OptiX-AI-RF: An Optimized AI-Enhanced Explainable Random Forest Model for Fault Detection in Streetlight Systems

Streetlights are crucial for public safety, especially for those traveling at night. Unfortunately, many areas encounter non-functioning streetlights due to various issues, which can lead to significant safety hazards. To address this problem, an optimized artificial intelligence (AI)-enhanced explainable Random Forest model, referred to as OptiX-AI-RF, has been developed for fault detection in streetlight systems. Initially, various machine learning (ML) models are trained using publicly available street light fault prediction dataset. Since the random forest (RF) model outperformed the others, it is utilized for optimization. Key RF model hyperparameters are adjusted using Bayesian optimization to improve the detection capabilities of the model. The experimental results demonstrated that the OptiX-AI-RF model effectively identified faults in streetlight systems, achieving an accuracy of 82.88%, a precision of 86.30%, a recall of 82.88%, and an F1-score of 78.98%. The predictions of the suggested model are further analyzed using explainable artificial intelligence (XAI) methods. OptiX-AI-RF enables authorities to evaluate the predictions of the model and gain insights into the various factors contributing to streetlight failures by utilizing XAI techniques such as permutation importance, partial dependence plots, and LIME (Local Interpretable Model-agnostic Explanations). This dual capability of accurate fault detection and explainability makes OptiX-AI-RF a valuable tool for maintaining streetlight infrastructure, ultimately aiding in the implementation of more effective maintenance strategies and enhancing public safety in urban areas.