Short-term rainfall forecast model based on the improved BP–NN algorithm

Abstract

The existing methods have been used the Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) derived from Global Navigation Satellite System (GNSS) for rainfall forecasting. However, the occurrence of rainfall is highly related to a myriad of atmospheric parameters, and a good forecast result cannot be obtained if it only depends on a single predictor. This study focused on rainfall forecasting by using a number of atmospheric parameters (such as: temperature, relative humidity, dew temperature, pressure, and PWV) based on the improved Back Propagation Neural Network (BP–NN) algorithm. Results of correlation analysis showed that each meteorological parameter contributed to rainfall. Therefore, a short-term rainfall forecast model was proposed based on an improved BP–NN algorithm by using multiple meteorological parameters. Two GNSS stations and collocated weather stations in Singapore were used to validate the proposed rainfall forecast model by using three years of data (2010–2012). True forecast (TFR), false forecast (FFR), and missed forecast (MFR) rate were introduced as evaluation indices. The experimental result revealed that the proposed model exhibited good performance with TFR larger than 96% and FFR of approximately 40%. The proposed method improved TFR by approximately 10%, whereas FFR was comparable to existing literature. This forecasted result further verified the reliability and practicability of the proposed rainfall forecasting method by using the improved BP–NN algorithm.