The ACLPSO algorithm, based on dimension specification intervals, adaptively tunes maximum velocity, inertia weight, acceleration coefficient, and learning probability for each dimension. It has demonstrated excellent performance in benchmark function tests involving both single-modal and multi-modal functions. However, to obtain the global optimal or approximate optimal solutions for all executed benchmark functions, it is necessary to manually set appropriate values for the maximum velocity coefficient s and the learning probability coefficient v during the operation of the ACLPSO algorithm. This study introduces an automatic approach to assign values to s and v, relying on function iteration count and test function fitness convergence. This enhancement enables the improved ACLPSO algorithm to directly derive the global optimal or approximate optimal solutions for all benchmark functions, eliminating the need for manual parameter tuning.
KEYWORDS: Data modeling, Air quality, Artificial neural networks, Machine learning, Air contamination, Neural networks, Deep learning, Support vector machines, Statistical analysis, Reflection
Along with the progress of social civilization, human demand for production and life is also increasing. As a factor that affects people’s health all the time, the air environment has caused extensive research by scholars on the prediction of air environment quality. However, due to the different correlations of various factors affecting the prediction, the prediction results are affected. Thus, this study uses statistical methods to analyze the correlation of different factors in the prediction and uses the long-short-term memory network on basis of the attention mechanism to make predictions. Finally, we tested with the air data in Beijing and calculated that the accuracy of the model was 87.7%. The results show that the long-short-term memory network with the attention mechanism can accurately predict PM2.5, helping us to monitor and control air pollution better in cities.
In response to the increasing severity of hazy weather and the difficulty of prediction, an SSA-SVR based PM2.5 content prediction method is proposed. Specifically, the excellent search capability of the Sparrow Search Algorithm (SSA) was utilized to search for the optimal parameter combinations for the Support Vector Regression (SVR) machine. Firstly, the meteorological factors are dimensionalized using factor analysis. Then the prediction effect of PM2.5 in Beijing is experimentally compared with the regression model constructed by other algorithms. The results show that SSA has stable global search performance and can effectively reduce the influence of SVR parameter selection on the generalization ability and regression accuracy of the system. This is useful for monitoring and prevention of haze and so on.
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