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2020 Vol.13, Issue 4 Preview Page

Original Article

Development of a Data-Driven Model for Forecasting Outflow to Establish a Reasonable River Water Management System

합리적인 하천수 관리체계 구축을 위한 자료기반 방류량 예측모형 개발

Hyung Ju Yoo1
,
Seung Oh Lee2
,
Seo Hye Choi3
,
Moon Hyung Park4*
유 형주1
,
이 승오2
,
최 서혜3
,
박 문형4*
1Ph.D Student, Dept. of Civil Engineering, Hongik University
2Professor, Dept. of Civil Engineering, Hongik University
3Researcher, Korea Institute of Civil Engineering and Building Technology
4Senior Researcher, Korea Institute of Civil Engineering and Building Technology
1홍익대학교 토목공학과 박사과정
2홍익대학교 토목공학과 교수
3한국건설기술연구원 국토보전연구본부 선진연구원
4한국건설기술연구원 국토보전연구본부 수석연구원
*Corresponding Author
31 December 2020. pp. 75-92
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Abstract

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

Keywords
Return water
River water management system
LSTM
GRU
SVR

일반적으로 물수지 분석 시 공급에 해당되는 회귀수량의 경우 용수별 회귀율을 일률적으로 정하여 산정하는 방법을 채택하고 있어 정확한 가용유량을 산정하지 못하는 한계를 갖고 있다. 이에 본 연구에서는 회귀수 중 하·폐수에 초점을 두었고 인공신경망 등의 기계학습 모형을 적용하여 하수종말처리장의 방류량 예측 모형을 개발하였다. 시계열 자료예측 시 사용되는 주요 기계학습 모형인 LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), SVR (Support Vector Regression)모형을 적용하였으며 관측 값과 예측 값을 비교하는 오차지표를 통하여 방류량 예측의 최적의 모형을 선정하였다. 모형 적용 결과, GRU 모형의 평균제곱근 오차(Root Mean Square Error, RMSE)는 LSTM 모형과 SVR 모형보다 작으며 Nash-Sutcliffe 계수(NSE)는 LSTM 모형과 SVR 모형보다 큰 것을 확인하였고, 이를 근거로 하수종말처리장의 방류량 예측에 최적모형은 GRU 모형이라고 판단하였다. 다만, 극값에서는 예측 값이 과소 및 과대 산정되는 경향을 보여 추후 예측 정확도 향상을 위해서는 극한사상에 대한 추가자료 구축 및 입력 자료의 최소시간단위를 축소하는 것이 필요할 것으로 판단되었다. 또한, 예측하고자 하는 대상지의 용수이용량을 검토하고 계절적 영향을 반영할 수 있는 추가인자를 고려하게 되면 기후변동성에 대비하여 정확한 방류량 예측이 가능하며 예측 결과를 토대로 종합적인 하천수 사용관리 및 물이용 계획 수립을 위한 기초자료로 활용될 수 있을 것으로 기대된다.

