Short-Term Rainfall Forecasting Using Multi-Layer Perceptron

Rainfall forecasting is crucial in the field of meteorology and hydrology. However, existing solutions always achieve low prediction accuracy for short-term rainfall forecasting. Atmospheric forecasting models perform worse in many conditions. Machine learning approaches neglect the influences of physical factors in upstream or downstream regions, which make forecasting accuracy fluctuate in different areas. To improve the overall forecasting accuracy for short-term rainfall, this paper proposes a novel solution called Dynamic Regional Combined short-term rainfall Forecasting approach (DRCF) using Multi-layer Perceptron (MLP). First, Principal Component Analysis (PCA) is used to reduce the dimension of thirteen physical factors, which serves as the input of MLP. Second, a greedy algorithm is applied to determine the structure of MLP. The surrounding sites are perceived based on the forecasting site. Finally, to solve the clutter interference which is caused by the extension of the perception range, DRCF is enhanced with several dynamic strategies. Experiments are conducted on data from 56 real-world meteorology sites in China, and we compare DRCF with atmospheric models and other machine learning approaches. The experimental results show that DRCF outperforms existing approaches in both threat score (TS) and root mean square error (RMSE).