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The economic model derived from the supply and demand of crude oil prices is a significant component that measures economic development and sustainability. Therefore, it is essential to mitigate crude oil price volatility risks by establishing models that will effectively predict prices. A promising approach is the application of long short-term memory artificial neural networks for time-series forecasting. However, their ability to tackle complex time series is limited. Therefore, a decomposition-forecasting approach is taken. Furthermore, machine learning model accuracy is highly dependent on hyper-parameter settings. Therefore, in this paper, a modified version of the salp swarm algorithm is tasked with determining satisfying parameters of the long short-term memory model to improve the performance and accuracy of the prediction algorithm. The proposed approach is validated on real-world West Texas Intermediate (WTI) crude oil price data throughout two types of experiments, one with the original time series and one with the decomposed series after applying variation mode decomposition. In both cases, models were adjusted to conduct one, three, and five-steps ahead predictions. According to the findings of comparative analysis with contemporary metaheuristics, it was concluded that the proposed hybrid approach is promising for crude oil price forecasting, outscoring all competitors.

In this paper, we explore the computational capabilities of advanced modeling tools to reveal the factors that shape the observed benzene levels and behavior under different environmental conditions. The research was based on two-year hourly data concentrations of inorganic gaseous pollutants, particulate matter, benzene, toluene, m, p-xylenes, total nonmethane hydrocarbons, and meteorological parameters obtained from the Global Data Assimilation System. In order to determine the model that will be capable of achieving a superior level of performance, eight metaheuristics algorithms were tested for eXtreme Gradient Boosting optimization, while the relative SHapley Additive exPlanations values were used to estimate the relative importance of each pollutant level and meteorological parameter for the prediction of benzene concentrations. According to the results, benzene levels are mostly shaped by toluene and the finest aerosol fraction concentrations, in the environment governed by temperature, volumetric soil moisture content, and momentum flux direction, as well as by levels of total nonmethane hydrocarbons and total nitrogen oxide. The types of conditions which provided the environment for the impact of toluene, the finest aerosol, and temperature on benzene dynamics are distinguished and described.

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