Urban three-dimensional wind speed prediction method based on three-dimensional morphological characteristics and machine learning

By constructing a three-dimensional wind speed prediction method for cities based on three-dimensional morphological features and machine learning, the problem of existing technologies being unable to accurately characterize the differential impact of building spatial morphology at different heights on airflow is solved. This method achieves efficient three-dimensional wind speed prediction across the entire height range of the urban canopy, improving prediction accuracy and cross-scenario adaptability.

CN122154498APending Publication Date: 2026-06-05SHANDONG UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANDONG UNIV OF TECH
Filing Date
2026-05-09
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing urban wind speed prediction methods are unable to accurately characterize the differentiated impact of building spatial morphology at different heights on local airflow, resulting in the inability to achieve three-dimensional wind speed prediction across the entire height range of the urban canopy, thus limiting the model's cross-scenario adaptability and engineering application value.

Method used

By employing a method based on three-dimensional morphological features and machine learning, a three-dimensional wind speed level prediction model for cities is constructed by building a vertically hierarchical computing system and regular grid prediction units, combined with a random forest classification algorithm. This model accurately depicts the differentiated impact of building morphology on airflow and achieves efficient prediction of the three-dimensional wind speed field.

Benefits of technology

It improves the accuracy and computational efficiency of wind speed prediction, reduces computational costs, has excellent cross-scenario adaptability, and can provide efficient and reliable three-dimensional wind environment analysis support for urban planning.

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Abstract

The application belongs to the technical field of wind speed prediction, and particularly relates to a city three-dimensional wind speed prediction method based on three-dimensional morphological characteristics and machine learning, steps of which comprise: obtaining a city three-dimensional building data set of a research area; dividing the research area into a plurality of regular grid prediction units, and establishing a space mapping relationship; forming a three-dimensional prediction unit set determined by the regular grid prediction units and a prediction height layer; constructing an original three-dimensional city morphological characteristic set; performing direction unification processing on the original three-dimensional city morphological characteristic set to obtain a direction unification model input sample, and constructing an extended wind direction sample; constructing a wind speed grade prediction model by using a random forest classification algorithm and training to obtain a final city three-dimensional wind speed grade prediction model, and taking the direction unification model input sample of an actual research area as input to output a wind speed grade prediction result. The application can realize efficient prediction of a city canopy three-dimensional wind speed field, and has high precision, fast calculation and strong cross-scene adaptation.
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