Evaluation of High-Intensity Precipitation Prediction Using Convolutional Long Short-Term Memory with U-Net Structure Based on Clustering

Author:

Kwon Taeyong1ORCID,Yoon Seong-Sim2ORCID,Shin Hongjoon3ORCID,Yoon Sanghoo1ORCID

Affiliation:

1. Department of Statistics, Daegu University, Gyeongsan-si 38453, Gyeongsangbuk-do, Republic of Korea

2. Korea Institute of Civil Engineering and Building Technology, 283, Goyangdae-ro, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea

3. Hydro-Power Research and Training Center, Korea Hydro & Nuclear Power Co., Ltd., Gyeongju-si 38120, Gyeongsangbuk-do, Republic of Korea

Abstract

Recently, Asia has experienced significant damage from extreme precipitation events caused by climate change. Improving the accuracy of quantitative precipitation forecasts over wide regions is essential to mitigate the damage caused by precipitation-related natural disasters. This study compared the predictive performances of a global model trained on the entire dataset and a clustered model that clustered precipitation types. The precipitation prediction model was constructed by combining convolutional long short-term memory with a U-Net structure. Research data consisted of precipitation events recorded at 10 min intervals from 2017 to 2021, utilizing radar data covering the entire Korean Peninsula. The model was trained on radar precipitation data from 30 min before the current time (t − 30 min, t − 20 min, t − 10 min, and t − 0 min) to predict the precipitation after 10 min (t + 10 min). The prediction performance was assessed using the root mean squared error and mean absolute error for continuous precipitation data and precision, recall, F1 score, and accuracy for the presence or absence of precipitation. The research findings indicate that, with sufficient training data for each precipitation type, models trained on clustered precipitation types outperform those trained on the entire dataset, particularly for predicting high-intensity precipitation events.

Funder

Korea Hydor & Nuclear Power Co., Ltd.

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

Reference30 articles.

1. Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J. (2019). Machine learning for precipitation nowcasting from radar images. arXiv.

2. RainNet v1. 0: A convolutional neural network for radar-based precipitation nowcasting;Ayzel;Geosci. Model Dev.,2020

3. CNGAT: A Graph Neural Network Model for Radar Quantitative Precipitation Estimation;Peng;IEEE Trans. Geosci. Remote Sens.,2021

4. Tran, Q.K., and Song, S.K. (2019). Computer vision in precipitation nowcasting: Applying image quality assessment metrics for training deep neural networks. Atmosphere, 10.

5. Very short-term rainfall prediction based on radar image learning using deep neural network;Yoon;J. Korea Water Resour. Assoc.,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3