针对传统降雨预测理论错报率高及算法拟合精度低等缺陷，将与降雨过程相关的多种气象参数（温度、相对湿度、露点温度、气压等）及时间参数（年积日和天积时）引入短临降雨预测模型的构建。将新加坡2个GNSS和气象并址的测站（NTUS、SNUS）2010～2012年的气象数据及降雨数据作为样本，研究气象参数与降雨数据的时变特征，结果发现，降雨发生前气象参数均表现出异常的变化趋势，且各类气象参数与降雨均表现出弱相关性特征。基于该发现，首次应用最小二乘支持向量机（LS-SVM）模型实现对未来降雨的预测，将气象参数和时间参数作为模型输入，降雨数据作为模型输出，并利用正确率（TFR）和错报率（FFR）评价LS-SVM模型的精度。实验结果表明，该算法可预测出99%的降雨事件，且FFR为40%；与现有最小二乘降雨预测模型相比，该算法的FFR降低近20%，TFR提高近10%。

Considering the high false forecast rate and the low accuracy of the existing method, we introduce a variety of meteorological parameters(temperature, relative humidity, dew point temperature, surface pressure) and time-related parameters (day of year, hour of day) correlated to the rainfall process. We use meteorological parameters and rainfall data of two GNSS meteorological stations in Singapore over the period of 2010-2012 to study the time-varying characteristics between meteorological parameters and rainfall data. We find that the meteorological parameters show abnormal variation trends before rainfall, and all kinds of meteorological parameters show weak correlation with rainfall. Based on the above discovery, we firstly forecast short-term rainfall using the least squares support vector machine (LS-SVM). This method takes the meteorology and time parameters as the model input, rainfall data as the model output, and uses the true forecast rate (TFR) and false forecast rate (FFR) to evaluate the accuracy of LS-SVM model. The results indicate that the proposed algorithm can forecast 99% rainfall events, and the FFR is 40%. Compared with the existed least square rainfall forecast model, the FFR of this paper is reduced by nearly 20%, and TFR is increased by nearly 10%.