Data Repository for Next-Day Mean Wind Speed Forecasting in a High-Variability Coastal Microclimate: An Explainable Gradient-Boosting Benchmark and Predictability Analysis for Şarköy, Türkiye

This repository contains the derived model outputs, performance metrics, and feature importance analyses accompanying the manuscript "Next-Day Mean Wind Speed Forecasting in a High-Variability Coastal Microclimate: An Explainable Gradient-Boosting Benchmark and Predictability Analysis for Şarköy, Türkiye." model_results_clean.csv: Contains the comprehensive performance metrics (RMSE, MAE, R², MaxAE) comparing the predictions of all tested models permutation_importance_rf.csv: Contains the permutation feature importances derived from the final Random Forest model on the training set. xgboost_feature_importance.csv: Contains the gain-based feature importances extracted from the optimized XGBoost model. rf_feature_importances_feature_selection_stage.csv: Contains the initial Random Forest feature importances calculated during the exploratory two-stage feature selection process. vif_results_feature_selection_stage.csv: Contains the Variance Inflation Factor (VIF) scores for the 23 features initially screened by the Random Forest threshold. It details the multicollinearity check where six features exceeded the VIF > 10 threshold, leaving the 17 features utilized in the reduced-set model testing.