A PM2.5 concentration data analysis and prediction model building method
A PM2.5 and predictive model technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of long training time, narrow selection range, huge rule space, etc., and achieve strong parallelism and good generality. The effect of chemicalization ability and simple structure
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Embodiment 1
[0049] A kind of data analysis of PM2.5 concentration of the present invention and method for establishing a prediction model, concrete steps are as follows:
[0050] A method for data analysis and prediction model building of PM2.5 concentration, comprising the steps of:
[0051] Step 1. Decompose the change process of PM2.5 into pollution generation, diffusion, dilution and sedimentation; divide the monitoring area of PM2.5 into multiple cells, and establish a cellular automaton model for each process;
[0052] Step 2, using historical data to train the parameters in each model to obtain a prediction model for PM2.5 data.
[0053]The present invention uses a cellular automata model to simulate and predict PM2.5 concentration changes. On the one hand, the model has a strong evolution ability, which can simulate a variety of complex phenomena and adapt to changes in complex systems. On the other hand, the model has strong parallelism and is easy to implement parallel compu...
Embodiment 2
[0055] A method for data analysis and prediction model establishment of PM2.5 concentration. Aiming at the problem that the change of PM2.5 concentration is difficult to simulate and predict, the change process of PM2.5 is evolved through the cellular automata model, and the data analysis method is used to narrow down the PM2.5 concentration. The PM2.5 rule selection space speeds up the modeling process of the cellular automaton and realizes the purpose of predicting the PM2.5 concentration, such as figure 2 As shown, the specific process is:
[0056] Step 1, data cleaning.
[0057] Use a polynomial model to learn historical data (related to weather), obtain the number n of items of the best fitting curve, and establish a polynomial of degree n. When evaluating the historical data monitored at the i-th moment, the n-degree polynomial is trained on the data of n moments with a short interval from the i-th moment and with high quality. The data at the i-th moment of the curve ...
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