Abstract
Machine learning algorithms should be tested for use in quantitative precipitation estimation models of rain radar data in South Korea because such an application can provide a more accurate estimate of rainfall than the conventional ZR relationship-based model. The applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to quantitative precipitation estimation models was investigated using case studies with polarization radar data from Gwangdeoksan radar station. Various combinations of input variable sets were tested, and results showed that machine learning algorithms can be applied to build the quantitative precipitation estimation model of the polarization radar data in South Korea. The machine learning-based quantitative precipitation estimation models led to better performances than ZR relationship-based models, particularly for heavy rainfall events. The extreme learning machine is considered the best of the algorithms used based on evaluation criteria.
Highlights
Quantitative precipitation estimation (QPE) using remote sensing data has been widely used to investigate the spatial characteristics of precipitation events [1, 2]. is method can be used to obtain rainfall estimation at ungauged locations, cloud characteristics, and areal rainfall depth [3,4,5,6]. e spatial resolution of rain radar data is the finest of all these
To the best of our knowledge, advanced Machine learning (ML) algorithms, e.g., random forest (RF), gradient boosted model (GBM), and extreme learning machine (ELM), have not been employed for QPE of rain radar data in South Korea. is should be resolved because applying ML algorithms may provide more accurate rainfall estimation of rain radar data than the conventional ZR relation-based model. erefore, this study investigated the applicability of the ML algorithms for QPE using Gwangdeoksan radar station, South Korea, as a case study in order to enhance performance of QPE in radar data
Results of evaluation criteria are presented in Figure 3. e ML-based models lead to lower root-mean-square error (RMSE) than ZR relationship-based models
Summary
Quantitative precipitation estimation (QPE) using remote sensing data has been widely used to investigate the spatial characteristics of precipitation events [1, 2]. is method can be used to obtain rainfall estimation at ungauged locations, cloud characteristics, and areal rainfall depth [3,4,5,6]. e spatial resolution of rain radar data is the finest of all these. E spatial resolution of rain radar data is the finest of all these. Because of the spatial resolution of rain radar data, it is often applied into rainfallrunoff modeling, in terms of flash flood and urban flood modeling [10, 11]. E accurate QPE of radar data is the key for the accurate forecast of extreme hydrological events. Reflectivity and rainfall rate (ZR) relationship-based models have been used broadly for QPE models of rain radar data [12,13,14]. Because ZR relationship can be changed based on the characteristics of the rainfall event and the radar instrument used, various methodologies are applied to build ZR relationship-based QPE models and correct their estimations [15,16,17,18]. The ZR relationship-based model still has high uncertainty in a rainfall estimation [19,20,21]
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