Journal of Korean Society of Disaster and Security (한국방재안전학회논문집)

Korean Society of Disaster & Security (한국방재안전학회)
권호 표지
DOI QR코드

DOI QR Code

LSTM Prediction of Streamflow during Peak Rainfall of Piney River

LSTM을 이용한 Piney River유역의 최대강우시 유량예측

  • Kareem, Kola Yusuff (Dept. of Advance Science and Technology Convergence, Kyungpook National Univ.) ;
  • Seong, Yeonjeong (Dept. of Advance Science and Technology Convergence, Kyungpook National Univ.) ;
  • Jung, Younghun (Dept. of Advance Science and Technology Convergence, Kyungpook National Univ.)
  • ;
  • 성연정 (경북대학교 미래과학기술융합학과) ;
  • 정영훈 (경북대학교 미래과학기술융합학과)
  • Received : 2021.12.01
  • Accepted : 2021.12.18
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m3/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

유량예측은 효과적인 홍수관리 및 수자원 계획을 위한 매우 중요한 재난방지 접근법이다. 현재 기후변화로 인한 집중호우가 나날이 증가하고 있어 막대한 기반시설 손실과 재산, 인명 피해가 발생하고 있다. 본 연구는 미국 테네시주 Hickman County의 Vernon에 있는 Piney Resort의 최근 홍수사례분석을 통해 최대 강우 시나리오에서 유량예측에 대한 강우의 기여도를 측정했다. Piney River 유역내 USGS 두개의 관측소(03602500, 03599500)에서 20년(2000-2019) 동안의 일별 하천 유량, 수위 및 강우 데이터를 수집했고, Long Short Term Memory(LSTM)을 사용하였다. 또한, Tensorflow, Keras Machine learning frameworks, Python을 이용하여 14일로 구별된 유량 값을 예측하였다. 또한, 모델이 2021년 8월 21일의 범람 이벤트를 예측할 수 있었는지를 결정하는 데 사용되었다. 전체 데이터(수위, 유량 및 강우량)가 포함된 LSTM 모델은 일부 강우 모델을 제외하고 지속성 모델보다 우수한 성능을 보였으며, 강우자료만 이용하여 유량예측을 하는 것은 충분하지 않음을 나타냈다. 결과는 LSTM 모델은 0.68 및 13.84m3/s의 최적 NSE 및 RMSE 값을 나타냈고, 가장 낮은 예측 오차로 예측 최대유량은 94m3/s로 나타났다. 향후 강우 패턴에 대한 다양한 분석이 이루어진다면 효율적인 홍수 경보 시스템 및 정책을 설계하는 관련 연구에 도움을 줄 것으로 판단된다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021NRF-2020R1I1A3052159).

References

  1. Chiew, F. H. S. (2006). Estimation of Rainfall Elasticity of Streamflow in Australia. Hydrol. Sci. J. 51(4): 613-625, doi:10.1623/hysj.51.4.613.
  2. Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations Using RNN Encoder-decoder for Statistical Machine Translation. EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. pp. 1724-1734. https://doi.org/10.3115/v1/d14-1179.
  3. Demirel, M. C., Venancio, A., and Kahya, E. (2009). Flow Forecast by SWAT Model and ANN in Pracana Basin, Portugal. Adv. Eng. Softw. 40: 467-473. https://doi.org/10.1016/j.advengsoft.2008.08.002
  4. Ghimire, S., Deo, R. C., Raj, N., and Mi, J. (2019). Deep Solar Radiation Forecasting with Convolutional Neural Network and Long Short-term Memory Network Algorithms. Appl. Energy. 253: 113541. https://doi.org/10.1016/j.apenergy.2019.113541
  5. Ghimire, S., Yaseen, Z. M., Farooque, A. A., Deo, R. C., Zhang, J., and Tao, X. (2021). Streamflow Prediction Using an Integrated Methodology based on Convolutional Neural Network and Long Short-term Memory Networks. Scientific Reports. 11(1): 1-26. https://doi.org/10.1038/s41598-021-96751-4.
  6. Grossman, D., Buckley, N., and Doyle, M. (2015). Data Intelligence for 21st Century Water Management: A Report from the 2015 Aspen-Nicholas Water Forum. Aspen-Nicholas Water Forum. Retrieved from https://www.aspeninstitute.org/publications/data-intelligence-21st-century-water-management-report-2015-aspen-nicholas-water-forum/ Accessed 9 December 2021.
  7. Jaiswal, R. K., Ali, S., and Bharti, B. (2020). Comparative Evaluation of Conceptual and Physical Rainfall-runoff Models. Applied Water Science. 10(1): 1-14. https://doi.org/10.1007/s13201-019-1058-x
  8. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep Learning. Nature. 521(7553): 436-444. https://doi.org/10.1038/nature14539
  9. Marugan, A. P., Marquez, F. P. G., Perez, J. M. P., and Ruiz-Hernandez, D. (2018). A Survey of Artificial Neural Network in Wind Energy Systems. Appl. Energy. 228: 1822-1836. https://doi.org/10.1016/j.apenergy.2018.07.084
  10. Meka, R., Alaeddini, A., and Bhaganagar, K. (2021). A Robust Deep Learning Framework for Short-term Wind Power Forecast of a Full-scale Wind Farm Using Atmospheric Variables. Energy. 221: 119759. https://doi.org/10.1016/j.energy.2021.119759
  11. Oh, S. L., Ng, E. Y. K., San Tan, R., and Acharya, U. R. (2018). Automated Diagnosis of Arrhythmia Using Combination of CNN and LSTM Techniques with Variable Length Heart Beats. Comput. Biol. Med. 102: 278-287. https://doi.org/10.1016/j.compbiomed.2018.06.002
  12. Park, K., Jung, Y., Kim, K., and Park, S. K. (2020). Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates. Water. 12: 3537. https://doi.org/10.3390/w12123537.
  13. Sahiner, B., Pezeshk, A., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., and Giger, M. L. (2019). Deep Learning in Medical Imaging and Radiation Therapy. Medical Physics. 46(1): e1-e36. https://doi.org/10.1002/mp.13264
  14. Vidal, A. and Kristjanpoller, W. (2020). Gold Volatility Prediction Using a CNN-LSTM approach. Expert Syst. Appl. 157: 113481. https://doi.org/10.1016/j.eswa.2020.113481
  15. WKRN (2021). Piney River Resort Campers Witness Devastation during Flooding. https://www.wkrn.com/news/piney-river-resort-campers-witness-devastation-during-severe-flooding, accessed 22 August 2021.
  16. Zhang, Z. (2017). Artificial Neural Network. In Multivariate Time Series Analysis in Climate and Environmental Research. 1-35 https://doi.org/10.1007/978-3-319-67340-0_1.
  17. Zhao, R., Yan, R., Wang, J., and Mao, K. (2017). Learning to Monitor Machine Health with Convolutional Bi-directional LSTM Networks. Sensors. 17: 273. https://doi.org/10.3390/s17020273