Enhancing drought forecasting with CNN-TQWT and metaheuristic hybrids: evidence from Norway

Droughts are among the most damaging natural hazards, exerting severe impacts on ecosystems, economies, and societies. While typically catastrophic, their effects can vary across regions, including polar areas where outcomes may differ unexpectedly. Accurate forecasting is therefore essential for climate adaptation and resource planning. This study analyzes monthly precipitation data from 1970 to 2025 across four Norwegian cities (Bergen, Kristiansand, Oslo, and Tromsø). Standardized Precipitation Index (SPI12) values were calculated and used as inputs to predictive models based on Convolutional Neural Networks (CNN). To enhance performance, the CNN framework was combined with Random Forest (RF) and also optimization techniques, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), as well as signal decomposition methods such as Variational Mode Decomposition (VMD) and the Tunable Q-Factor Wavelet Transform (TQWT). Four input configurations were tested, and results show that CNN-TQWT hybrid models consistently outperformed other approaches across all study sites, highlighting their potential for reliable drought prediction in diverse climatic settings. Specifically, the most effective models were identified as: Bergen: CNNTQWTM03 (R = 0.9732, NSE = 0.9231) Kristiansand: CNNTQWTM03 (R = 0.9879, NSE = 0.9568) Oslo: CNNTQWTM02 (R = 0.9727, NSE = 0.9172) Tromsø: CNNTQWTM01 (R = 0.9777, NSE = 0.9395). The findings highlight the superior accuracy of the TQWT decomposition method across all stations, underscoring its potential to support decision-makers in developing effective agricultural and water management policies. Furthermore, the findings of this study will contribute to the formulation of drought preparedness and development plans. By providing a technical foundation for proactive planning, these results will assist in mitigating the impacts of drought through more resilient management strategies.