Determination of the performance of training algorithms and activation functions in meteorological drought index prediction with nonlinear autoregressive neural network

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Analysis of long-term meteorological data is critical for monitoring climate trends and understanding the drought situation in a given region. In this study, monthly average precipitation data from the Niğde meteorological station in Turkey covering the period 1950–2020 were used. Within the scope of the study, seven different drought index methods were used for drought analysis, and the number and percentages of drought conditions were calculated according to these indices. For example, according to the Standardized Precipitation Index (SPI) method, the proportion of dry periods was determined as 16.2% and the proportion of humid periods as 83.8%. The Mann-Kendall trend analysis performed to determine the drought trends of the region revealed an increasing trend towards humidity in all indices (e.g., z = 1.299, p = 0.194 for SPI). In the study, 60-month drought forecasts covering the years 2020–2025 were realized using the Nonlinear Autoregressive Neural Network (NARNN) model, and the results were compared with the Autoregressive (AR) model. In the prediction performance analysis, the NARNN model showed superior prediction performance for all indices with lower RMSE values (e.g., NARNN RMSE = 0.977 for SPI; AR RMSE = 1.704). The prediction performances of different training algorithms and activation functions used in the NARNN model were analyzed. The best performance was obtained with the trainbr training algorithm and sigmoid activation function (e.g., RMSE = 0.997 for SPI). Based on these best parameters, more than 70% of the drought conditions during the 2020–2025 period were found to be normal or humid according to NARNN predictions. This study demonstrates the superiority of the NARNN model in nonlinear time series analyses and that it is a reliable tool, especially for future drought forecasts. In addition, comprehensive analyses with different index methods have significantly contributed to understanding the long-term drought trends in the Niğde region.