키워드
회귀수
하천수 관리체계
LSTM
GRU
SVR
References
1
Agarap, A. F. (2018). Deep Learning using Rectified Linear Units (RELU). arXiv preprint arXiv:1803.08375.
2
Chen, W. B., Liu, W. C., and Hsu, M. H. (2012). Comparison of ANN Approach with 2D and 3D Hydrodynamic Models for Simulating Estuary Water Stage. Advances in Engineering Software. 45(1): 69-79. 10.1016/j.advengsoft.2011.09.018
3
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-decoder for Statistical Machine Translation. arXiv Preprint arXiv: 1406.1078. 10.3115/v1/D14-1179
4
Choi, S., Kang, S., Lee, D., and Kim, J. (2018). A Study on Water Supply and Demand Prospects for Water Resources Planning. Journal of the Korean Society of Hazard Mitigation. 18(7): 589-596. 10.9798/KOSHAM.2018.18.7.589
5
Choi, S. H., Kwon, H. H., and Park, M. (2019). Prediction on the Amount of River Water use using Support Vector Machine with Time Series Decomposition. Journal of Korea Water Resources Association. 52(12): 1075-1086.
6
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv preprint arXiv: 1412.3555.
7
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and Schmidhuber, J. (2016). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems. 28(10): 2222-2232. 10.1109/TNNLS.2016.258292427411231
8
Hochreiter, S. (1998). The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions. International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems. 6(02): 107-116. 10.1142/S0218488598000094
9
Jozefowicz, R., Zaremba, W., and Sutskever, I. (2015, June). An Empirical Exploration of Recurrent Network Architectures. In International Conference on Machine Learning (pp. 2342-2350).
10
Korea Meteorological Administration (KMA). (2020). Korea Climate Change Assessment Report 2020. Seoul: KMA.
11
Lee, G., Jung, S., and Lee, D. (2018). Comparison of Physics-based and Data-driven Models for Streamflow Simulation of the Mekong River. Journal of Korea Water Resources Association. 51(6): 503-514.
12
Lee, J. Y., Kim, H. I., and Han, K. Y. (2020). Linkage of Hydrological Model and Machine Learning for Real-time Prediction of River Flood. Journal of The Korean Society of Civil Engineers. 40(3): 303-314.
13
Lee, S. W., Kim, Y. O., and Lee, D. R. (2005). Quantifying Uncertainty for the Water Balance Analysis. Journal of Korea Water Resources Association. 38(4): 281-292. 10.3741/JKWRA.2005.38.4.281
14
Ministry of Land Infrastructure and Transport (MOLIT). (2016). National Water Resources Plan (2011~2020) (3rd revision). Sejong: MOLIT.
15
Oh, J. H., Ryu, K. S., Bok, J. S., Jang, Y. S., Bae, Y. D., and Lee, B. G. (2019). Water Supply-and-Demand Analysis Considering the Actual Water-Use System in the East Basin of Han River. Journal of the Korean Society of Hazard Mitigation. 19(7): 529-543. 10.9798/KOSHAM.2019.19.7.529
16
Olah, C. (2018). Understanding Lstm Networks, August 2015. URL https://colah.github.io/posts/2015-08-Understanding-LSTMs.
17
Pascanu, R., Mikolov, T., and Bengio, Y. (2012). Understanding the Exploding Gradient Problem. CoRR, abs/1211.5063, 2, 417.
18
Ruxton, G. D. (2006). The Unequal Variance t-test is an Underused Alternative to Student's t-test and the Mann-Whitney U test. Behavioral Ecology. 17(4): 688-690. 10.1093/beheco/ark016
19
Tran, Q. K. and Song, S. K. (2017). Water Level Forecasting based on Deep Learning: A use Case of Trinity River-Texas-The United States. Journal of KIISE. 44(6): 607-612. 10.5626/JOK.2017.44.6.607
20
Vapnik, V., Guyon, I., and Hastie, T. (1995). Support Vector Machines. Mach. Learn. 20(3): 273-297. 10.1007/BF00994018
21
Yeo, W. K., Seo, Y. M., Lee, S. Y., and Jee, H. K. (2010). Study on Water Stage Prediction using Hybrid Model of Artificial Neural Network and Genetic Algorithm. Journal of Korea Water Resources Association. 43(8): 721-731. 10.3741/JKWRA.2010.43.8.721
22
Yoo, H. J., Kim, D. H., Kwon, H. H., and Lee, S. O. (2020). Data Driven Water Surface Elevation Forecasting Model with Hybrid Activation Function-A Case Study for Hangang River, South Korea. Applied Sciences. 10(4): 1424. 10.3390/app10041424
23
Yoo, H. J., Lee, S. O., Choi, S., and Park, M. (2019). A Study on the Data Driven Neural Network Model for the Prediction of Time Series Data: Application of Water Surface Elevation Forecasting in Hangang River Bridge. Journal of Korean Society of Disaster and Security. 12(2): 73-82.
24
Zhang, J., Zhu, Y., Zhang, X., Ye, M., and Yang, J. (2018). Developing a Long Short-Term Memory (LSTM) based Model for Predicting Water Table Depth in Agricultural Areas. Journal of Hydrology. 561: 918-929. 10.1016/j.jhydrol.2018.04.065
25
Zhang, D., Lindholm, G., and Ratnaweera, H. (2018). Use Long Short-term Memory to Enhance Internet of Things for Combined Sewer Overflow Monitoring. Journal of Hydrology. 556: 409-418. 10.1016/j.jhydrol.2017.11.018

Korean References Translated from the English

1
국토교통부 (2016). 수자원장기종합계획(2001~2020) 제3차 수정계획. 세종: 국토교통부.
2
기상청 (2020). 한국 기후변화 평가보고서 2020-기후변화 과학적 근거-. 서울: 기상청.
3
여운기, 서영민, 이승윤, 지홍기 (2010). 인공신경망과 유전자알고리즘의 결합모형을 이용한 수위예측에 관한 연구. 한국수자원학회논문집. 43(8): 721-731. 10.3741/JKWRA.2010.43.8.721
4
오지환, 류경식, 복정수, 장연석, 배영대, 이봉국 (2019). 실제 물이용 체계를 고려한 한강 동해 권역 물수급 평가. 한국방재학회논문집. 19(7): 529-543.
5
유형주, 이승오, 최서혜, 박문형 (2019). 시계열 자료의 예측을 위한 자료 기반 신경망 모델에 관한 연구: 한강대교 수위예측 적용. 한국방재안전학회논문집. 12(2): 73-82.
6
이기하, 정성호, 이대업 (2018). 메콩강 유출모의를 위한 물리적 및 데이터 기반 모형의 비교·분석. 한국수자원학회논문집. 51(6): 503-514.
7
이승욱, 김영오, 이동률 (2005). 물수지 분석을 위한 불확실성 정량화. 한국수자원학회논문집. 38(4): 281-292. 10.3741/JKWRA.2005.38.4.281
8
이재영, 김현일, 한건영 (2020). 수문모형과 기계학습을 연계한 실시간 하천홍수 예측. 대한토목학회논문집. 40(3): 303-314.
9
최서혜, 권현한, 박문형 (2019). TDSVM을 이용한 하천수 취수량 예측. 한국수자원학회논문집. 52(12): 1075-1086.
10
최시중, 강성규, 이동률, 김중훈 (2018). 수자원 계획 수립을 위한 물 수급 전망 개선 방안 연구. 방재학회논문집. 18(7): 589-596.
11
트란 광 카이, 송사광 (2017). 딥러닝 기반 침수 수위 예측: 미국 텍사스 트리니티강 사례연구. 정보과학회 논문지. 44(6): 607-612. 10.5626/JOK.2017.44.6.607
Information
  • Publisher :Korean Society of Disaster and Security
  • Publisher(Ko) :한국방재안전학회
  • Journal Title :Journal of Korean Society of Disaster and Security
  • Journal Title(Ko) :한국방재안전학회 논문집
  • Volume : 13
  • No :4
  • Pages :75-92
  • Received Date : 2020-09-15
  • Revised Date : 2020-10-06
  • Accepted Date : 2020-10-24
  • DOI :https://doi.org/10.21729/ksds.2020.13.4.75
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