Hybrid CNN–ML System for Wind Speed Level Identification in Complex Terrain: A Case Study from Maden, Turkey

This study proposes a hybrid deep and traditional learning framework for wind speed level classification in the complex terrain of the Maden region, Turkey. A one-dimensional convolutional neural network (1D-CNN) was employed for automatic feature extraction from a 30-day meteorological window, followed by classification using multiple machine learning algorithms. Among them, K-Nearest Neighbor (KNN) achieved the highest accuracy (98.75%) when applied to features extracted from the global average pooling (GAP) layer. The hybrid CNN–KNN model significantly outperformed standalone CNN and KNN baselines. The study highlights the effectiveness of combining deep feature representations with interpretable classifiers in data-scarce, topographically challenging regions, offering a transparent and high-performance alternative for wind energy assessment.