Employing Artificial Intelligence Algorithms to Estimate the Hazard Function of the Inverse Gompertz Distribution with a Practical Application

Qasim Muhammad Jawad Noah Sulaiman

doi:10.47134/ppm.v2i4.2053

Authors

Qasim Muhammad Jawad Noah Sulaiman Mohaghegh Ardabili University

DOI:

https://doi.org/10.47134/ppm.v2i4.2053

Keywords:

Inverse Gompertz Distribution, Properties, Maximum Likelihood Method, Genetic Algorithm, Risk Function

Abstract

Environmental pollution is one of the most important and serious problems facing humanity today, due to its direct impact on the health of humans and other living organisms. In recent years, an increase in environmental pollution rates has been observed, significantly impacting human health and leading to the emergence of numerous diseases, such as cancer, pneumonia, poisoning, birth defects, and others. Given the importance and seriousness of the issue and its direct impact on human life, this research was conducted to determine the percentage of pollution caused by two of the most important factors in air pollution, CO2. This research was conducted based on the explanatory variables: average temperature, average dew point, average humidity, average wind speed, and the average amount of crude oil used in the refining process. In this research, the risk function of the inverse Gompertz model was estimated using artificial intelligence algorithms, namely the genetic algorithm. These methods were applied to air pollution data obtained from the Central Refineries Company in Baghdad (Dora Refinery), which represents daily measurements of environmental pollution compounds based on time for the period from 2019 to 2025.

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