Explosion prediction of oil gas using SVM and Logistic Regression

TL;DR

This paper compares SVM and Logistic Regression for predicting oil gas explosions, highlighting LR's explicit probability formula and SVM's higher accuracy, with analysis of error distribution based on penalty parameters.

Contribution

It introduces a combined approach using SVM and LR for oil gas explosion prediction, emphasizing practical error management and model performance.

Findings

SVM achieves higher prediction accuracy.

LR provides explicit explosion probability formulas.

Penalty parameters influence error distribution.

Abstract

The prevention of dangerous chemical accidents is a primary problem of industrial manufacturing. In the accidents of dangerous chemicals, the oil gas explosion plays an important role. The essential task of the explosion prevention is to estimate the better explosion limit of a given oil gas. In this paper, Support Vector Machines (SVM) and Logistic Regression (LR) are used to predict the explosion of oil gas. LR can get the explicit probability formula of explosion, and the explosive range of the concentrations of oil gas according to the concentration of oxygen. Meanwhile, SVM gives higher accuracy of prediction. Furthermore, considering the practical requirements, the effects of penalty parameter on the distribution of two types of errors are discussed.