<![CDATA[Rainfall is an essential meteorological parameter that affects various sectors of life. Accurately predicting rainfall has become crucial, and artificial intelligence-based models are increasingly popular in this field. Artificial Neural Networks (ANNs) have been widely used due to their ability to identify non-linear patterns in complex data. However, ANN-based predictions have limitations in optimally handling uncertainty or data variability. To address this issue, this study proposes a hybrid model that combines ANNs with fuzzy algorithms. Fuzzy algorithms are capable of managing uncertainty and providing flexible decision-making. This research proposes a hybrid model that integrates Artificial Neural Networks (ANNs) and fuzzy algorithms to predict annual rainfall based on meteorological data from 2019 to 2024. ANNs are used to detect non-linear patterns in temperature, humidity, and atmospheric pressure data, while fuzzy algorithms handle the uncertainty in input data. The model was tested using data from local meteorological stations and evaluated using MAE, RMSE, and the coefficient of determination (R²) metrics. The evaluation results show that the hybrid model achieved the best performance, with an MAE of 3.17 mm, RMSE of 3.4 mm, and R² of 0.98. These findings indicate that the combination of ANN and fuzzy logic significantly improves the accuracy of rainfall prediction compared to individual methods. This model has the potential to be applied in early warning systems and more precise climate management.]]>
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