Machine learning Model, predicting South Korea precipitation

The risks arising from precipitation are immediate and broad. The Korea Meteorological Administration also carries out predictions and observations on precipitation in Korea. Still, the results are inaccurate due to cases where precipitation in the north is not well matched or incorrect observations are made due to difficulties in identifying the surface. We intend to construct accurate precipitation data by analyzing a machine-learning model. If highly influential precipitation data are properly secured, it is expected that it will benefit greatly from more accurate analysis of health effects.After collecting and refining data from 2015 to 2023 for about 50 detailed variables related to temperature, river, and snow using Google Earth Engine data, which is the input of the model, ASOS (Automated Synchronous Observing System) data from the Korea Meteorological Administration are collected in the same manner as in the relevant year and combined based on GeoID. Among many ASOS data parameters, precipitation data were used and provided as ground truth for the model. Random Forest, XGB (Extreme Gradient Boosting), which is known to be effective with those data, is used and the best performance model is chosen as the final model.The R2 of the model using XGB was about 0.8.When the additional hyperparameter was not used, the R2 of the model using XGB was the best at 0.8 which is a high value compared to the others