Data collection strategy for building rainfall-runoff LSTM model predicting daily runoff

Dongkyun Kim; Seokkoo Kang

doi:10.3741/JKWRA.2021.54.10.795

All Issue

2021 Vol.54, Issue 10 Preview Page Next Page

Research Article

Data collection strategy for building rainfall-runoff LSTM model predicting daily runoff 강수-일유출량 추정 LSTM 모형의 구축을 위한 자료 수집 방안

31 October 2021. pp. 795-805

PDF XML

Abstract

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

Keywords

Long short-term memory

Rainfall-runoff model

Deep learning

Artificial neural network

Artificial intelligence

Flow discharge

Soyang river dam

Flow prediction

본 연구는 소양강댐 유역을 대상으로 LSTM 기반의 일유출량 추정 딥러닝 모형을 개발한 후, 모형구조 및 입력자료의 다양한 조합에 대한 모형의 정확도를 살폈다. 첫 12년(1997.1.1-2008.12.31) 동안의 유역평균 일강수량, 일기온, 일풍속 (이상 입력), 일평균 유량 (출력)으로 이루어진 데이터베이스를 기반으로 모형을 구축하였으며, 이후 12년(2009.1.1-2020.12.31) 동안의 자료를 사용하여 Nash-Sutcliffe Model Efficiency Coefficient (NSE)와 RMSE를 살폈다. 가장 높은 정확도를 보인 조합은 64개의 은닉유닛을 가진 LSTM 모형 구조에 가능한 모든 입력자료(12년치의 일강수량, 일기온, 일풍속)를 활용한 경우로서 검증기간의 NSE와 RMSE는 각각 0.862와 76.8 m³/s를 기록하였다. LSTM의 은닉유닛이500개를 초과하는 경우 과적합으로 인한 모형의 성능 저하가 나타나기 시작했으며, 1000개를 초과하는 경우 과적합 문제가 두드러졌다. 12년치의 일강수만 입력자료로 활용한 경우에도 매우 높은 성능(NSE=0.8~0.84)의 모형이 구축되었으며, 한 해의 자료만을 활용하여 학습한 경우에도 충분히 활용 가능한 정확도(NSE=0.63~0.85)를 가진 모형을 구축할 수 있었다. 특히 유량의 변동성이 큰 한 해의 자료만을 활용하여 모형을 학습한 경우 매우 높은 정확도(NSE=0.85)의 모형이 구축되었다. 학습자료가 중유량과 양극한의 유량을 모두 포함한 경우라면 5년 이상의 입력자료는 모형의 성능을 크게 개선시키지 못했다.

키워드

LSTM

강우-유출 모형

딥러닝

인공신경망

인공지능

유량

소양강댐

유량예측

References

Ahmad, W., and Kim, D. (2019). "Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea." International Journal of Applied Earth Observation and Geoinformation, Vol. 83, 101930. 10.1016/j.jag.2019.101930

Bai, Y., Bezak, N., Zeng, B., Li, C., Sapač, K., and Zhang, J. (2021). "Daily runoff forecasting using a cascade long short-term memory model that considers different variables." Water Resources Management, Vol. 35, No. 4, pp. 1167-1181. 10.1007/s11269-020-02759-2

Boulmaiz, T., Guermoui, M., and Boutaghane, H. (2020). "Impact of training data size on the LSTM performances for rainfall-runoff modeling." Modeling Earth Systems and Environment, Vol. 6, pp. 2153-2164. 10.1007/s40808-020-00830-w

Bowes, B.D., Sadler, J.M., Morsy, M.M., Behl, M., and Goodall, J.L. (2019). "Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks." Water, Vol. 11, No. 5, 1098. 10.3390/w11051098

Chen, Z., Zhu, Z., Jiang, H., and Sun, S. (2020). "Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods." Journal of Hydrology, Vol. 591, 125286. 10.1016/j.jhydrol.2020.125286

Fang, K., and Shen, C. (2020). "Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel." Journal of Hydrometeorology, Vol. 21, No. 3, pp. 399-413. 10.1175/JHM-D-19-0169.1

Fang, K., Kifer, D., Lawson, K., and Shen, C. (2020). "Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions." Water Resources Research, Vol. 56, No. 12, e2020WR028095. 10.1029/2020WR028095

Han, J., Olivera, F., and Kim, D. (2021a). "An algorithm of spatial composition of hourly rainfall fields for improved high rainfall value estimation." KSCE Journal of Civil Engineering, Vol. 25, No. 1, pp. 356-368. 10.1007/s12205-020-0526-z

Han, H., Choi, C., Jung, J., and Kim, H.S. (2021b). "Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow." Journal of Korea Water Resources Association, Vol. 54, No. 3, pp. 157-166.

