Modeling and prediction of monthly precipitation in Nanjing based on machine learning methods

Abstract

Accurate precipitation prediction plays a crucial role in regional flood prevention and mitigation,water resources management,and socioeconomic development.However,the precipitation process is influenced by the interaction of multi-scale meteorological factors and shows significant nonlinearity and spatiotemporal heterogeneity.Traditional numerical models fail to effectively capture its complex evolution patterns. [Methods]Based on random forest stacking techniques,six hybrid prediction models were constructed:KNN-LSTM,SARIMA-KNN,SARIMA-Prophet,SARIMA-LSTM,Prophet-LSTM,and Prophet-KNN.Monthly precipitation data from 1990 to 2023 at station 58238 in Nanjing were used for modeling,with data from 1990 to 2020 used as the training set and data from 2021 to 2023 used as the testing set.The regional generalization ability was validated using contemporaneous data from 12 independent meteorological stations in Jiangsu Province. [Results]The result showed that the SARIMA-LSTM hybrid model,which integrated the seasonal decomposition advantage of SARIMA with the long-term dependency capturing ability of LSTM,achieved the highest prediction accuracy on the testing set,with R²=0.904,MAE=16.16 mm,and MSE=477.87 mm².The regional generalization validation demonstrated that the model achieved $ \overline{R^{2}}$=0.919,$ \overline{M A E}$ =15.33 mm,and $ \overline{M S E}$=537.52 mm² across 13 meteorological stations in Jiangsu Province,indicating good spatial generalization capability. [Conclusion] The constructed hybrid models exhibit excellent predictive performance,providing reliable technical support for precipitation prediction in the lower Yangtze River region.This holds significant application value for the optimization of regional water resource allocation and disaster early warning.

Keywords

precipitation prediction / machine learning / hybrid models / SARIMA-LSTM / regional generalization / monthly precipitation / randomforeststacking / influencingfactors

Funding

National Natural Science Foundation of China(52309025)