Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

Shin, Ju Young; Ro, Yonghun; Cha, Jaehyuk; Kim, Kyu-Rang; Ha, Jong-Chul

doi:10.1155/2019/6542410

Cited by 17 publications

(7 citation statements)

References 47 publications

(47 reference statements)

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“…Nevertheless, the monthly mean visibility estimation accuracy was as follows: bias=-0.11 km, RMSE=2.05 km, and r=0.93. These results showed lower variability and higher accuracy than previous studies that quantitatively estimated precipitation using satellite-based (Nguyen et al, 2021), radar-based (Shin et al, 2019), andnumerical model-based (Ko et al, 2020) data using ML algorithms. Therefore, the application of…”

Section: Accepted Manuscriptcontrasting

confidence: 54%

Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Kim

Wan

Chang

et al. 2022

Aerosol Air Qual. Res.

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Visibility is an important indicator of air quality and of any consequent meteorological and climate change. Therefore, visibility in Seoul, which is the most polluted city in South Korea, was estimated using machine learning (ML) algorithms based on meteorological (temperature, relative humidity, and precipitation) and particulate matter (PM10 and PM2.5) data acquired from an automatic weather station, and the estimated visibility was compared with the observed visibility. Meteorological data, observed at 1-h intervals between 2018 and 2020, were used. Through learning and validation of each ML algorithm, the extreme gradient boosting (XGB) algorithm was found to be most suitable for visibility estimations (bias=0 km, root mean square error (RMSE)=0.08 km, and r=1 for training data set). Among the meteorological and particulate matter data used for learning the XGB algorithm, the relative importance of PM2.5 and relative humidity variables were high (51% and 19%, respectively), whereas precipitation and wind speed had the low relative importance (approximately 1%). The estimation accuracy for the test dataset was good (bias=-0.11 km, RMSE=2.08 km, and r=0.94); the estimation accuracy was higher in the dry season (bias=-0.06 km, RMSE=1.79 km, and r=0.96) than in the rainy season (bias=-0.17 km, RMSE=2.34 km, and r=0.91). The results of this study indicated a higher correlation than the results of previous visibility estimation studies. The proposed method promotes accurate estimation of visibility in areas with poor visibility, and thus, it can be used to assess public health in areas with poor air quality.

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Section: Accepted Manuscriptcontrasting

confidence: 54%

Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Kim

Wan

Chang

et al. 2022

Aerosol Air Qual. Res.

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“…Therefore, the deep neural network is a new option for improving the accuracy of QPE (Wu et al, 2021). Shin et al (2019) evaluated the applicability of random forests, stochastic gradient augmentation models, and extreme learning machine methods to QPE and used multivariate combinations as inputs. The results show that the approach based on machine learning performs better than the model with Z-R relationship and resolves the time lag between the radar data and ground observations, and the accuracy is improved by an appropriate combination of multiple input variables.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

et al. 2022

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Heavy precipitation tends to cause mountain torrents, urban waterlogging and other disasters. It poses a serious threat to people’s life and property safety. Therefore, real-time quantitative precipitation estimation is especially important to keep track of precipitation changes and reduce negative impacts. However, high-resolution and high-accuracy quantitative precipitation estimation is a challenging task due to the complex spatial and temporal variability of microphysics in precipitation processes. Previous studies have focused only on small-scale radar reflectivity factors above rain gauges and did not pay enough attention to the contribution of covariates to model performance. Meteorological and geographical factors play an important role in rain process, so these factors are taken into account during our research. In this study, a quantitative precipitation estimation model that can employ multi-scale radar reflectivity factors and fuse meteorological and geographical factors is proposed to further improve precipitation accuracy. In addition, we propose the muti-scale self-attention (MS-SA) module that can further utilize information at multiple spatial scales to improve the accurate precipitation estimation. The proposed model reduced the root mean square error of precipitation estimation by 83.8% compared to the conventional Z-R relationship that correlates the rainfall and radar reflectivity factors, i.e., Z=aRb, and by 43.7, 24.6, and 22.7% compared to the back propagation neural network (BPNN), convolutional neural network (CNN), and convolutional neural network with the addition of meteorological factors and geographical factors as covariates in the proposed model, respectively. Therefore, we can conclude that multi-scale radar reflectivity factors fused with meteorological and geographical factors can produce more accurate precipitation estimation.

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“…Despite the introduction of remote observation instruments, such as radar and satellites, generating quantitative rainfall data remains challenging because of uncertainty arising from the di culties in observations over mountains and oceans (Tesfagiorgis et Studies have attempted to merge radar and ground observations or use machine learning to generate quantitative rainfall data (Tang et al 2018;Shin et al 2019;Ro and Yoo 2020). These research showed that how important to secure accurate ground observation data for precise analysis of water resources and water cycle.…”

Section: )mentioning

confidence: 99%

Comparative study of rainfall measurement by optical disdrometer, tipping-bucket rain gauge, and weighing precipitation gauge

Chang

Hwang

et al. 2022

Preprint

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In this study, the rainfall measurement characteristics of an optical Particle Size Velocity (Parsivel) disdrometer, tipping-bucket rain gauge (TBG), and Pluvio weighing precipitation gauge (WPG) were analyzed and compared. Correlation analysis was performed between the 10-min and 1-h rainfall data observed with the Parsivel, TBG, and Pluvio from 2010 to 2019 at the Cloud Physics Observation Site which is located in north-east area of South Korea (N37.6869, E128.7586). At higher rainfall intensities, the Parsivel observed more rainfall; however, the TBG lost more rainfall during observation. The correlation between the Pluvio and Parsivel data was higher than that between the TBG and Parsivel data. Additionally, the Pluvio showed reduced loss in the rainfall observation than that by the TBG. The correlation between the Pluvio and TBG data was the highest, and the coefficient of determination increased by a maximum of 42.08% for 1-h rainfall compared to that for 10-min rainfall. Therefore, the Pluvio can generate relatively accurate rainfall data for water resource utilization.

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Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

Cited by 17 publications

References 47 publications

Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Quantitative Precipitation Estimation Model Integrating Meteorological and Geographical Factors at Multiple Spatial Scales

Comparative study of rainfall measurement by optical disdrometer, tipping-bucket rain gauge, and weighing precipitation gauge

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