<p>Results of Classifiers.</p>

Conventional techniques included descriptive statistics, correlation analysis, and regression modeling, while AI-based methods incorporated support vector machine (SVM) and artificial neural network (ANN) models. The findings indicate that ambient temperature has a significant negative impact on turbine performance, showing a strong correlation with output power and efficiency (r = −0.959, R² = 92.1%). Humidity and dew point also affected turbine behavior, although their relationships with performance were comparatively less pronounced.

A regression-based predictive model was developed and validated using operational plant data, achieving high prediction accuracy with a minimum error of −0.01088 for Gas Turbine-I. Comparative analysis demonstrated that ANN-based models outperformed conventional techniques in prediction capability. Overall, the study provides a reliable framework for predicting gas turbine performance and improving operational efficiency under varying climatic conditions in Pakistan.</p></div>