The invention discloses an urban fire
station site selection prediction method based on GMM clustering and
machine learning, and the method comprises the steps: firstly, determining a clustering center of a research region through employing a GMM clustering
algorithm based on POI data, building a Voronoi diagram in consideration of the boundary features of the region and county on this basis, and achieving the reasonable division of the research region; secondly, based on consideration of urban
fire risk distribution, road
accessibility and the like, multi-source spatial data such as POI, road density,
population density and the like are fused, and a comprehensive
feature matrix is constructed; and finally, training and evaluating by respectively adopting five
machine learning models, namely a
random forest (RF), a
gradient boosting decision tree (GBDT), a
support vector machine (SVM), an
extreme gradient boosting (XGBoost) and
logistic regression (LR), and selecting a GBDT model with optimal F1
score and optimal AUC value to predict and analyze the
site selection of the fire
station. According to the method,
site selection prediction is performed on the basis of region division based on a GMM model and a Voronoi diagram, so that the defect that direct gridding of a research region neglects significant differences of different regions in aspects of
population density, urban functions,
terrain and the like can be effectively avoided, and the subjectivity of determining the size of a grid unit can be overcome; and rationality and practicability of site selection of the fire
station are effectively improved.