[논문]강수-일유출량 추정 LSTM 모형의 구축을 위한 자료 수집 방안

김동균; 강석구

doi:10.3741/jkwra.2021.54.10.795

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

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.10, 2021년, pp.795 - 805

김동균 (홍익대학교 건설환경공학과) , 강석구 (한양대학교 건설환경공학과)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (13)

그림 Fig. 1. Study area: Soyang River watershed located at the north-eastern part of South Korea
그림 Fig. 2. Input and output data used to develop the inflow estimation deep learning model of this study. (a) Daily inflow (output), (b) Daily rainfall (input), (c) Daily average temperature (input), (d) Daily average wind speed (input)
그림 Fig. 3. Schematic of the deep learning model developed in this study. (a) Network diagram of the deep learning blocks. (b) LSTM block composed of multiple hidden blocks (source: https://towardsdatascience.com/animated-rnn-lstm-and-gru-ef124d06cf45, obtained on 2021.6.27)
그림 Fig. 4. Relative RMSE value between the observed and the simulated inflow improving as the training epoch increases
표 Table 1. The RMSE and the N-S coefficient of the deep learning model for the training and validation period
그림 Fig. 5. Time series of observed vs. simulated flow at Soyang Lake shown in (a) linear and (b) log scale. The shaded area is the calibration period. Scatter plot of observed (x) vs. simulated (y) flow shown in (c) linear and (d) log scale
그림 Fig. 6. Root mean square error (RMSE) and Nash-Sutcliffe Model Efficiency Coefficient (NSE) of the flood prediction model varying with the number of the hidden units of the deep learning model. The result for the calibration period (a, c) and the validation period (b, d) are shown separately
표 Table 2. The scenarios of the training data used to investigate the sensitivity of the deep learning model accuracy to the training data wetness and dryness
그림 Fig. 8. Root mean square error (RMSE) and Nash-Sutcliffe Model Efficiency Coefficient (NSE) of the flood prediction model varying with training data wetness scenarios (Table 1). The result for the calibration period (a, c) and the validation period (b, d) are shown separately
그림 Fig. 7. (a) Root mean square error (RMSE) and (b) Nash-Sutcliffe Model Efficiency Coefficient (NSE) of the flow prediction model varying with the L-N scenarios (Table 1)
그림 Fig. 9. Relationship between the standard deviation of the flow discharge and the mean accuracy of the input data scenarios shown in Table 1
그림 Fig. 10. Root mean square error (RMSE) and Nash-Sutcliffe Model Efficiency Coefficient (NSE) of the flood prediction model varying with various combinations of input variables (Table 2). The result for the calibration period (a, c) and the validation period (b, d) are shown separately
표 Table 3. The scenarios of the training data used to investigate the sensitivity of the deep learning model accuracy to input data type

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