Due to unpredictability of climatic conditions across the world, early fire forecasting has become more challenging and critical for many oil and gas sectors. It is extremely hard for anyone to predict fires with any degree of certainty, especially in the gas or oil sectors. Until now, the models in use have not been adequate. However, this is critical in order to maintain workers and property safe. As a result, this research work investigates the different approaches available for fire hazard assessment and prediction in order to deal with fire dangers. Also, this research work presents the statistical machine learning methods to detect fire accidents in petroleum industries based on risk index models and risk assessment parameters by performing a statistical process. Moreover, this research work develops a statistical machine learning method to enhance the accuracy in predicting the fire occurrence. Finally, the proposed algorithm is measured by utilizing the performance metrics such as accuracy, proposed risk index, and sensitivity.

Machine learning SVM