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A Comparative Study on Reservoir Level Prediction Performance Using a Deep Neural Network with ASOS, AWS, and Thiessen Network Data

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(3), pp.67-74
  • DOI : 10.9708/jksci.2024.29.03.067
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 15, 2024
  • Accepted : March 12, 2024
  • Published : March 29, 2024

Park Hyeseung 1 Hyun-Ho Yang 2 Hojun Lee 3 Jongwook Yoon 1

1협성대학교
2군산대학교
3수리이엔씨

Accredited

ABSTRACT

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method’s accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.