Using Some Mixture Probability Distributions in Predicting the Amounts of Pollution by Gas Emission in Basrah Governorate
DOI:
https://doi.org/10.62933/byj2ts29Keywords:
Probability Distributions , Mixture Distributions , Estimation, PredictingAbstract
Mixture probability distributions are among the topics that have received great attention because of their role in reaching new distributions that have characteristics that are superior to traditional probability distributions, especially since there are data that have compound distributional characteristics when examined, and mixture distributions often contribute to improving the results of estimation and prediction. Therefore, this paper dealt with the problem of increasing gases emitted by factories, especially in oil installations, which is one of the main causes of environmental pollution, which negatively affects the health of citizens and the increase in diseases resulting from air pollution, including cancer. Therefore, the aim of preparing this paper was to predict the amounts of gases emitted by oil installations in Basrah Governorate, as the gas emissions data were modeled using mixed probability distribution models. These probability models were applied based on real data representing the gas emissions emitted by oil companies operating in Basrah Governorate for the period (Jan2010-Nov2020). Three probability distribution mixture were adopted, namely (Gamma-Gamma, Normal-Normal, Gamma-Lognormal). The comparison was made between them using the Kolmogorov-Smirnov goodness of fit test and the Criteria represented by (AIC, BIC, AICC), where the results determined the preference of the distribution model (N-N), after which the predictions of gas emissions associated with oil operations were found to be expected to increase in a short time.
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