Hourly Rainfall Forecast Model Using Supervised Learning Algorithm

Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is prop...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 9
Hlavní autori: Zhao, Qingzhi, Liu, Yang, Yao, Wanqiang, Yao, Yibin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Accuracy
Algorithms
Autocorrelation
Data models
Global navigation satellite system
Global Navigation Satellite System (GNSS)
Machine learning
Mathematical models
Meteorological parameters
Navigation
Navigational satellites
Parameters
precipitable water vapor (PWV)
Predictive models
Rain
Rainfall
rainfall forecast model
Resolution
Root-mean-square errors
Supervised learning
support vector machine (SVM)
Support vector machines
Temperature data
Water vapor
Water vapour
Weather forecasting
ISSN:0196-2892, 1558-0644
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Popis
Shrnutí:Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy.
Bibliografia:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3054582