Preprints
https://doi.org/10.5194/nhess-2022-118
https://doi.org/10.5194/nhess-2022-118
22 Apr 2022
 | 22 Apr 2022
Status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

A Comparative Analysis of Machine Learning Algorithms for Snowfall Prediction Models in South Korea

Moon-Soo Song, Hong-Sik Yun, Jae-Joon Lee, and Sang-Guk Yum

Abstract. Heavy snowfall is a natural disaster that causes extensive damage in South Korea. Therefore, it is crucial to predict snowfall occurrence and establish countermeasures to reduce the damage caused by heavy snowfall. In this study, the meteorological and geographic data of the past 30 years were collected, and four machine learning algorithms were used: multiple linear regression (MLR), support vector regression (SVR), random forest regressor (RFR), and eXtreme gradient boosting (XGB). Subsequently, the performances of the machine learning algorithms were compared. Machine-learning algorithms were selected as regression models to predict heavy snowfall. Additionally, grid search and five-fold cross-validation techniques were used to improve learning performance. Model performance was evaluated by comparing the observed and predicted data. It was observed that the RFR model accurately predicted the occurrence of snowfall (R2 = 0.64) compared with other models with various statistical criteria. This result demonstrates the possibility of using the RFR model for heavy snowfall prediction. The proposed study can aid the government, local governments, and public institutions in developing strategies to respond to heavy snowfall in the fields of facilities, roads, and transportation.

Moon-Soo Song, Hong-Sik Yun, Jae-Joon Lee, and Sang-Guk Yum

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-118', Anonymous Referee #1, 25 Apr 2022
    • AC2: 'Reply on RC1', Sang-Guk Yum, 27 Jun 2022
  • AC1: 'Comment on nhess-2022-118', Sang-Guk Yum, 26 Apr 2022
  • RC2: 'Comment on nhess-2022-118', Anonymous Referee #2, 24 May 2022
    • AC3: 'Reply on RC2', Sang-Guk Yum, 27 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-118', Anonymous Referee #1, 25 Apr 2022
    • AC2: 'Reply on RC1', Sang-Guk Yum, 27 Jun 2022
  • AC1: 'Comment on nhess-2022-118', Sang-Guk Yum, 26 Apr 2022
  • RC2: 'Comment on nhess-2022-118', Anonymous Referee #2, 24 May 2022
    • AC3: 'Reply on RC2', Sang-Guk Yum, 27 Jun 2022
Moon-Soo Song, Hong-Sik Yun, Jae-Joon Lee, and Sang-Guk Yum
Moon-Soo Song, Hong-Sik Yun, Jae-Joon Lee, and Sang-Guk Yum

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Latest update: 31 Jan 2024
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Short summary
In this study, emerging engineering techniques such as machine learning and deep learning technique was applied to predict heavy snowfall prediction in the Korean Peninsula. More specifically, it was observed that the predictive model using the RFR algorithm had the best performance based on a comparison between the observed and predicted data. In addition, it was observed that the performance of the ensemble models (RFR and XGB) was better than that of the single regression models.
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