Active Sampling of Air Quality Based on Compressed Sensing Adaptive Measurement Matrix
With the continuous acceleration of urbanization,industrial development and population agglomeration make the pro-blem of air quality increasingly serious.Due to the cost of sampling,more and more attention is paid to active sampling of air qua-lity.However,the existing models can either only select the sampling location iteratively or hardly update the sampling algorithm in real time.Motivated by this,an active sampling method of air quality based on compressed sensing adaptive measurement ma-trix is proposed in this paper.The problem of sampling location selection is transformed into the column subset selection problem of the matrix.Firstly,the historical complete data is used for dictionary learning.After column subset selection of the learned dic-tionary,an adaptive measurement matrix that can guide batch sampling is obtained.Finally,the unsampled data is recovered by using the sparse basis matrix constructed by the data characteristics of air quality.This method uses a compressed sensing model to realize sampling and inference integrally,which avoids the shortcoming of using multiple models.In addition,considering the ti-ming variation of air quality,after each active sampling,the dictionary is updated online with the latest data to guide the next sam-pling.Experimental results on two real datasets show that the adaptive measurement matrix obtained after dictionary learning has better recovery performance than all baselines at multiple sampling rates less than 20％.
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