Smart sustainable architecture: leveraging machine learning for adaptive digital design and resource optimization

Abstract

This study investigates the application of the Fruit Fly Optimization Algorithm (FOA) in enhancing the predictive performance of the Light Gradient Boosting Machine (LightGBM) model for smart sustainable architecture. Key features, including Energy Consumption, Water Usage, Material Cost, CO2 Emissions, and Design Flexibility, were selected using FOA to optimize the model’s predictive accuracy. The FOA-based feature selection significantly improved across all performance metrics: Accuracy increased from 0.85 to 0.88, Precision from 0.80 to 0.84, Recall from 0.78 to 0.82, and the F1-Score from 0.79 to 0.83. Moreover, the Root Mean Square Error (RMSE) decreased from 0.25 to 0.22, while the Area Under the Curve (AUC) improved from 0.76 to 0.8625. These findings underscore the effectiveness of FOA in refining feature selection, thereby enhancing the efficiency and reliability of predictive models in sustainable architectural design. The study highlights the potential of advanced optimization algorithms in developing more adaptive, resource-efficient, and sustainable architectural solutions.