Multi-source man-made thermal space-time quantization method based on machine learning

Abstract

The man-made heat has obvious influence on urban climate and air quality, but an accurate and efficient estimation method for the multi-source man-made heat is lacked at present. The invention improves the process of man-made thermal modeling, and provides a multi-source man-made thermal time-space quantization method based on machine learning. The method comprises the following steps: step 1) calculating a county-level annual average artificial heat flux (AHF) on the basis of energy consumption and social economic data; 2) carrying out time dimension downscaling processing on artificial heat from different sources by using the substitution data to obtain county-level monthly average AHF; 3) calculating a monthly county-level average value of the man-made thermal correlation multi-source data as an explanatory variable, and forming a training sample with the corresponding AHF; 4) training the model based on two machine learning algorithms of a gradient lifting regression tree and Cubist, performing error analysis, and selecting an optimal algorithm for modeling for different heat sources; and 5) inputting specific raster data into the optimal model to calculate the multi-source artificial heat flux in a specific area at a specific time.

Description

technical field [0001] The present invention relates to a multi-source anthropogenic heat spatiotemporal quantification method based on machine learning, which is based on machine learning algorithm training model to calculate multi-type anthropogenic heat flux distribution in a specific period of time in a specific area, providing more accurate and efficient anthropogenic heat modeling A new way of thinking and method. Background technique [0002] Anthropogenic heat emissions have a significant impact on urban climate and air quality, and are also important data inputs for climate modeling. Accurate anthropogenic thermal data can be used as surface boundary conditions for regional or global scale climate simulations, and a reasonable assessment of the impact of human activities on the urban environment is an important basis for solving problems such as climate warming, heat island effect, and air pollution. Anthropogenic heat flux (AHF) is the anthropogenic heat per unit ...

AI Technical Summary

Problems solved by technology

In conclusion, current research does not fully consider the diversity and variability of training samples

On the other hand, AHF modeling based on machine learning is often affected by different algorithms. Different anthropogenic heat sources may have different optimal modeling algorithms due to differences in their spatio-temporal characteristics. This factor has not been considered and practiced yet.

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