Hochreiter, S., and Schmidhuber, J. (1997). "Long short-term memory." Neural Computation, Vol. 9, No. 8, pp. 1735-1780. 10.1162/neco.1997.9.8.17359377276

Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., Oki, R., Nakamura, K., and Iguchi, T. (2014). "The global precipitation measurement mission." Bulletin of the American Meteorological Society, Vol. 95, No. 5, pp. 701-722. 10.1175/BAMS-D-13-00164.1

Hu, C., Wu, Q., Li, H., Jian, S., Li, N., and Lou, Z. (2018). "Deep learning with a long short-term memory networks approach for rainfall-runoff simulation." Water, Vol. 10, No. 11, 1543. 10.3390/w10111543

Idrees, M., Jehanzaib, M., Kim, D., Kim, T. (2021a). "Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir." Stochastic Environmental Research and Risk Assessment, Vol. 35, pp. 1805-1823. doi: 10.1007/s00477-021-01982-6. 10.1007/s00477-021-01982-6

Idrees, B.M., Lee, J.-Y., Kim, D., Kim., T.-W. (2021b) "Complementary modeling approach for estimating sedimentation and hydraulic flushing parameters using artificial neural networks and RESCON₂ Model." KSCE Journal of Civil Engineering, Vol. 25, pp. 3766-3778. doi: 10.1007/s12205-021-1877-9 10.1007/s12205-021-1877-9

Jehanzaib, M., Idrees, M.B., Kim, D., and Kim, T. (2021). "Comprehensive evaluation of machine learning techniques for hydrological drought forecasting." Journal of Irrigation and Drainage Engineering, Vol. 147, No. 7. doi: 10.1061/(ASCE) IR.1943-4774.0001575 10.1061/(ASCE)IR.1943-4774.0001575

Jung, S., Cho, H., Kim, J., and Lee, G. (2018). "Prediction of water level in a tidal river using a deep-learning based LSTM model." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1207-1216.

Jung, Y., Kim, D., Kim, D., Kim, M., and Lee, S.O. (2014). "Simplified flood inundation mapping based on flood elevation-discharge rating curves using satellite images in gauged watersheds." Water, Vol. 6, No. 5, pp. 1280-1299. 10.3390/w6051280

Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M. (2018). "Rainfall-unoff modelling using long short-term memory (LSTM) networks." Hydrology and Earth System Sciences, Vol. 22, No. 11, pp. 6005-6022. 10.5194/hess-22-6005-2018

Lee, G., Kim, D., Kwon, H., and Choi, E. (2019). "Estimation of maximum daily fresh snow accumulation using an artificial neural network model." Advances in Meteorology, Vol. 2019, 2709351. doi: 10.1155/2019/2709351 10.1155/2019/2709351

Lee, Y.O., Jo, J., and Hwang, J. (2017). "Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection." 2017 IEEE International Conference on big data (big data), IEEE, Boston, MA, U.S., pp. 3248-3253. doi: 10.1109/BigData.2017.8258307 10.1109/BigData.2017.8258307

Park, C., and Chung, I.M. (2020). "Evaluating the groundwater prediction using LSTM model." Journal of Korea Water Resources Association, Vol. 53, No. 4, pp. 273-283.

Seo, M., Kim, D., Ahmad, W., and Cha, J.H. (2018). "Estimation of stream flow discharge using the satellite synthetic aperture radar images at the mid to small size streams." Journal of Korea Water Resources Association, Vol. 51, No. 12, pp. 1181-1194.

Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015). "Convolutional LSTM network: A machine learning approach for precipitation nowcasting." Advances in Neural Information Processing Systems, pp. 802-810.

Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2017). "Deep learning for precipitation nowcasting: A benchmark and a new model." arXiv Preprint, arXiv:1706.3458.

Silva, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T. and Hassabis, D. (2017). "Mastering the game of go without human knowledge." Nature, Vol. 550, No. 7676, pp. 354-359. 10.1038/nature2427029052630

Sit, M., Demiray, B.Z., Xiang, Z., Ewing, G.J., Sermet, Y., and Demir, I. (2020). "A comprehensive review of deep learning applications in hydrology and water resources." Water Science and Technology, Vol. 82, No. 12, pp. 2635-2670. 10.2166/wst.2020.36933341760

Tomlinson, C.J., Chapman, L., Thornes, J.E., and Baker, C. (2011). "Remote sensing land surface temperature for meteorology and climatology: A review." Meteorological Applications, Vol. 18, No. 3, pp. 296-306. 10.1002/met.287

Yin, J., Deng, Z., Ines, A.V., Wu, J., and Rasu, E. (2020). "Forecast of short-erm daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)." Agricultural Water Management, Vol. 242, 106386. 10.1016/j.agwat.2020.106386

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 54
No :10
Pages :795-805
Received Date : 2021-07-12
Revised Date : 2021-08-05
Accepted Date : 2021-08-05
DOI :https://doi.org/10.3741/JKWRA.2021.54.10.795

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

All Issue