一般化加法モデルとLASSOによる台風の風速の予測

抄録

In the GeoSciAI2024 meteorology challenge, a regression model was proposed to predict maximum wind speed for 24 hours later of simulated typhoons using the provided track data and two-dimensional atmospheric data. The variables derived from current data and data from 12 hours earlier were used as explanatory variables. First, typhoons were stratified into 21 regions based on their location. Predictive models were made for each stratum and generalized additive model (GAM) were used for regions with sufficient training data, while LASSO regression was applied for regions with fewer than 300 training samples. Despite the stratification into 22 regions, it was observed in many areas that including latitude and longitude information in the generalized linear models tended to reduce the training mean squared error. These findings suggest that more detailed grouping based on location may enhance the accuracy of the model.