Multi-scale SPI drought forecasting and threshold-based early warning using optimized machine learning models: A case study of Sharjah, United Arab Emirates

Study region: Sharjah, United Arab Emirates (UAE), Arabian Peninsula Study focus: Drought forecasting in arid environments is constrained by sparse, intermittent rainfall and abrupt transitions between wet and dry states. This study develops a multi-scale machine learning (ML) framework for one-month-ahead Standardized Precipitation Index (SPI) forecasting in Sharjah, UAE, using monthly precipitation and temperature data from 1995 to 2024. SPI-3, SPI-6, and SPI-12 were forecast under three predictor configurations: antecedent SPI combined with climatic variables, SPI-only, and climate-only. Seven model families were evaluated: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Regression (SVR), Autoregressive Integrated Moving Average with eXogenous inputs (ARIMAX)/Seasonal ARIMA (SARIMA), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM). These models were tested under standard tuning, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hybrid FOX–Tree-Seed Algorithm (HFTA) optimization, with optional ARIMA residual correction. Performance was assessed using continuous metrics, threshold-based drought classification, permutation-based predictor importance, and Taylor diagrams. New hydrological insights for the region: This study provides a systematic separation of drought memory and meteorological forcing as SPI predictors across three accumulation scales in an arid environment. Forecasting skill improved consistently from SPI-3 to SPI-12. The best SPI-3 result used SVR + HFTA + ARIMA under the combined configuration (R² = 0.808, RMSE = 0.295). For SPI-6, the climate-only XGB + PSO model achieved the strongest performance (R² = 0.891, RMSE = 0.266), demonstrating that seasonal drought can be forecast from meteorological predictors alone, without prior SPI records. The highest overall skill was obtained for SPI-12 using SVR + GA + ARIMA under the SPI-only configuration (R² = 0.943, RMSE = 0.222), highlighting the dominant role of drought memory at annual scales. Threshold-based classification showed that SPI-6 < 0 was detected most reliably by the climate-only XGB + GA model, with 98.33% balanced accuracy and zero false alarms. SPI-12 ≤ −1 was best detected by LSTM + PSO, reaching 94.44% balanced accuracy. Predictor importance showed that antecedent SPI governs long-scale forecasts, while precipitation drives climate-only models. The framework supports multi-scale drought early warning and water-resource planning in arid regions, contributing to SDG 2, SDG 6, and SDG 13.