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Cited by 25 publications
(6 citation statements)
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“…Common machine learning algorithms include Support Vector Machine (SVM) [12], Bayesian Additive Regression Tree (BART) [13], and Quantile Regression Forests (QRF) [14]. In recent years, machine learning algorithms have been widely applied in various research domains related to electricity, such as power outage prediction [15], wind speed prediction [16], and storm outage prediction [17]. In [15], Yang et al proposed a method to quantify the uncertainty in power outage prediction modeling based on machine learning.…”
Section: Machine Learningmentioning
confidence: 99%
See 1 more Smart Citation
“…Common machine learning algorithms include Support Vector Machine (SVM) [12], Bayesian Additive Regression Tree (BART) [13], and Quantile Regression Forests (QRF) [14]. In recent years, machine learning algorithms have been widely applied in various research domains related to electricity, such as power outage prediction [15], wind speed prediction [16], and storm outage prediction [17]. In [15], Yang et al proposed a method to quantify the uncertainty in power outage prediction modeling based on machine learning.…”
Section: Machine Learningmentioning
confidence: 99%
“…In [15], Yang et al proposed a method to quantify the uncertainty in power outage prediction modeling based on machine learning. Cerrai et al [16] proposed three new modules based on Outage Prediction Model (OPM) and evaluated them on 76 extratropical and 44 convective storms. Bhuiyan et al [17] evaluated the performance of BART and QRF on wind speed prediction, and their study suggested that QRF outperformed BART.…”
Section: Machine Learningmentioning
confidence: 99%
“…Even single-cell convective events, which are the most commonly observed, cause the aforementioned damages. (Bhuiyan et al 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Recently, some investigations produced new ensemble forecasts by combining atmospheric and land variables information [18][19][20][21][22]. For example, Beck et al [18] presented a global 3-hourly precipitation dataset with a 0.25 spatial resolution, in which a correction for gauge under-catch and orographic effects was proposed by inferring precipitation from streamflow observations across the globe.…”
Section: Introductionmentioning
confidence: 99%