| Title |
Comparing Monthly Precipitation Predictions Using Time Series Analysis with Deep Learning Models |
| Authors |
정연지(Chung Yeon-Ji);김민기(Kim Min-Ki);엄명진(Um Myoung-Jin) |
| DOI |
https://doi.org/10.12652/Ksce.2024.44.4.0443 |
| Keywords |
강수량 예측;통계적 모형;딥러닝 모형;적합 모형 Precipitation forecasting;Statistical model;Deep learning model;Optimal model |
| Abstract |
This study sought to improve the accuracy of precipitation prediction by utilizing monthly precipitation data for each region over the past 30 years. Using statistical models (ARIMA, SARIMA) and deep learning models (LSTM, GBM), we learned monthly precipitation data from 1983 to 2012 in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. Based on this, monthly precipitation was predicted for 10 years from 2013 to 2022. As a result of the prediction, most models accurately predicted the precipitation trend, but showed a tendency to underpredict the actual precipitation. To solve these problems, appropriate models were selected for each region and season. The LSTM model showed suitable results in Gangneung, Gwangju, Daegu, Daejeon, Busan, Seoul, Jeju, and Chuncheon. When comparing forecasting power by season, the SARIMA model showed particularly suitable forecasting performance in winter in Gangneung, Gwangju, Daegu, Daejeon, Seoul, and Chuncheon. Additionally, the LSTM model showed higher performance than other models in the summer when precipitation is concentrated. In conclusion, closely analyzing regional and seasonal precipitation patterns and selecting the optimal prediction model based on this plays a critical role in increasing the accuracy of precipitation prediction. |