Abstract
In this paper, base on the radar rainfall products, a classification model which can forecast rain or shine weather in precipitation nowcasting is established from the data-driven point, and the model is implemented with the Bayesian optimization XGBoost method. Finally, the model results are compared with the SVM method, the Random Rorest (RF) method and the Gradient Boosting Decision Tree (GBDT) method. The results show that the XGBoost method is better than the other three methods on rain or shine weather forecast in precipitation nowcasting. The TS score and the POD are at a high level, so the model has a certain practical value.
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Acknowledgement
This research was financially supported by the Natural Science Foundation of Guangxi (NO. 2018JJA150144, 2018GXNSFAA294079) and the National Science Foundation of China (NO. 61562008, 41401524, 4166010274).
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Mai, X., Zhong, H., Li, L. (2021). Research on Rain or Shine Weather Forecast in Precipitation Nowcasting Based on XGBoost. In: Meng, H., Lei, T., Li, M., Li, K., **ong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_143
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DOI: https://doi.org/10.1007/978-3-030-70665-4_143
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