Regional modeling of geographic distribution of field songs breeding in Hubei based on GIS and random forest algorithm

Relationship between distribution of field songs in Hubei and geographic environment in which the field songs are nurtured is explored in this paper, to provide new ideas and methods for empirical research on regional music． A total of 1248 sample data sets of field songs are selected． Geographic information system (GIS) is used to build a database for distribution of preliminarily selected field songs and influencing factors of the music elements． An analytical model is constructed of the system of influencing factors of the field songs based on random forest and shapley additive explanations (SHAP) interpretable algorithms． Validity of the model is evaluated through the receiver operating characteristic (ROC) curve, to analyze field songs distribution, relationship between music elements and geographic environment, and to analyze relationship between distribution of field songs, music elements and geographic environment． The model of the influence factor system of field songs constructed based on the random forest has a good prediction effect, and its area under the curve (AUC) value is 0.82． Importance ranking of influence factors for generation of field songs and music elements shows that multi-year average rainfall and multi-year average temperature are the main factors for the cultivation of field songs in Hubei． Random Forest and SHAP algorithms can predict distribution patterns of Hubei field to a certain extent, which is of great significance to the study of regional music culture and geographic correlation．