Optimized IDF equations under CMIP6 scenarios: comparative performance of GA and HFO methods

Abstract

Global warming and decreasing precipitation trends are causing severe climate change impacts worldwide. This study provides regional Intensity-Duration-Frequency (IDF) equations for the Konya Closed Basin using eleven empirical formulations calibrated against historical and future CMIP6 climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Two evolutionary optimization techniques, Genetic Algorithm (GA) and Honey Formation Optimization (HFO), were used to optimize equation parameters. Model performance was evaluated using statistical metrics such as MAE, MSE, RMSE, NSE, and R-2. Equation 3 under the MGM scenario consistently outperformed other optimization techniques at Ere & gbreve;li and Konya stations. In GA optimization, Ere & gbreve;li station performed best with Eq. 3 (MAE = 15.22, RMSE = 27.23, NSE = 0.993). However, in future scenarios, Eq. 2 showed excellent predictive capability (e.g., MAE = 0.79, RMSE = 1.70, NSE = 0.997 under SSP5-8.5). Similarly, for the Konya station, Eq. 3 (MGM) optimized by GA produced the most accurate results (MAE = 7.14, RMSE = 17.43, NSE = 0.997), while Eqs. 2 and 4 performed well under SSP scenarios. As a result, the GA-optimized IDF equations outperformed those calibrated with HFO, but both methods were effective in capturing non-linear precipitation characteristics. The study emphasizes the efficacy of evolutionary optimization in producing accurate IDF curves for future climate projections. These findings have important implications for hydrological design, flood risk mitigation, and climate adaptation in semi-arid regions. Future research could investigate integration with real-time data sources and application in other climate-sensitive basins.