GNSS-derived PWV and meteorological data for short-term rainfall forecast based on support vector machine. Issue 4 (15th August 2022)

• Record Type: Journal Article
• Authors: Liu, Yang; Zhao, Qingzhi; Li, Zufeng; Yao, Yibin; Li, Xin
• Is Part Of: Advances in space research. Volume 70:Issue 4(2022)
• Page Start: 992
• Page End: 1003
• Publication Date: 2022-08-15
• Subjects: GNSS -- PWV -- SVM -- Meteorological parameters -- Rainfall forecasting; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2022.05.057
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: Rainfall is one of the key triggers of severe weather, such as floods and mudslides. It is difficult to forecast accurately and timely. With the extended application of global navigation satellite system (GNSS) into meteorology, existing methods have used GNSS-derived atmospheric parameters to forecast rainfall. However, low success rate and high false alarm rate exist because only a few related predictors have been used. To overcome the disadvantages of existing rainfall forecasting models, this study proposes a short-term rainfall forecast model based on the support vector machine (SVM) algorithm. GNSS-derived precipitable water vapor (PWV), meteorological parameters (such as: pressure (P), temperature (T), relative humidity (RH), and dew point temperature (DPT)); and time-related parameters, such as day of the year (DoY), hour of the day (HoD), and minute of the hour (MoH); are considered as rainfall forecast parameters. The correlation analysis between meteorological parameters/PWV and rainfall is first performed by using a five-minute PWV time series obtained from GNSS observations in Singapore over the period of 2010 to 2012. SVM is then used to establish the rainfall forecast model and forecast the occurrence of rainfall from 10 to 60 min in advance. Experimental results present that (1) various parameters (T, P, RH, DPT, and PWV) show different variation characteristics before the occurrence of rainfall, thereby indicating that good results can be obtained to describe the process of rainfall occurrence by using multiple parameters and (2) the SVM-based rainfall forecast model can forecast rainfall with and average true (TFR) and false (FFR) forecast rates of more than 99% and less than 23%, respectively. In this study, the SVM-based rainfall forecast model with multiple factors performs better than existing methods. TFR and FFR of the SVM-based rainfall forecast model are improved by nearly 10% and reduced by more than 10%, respectively.