Novel and hybrid model of pelican optimization algorithm with light gradient boosting model for smart infrastructure: A high-precision approach to occupancy and failure prediction

Prediction of building occupancy is a very important element of structural health monitoring (SHM), smart building systems, and energy management. In this study, a number of visualization tools are used to analyze the performance of models, which would allow engineers and operations personnel of a system to interpret forecasts and review them to make informed decisions based on the data on the structural health of the building. It also introduces a new machine learning (ML) system, coupled with optimization schemes, to improve the efficiency and accuracy of prediction. This paper employs a set of state-of-the-art ML models, such as Light Gradient Boosting Machine (LightGBM), LightGBM Logistic Regression (LR), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GBM), and Random Forest (RF), to come up with a model to predict occupancy. The model is applicable in structural health monitoring, smart building applications, and energy management. The proposed POA-LightGBM approach has a high F1-score of training (0.9373) and test (0.9267), respectively. In addition, the model has enhanced AUC–ROC values of 0.9995 and 0.9990 during training and testing stages, respectively, meaning that the model has a high degree of classification. These findings underline the efficiency, effectiveness, and dependability of the POA-LightGBM approach, solving the practical issues of structural health and failure prediction. By implementing the proposed approach, based on the optimization of performance with the help of ML, to enhance the resiliency of infrastructure and energy efficiency of smart buildings, an efficient and effective solution can be provided. The proposed solution would promote energy through streamlined predictive modeling, smart building efficiency, and resilience of infrastructure.