[논문]LSTM Networks 딥러닝 기법과 SWAT을 이용한 유량지속곡선 도출 및 평가

최정렬; 안성욱; 최진영; 김병식

doi:10.3741/jkwra.2021.54.s-1.1107

LSTM Networks 딥러닝 기법과 SWAT을 이용한 유량지속곡선 도출 및 평가
A study on the derivation and evaluation of flow duration curve (FDC) using deep learning with a long short-term memory (LSTM) networks and soil water assessment tool (SWAT) 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.12 suppl., 2021년, pp.1107 - 1118

최정렬 (강원대학교 도시환경재난관리전공) , 안성욱 (강원대학교 도시환경재난관리전공) , 최진영 (한국환경공단 환경인증검사처) , 김병식 (강원대학교 도시환경재난관리전공)

초록
AI-Helper

지구온난화로 인해 발생한 기후변화는 한반도의 홍수, 가뭄 등의 발생빈도를 증가시켰으며, 이로 인해 인적, 물적 피해가 증가한 것으로 나타났다. 수재해 대비 및 대응을 위해서는 국가 차원의 수자원 관리 계획 수립이 필요하며, 유역 단위 수자원 관리를 위해서는 장기간 관측된 유량 자료를 이용하여 도출된 유량지속곡선이 필요하다. 전통적으로 수자원 분야에서 유량지속곡선을 도출하기 위하여 물리적 기반의 강우-유출 모형이 많이 사용되고 있으며, 최근에는 데이터 기반의 딥러닝 기법을 이용한 유출량 예측 기법에 관한 연구가 진행된 바 있다. 물리적 기반의 모형은 수문학적으로 신뢰도 높은 결과를 도출할 수 있으나, 사용자의 높은 이해도가 요구되며, 모형 구동 시간이 오래 걸릴 수 있는 단점이 있다. 데이터 기반의 딥러닝 기법의 경우 입력 자료가 간단하며, 모형 구동 시간이 비교적 짧으나 입력 및 출력자료 간의 관계가 블랙박스로 처리되어 수리·수문학적 특성을 반영할 수 없는 단점이 있다. 본 연구에서는 물리적 기반 모형으로 국내외에서 적용성이 검증된 Soil Water Assessment Tool (SWAT)의 매개변수 보정(Calibration)을 통해 장기간의 결측치 없는 데이터를 산출하고, 이를 데이터 기반 딥러닝 기법인 Long Short-term Memory (LSTM)의 훈련(Training) 데이터로 활용하였다. 시계열 데이터 분석 결과 검·보정 전체 기간('07-'18) 동안 Nash-Sutcliffe Efficiency (NSE)와 적합도 비교를 위한 결정계수는 각각 0.04, 0.03 높게 도출되어 모형에서 도출된 SWAT의 결과가 LSTM보다 전반적으로 우수한 것으로 나타났다. 또한, 모형에서 도출된 연도별 시계열 자료를 내림차순하여 산정된 유량지속곡선과 관측유량 기반의 유량지속곡선과 비교한 결과 NSE는 SWAT과 LSTM 각각 0.95, 0.91로 나타났으며, 결정계수는 0.96, 0.92로 두 모형 모두 우수한 성능을 보였다. LSTM 모형의 경우 저유량 부분 모의의 정확도 개선이 필요하나, 방대한 입력 자료로 인해 모형 구축 및 구동 시간이 오래 걸리는 대유역과 입력 자료가 부족한 미계측 유역의 유량지속곡선 산정 등에 활용성이 높을 것으로 판단된다.

Abstract ▼ AI-Helper

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

주제어

표/그림 (12)

그림 Fig. 1. flowchart of the study
그림 Fig. 2. LSTM Structure (Kratzert et al., 2018)
그림 Fig. 3. Location map of study watershed
그림 Fig. 4. Topographic data of the SWAT (a): Digital Elevation Model (DEM, Cellsize = 5 m), (b): Landuse map, (C): Soil map and (d): Hydrologic Response Unit (HRU, n = 2,274)
그림 Fig. 5. Time-series plot derived by applying the calibrated parameters of the SWAT
표 Table 1. Selected value for explanation and calibration of SWAT model parameters
그림 Fig. 6. Time-series plot derived by applying the calibrated parameters of the LSTM
그림 Fig. 7. Scatter plot derived by applying the calibrated parameters of the SWAT (a), and LSTM (b), validation results of the SWAT (c) and LSTM (d), complete duration of SWAT (e) and LSTM (f)
그림 Fig. 8. Flow duration curve (FDC) using SWAT and LSTM results (a), SWAT vs observed FDC QQ plot (b), LSTM vs observed FDC (c)
표 Table 2. Statistical index for SWAT & LSTM results (Discharge)
표 Table 3. Flow value for exceedance probability using SWAT and LSTM
표 Table 4. Statistical index for SWAT & LSTM results (Flow Duration Curve)

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