A GeoAI Framework for Offshore Wind Farm Site Suitability Prediction Using Ensemble Machine Learning

The integration of artificial intelligence (AI) with geospatial analysis, commonly referred to as GeoAI, provides new opportunities for renewable-energy planning and site-suitability assessment. However, limited attention has been given to integrating Multi-Criteria Decision Analysis (MCDA) with machine learning (ML) for offshore wind farm suitability assessment. This study proposes a GeoAI framework to predict offshore wind farm suitability using ensemble machine-learning techniques. A pooled technology-aware ML model was developed using nine predictors, including a turbine type to account for fixed-bottom and floating technologies. Gradient Boosting, XGBoost, LightGBM, Random Forest, Decision Tree, and Logistic Regression were evaluated, with a majority-class classifier included as a baseline. Model development and hyperparameter optimization were conducted using five-fold stratified group cross-validation based on geographic blocks, with weighted F1-score used as the primary model-selection metric. Gradient Boosting achieved the best model, with a weighted F1-score of 0.8077, followed closely by XGBoost (0.8031), and was therefore selected for final evaluation. SHapley Additive exPlanations (SHAP) analysis further revealed that wind power density, wind speed, and water depth were the most influential factors governing offshore wind farm suitability predictions.