data · 2026Article
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Abstract
Facing Iran’s acute water scarcity and the compounding effects of drought, this research undertakes a comprehensive assessment of past drought conditions, decadal forecast developments, and analyzes the progression of trends over time. The study draws on 58 years (1967–2024) of climate observations from 34 meteorological stations, applying the Reconnaissance Drought Index (RDI) at 1, 3, and 12-month time scales. To develop a robust forecasting model, we first conducted a benchmark analysis to select an optimal optimization algorithm for a Support Vector Machine (SVM). The Pelican Optimization Algorithm (POA) demonstrated superior performance compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), leading to the selection of the SVM-POA framework. This validated model and a Bidirectional Long Short-Term Memory (BiD-LSTM) network were then tested for forecasting performance. A comprehensive evaluation using multiple performance metrics, including R², Mean Absolute Error (MAE), Normalized Root Mean Squared Error (NRMSE), and the Kappa index, identified SVM-POA as the most accurate model, which was subsequently employed to decadal forecast drought conditions for 2025–2036. Historically, the Normal (N) RDI class was the most frequent. Forecasts indicate that N and Moderately Dry (M-D) classes will be the most frequent in the coming decade, with extreme wet conditions expected to disappear. Importantly, trend diagnostics detected a prevailing shift toward drier conditions in all stations, whether statistically significant or not. The share of stations with significant drying is projected to rise sharply, from 26.47 to 88.23% for the 1-month scale and from 2.9 to 35.29% for the 3-month scale, between the historical and extended periods. For the 12-month scale, the proportion of stations with significant drying trends remains unchanged. These findings underscore the likelihood of increasingly severe droughts in Iran, demanding urgent, forward-looking strategies for sustainable water resource management.
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