Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

Ryu, Jichul; Jang, Won Sik; Kim, Jonggun; Choi, Joong Dae; Engel, Bernard A.; Yang, Jae E.; Lim, Kyoung Jae

doi:10.3390/w8040153

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…Generally, various hydrological models, such as the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program-Fortran (HSPF), and the watershed-scale Long-Term Hydrologic Impact Assessment Model (watershed-scale L-THIA), have been developed and utilized to predict river discharge and dam inflow [5][6][7]. The hydrological model is one of the most popular methods to predict water cycle components by reflecting the physical mechanisms of the hydrological process in the watersheds.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Hong

Lee

Bae

et al. 2020

Water

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Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Hong

Lee

Bae

et al. 2020

Water

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“…In this study, the watershed-scale L-THIA ACN model, developed by Ryu et al [27], was enhanced by developing a field pollutant load estimation module and incorporating the instream QUAL2E model, in order to simulate pollutant loads at a watershed scale. Comparison of the simulated results of this L-THIA ACN-WQ model with observed values from two watersheds in These results show that the watershed-scale L-THIA ACN-WQ model can simulate not only streamflow but also pollutant loads at a watershed with less input data required than for complex watershed-scale models.…”

Section: Discussionmentioning

confidence: 99%

Development of Field Pollutant Load Estimation Module and Linkage of QUAL2E with Watershed-Scale L-THIA ACN Model

Ryu

Jang

Kim

et al. 2016

Water

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Abstract:The Long Term Hydrologic Impact Assessment (L-THIA) model was previously improved by incorporating direct runoff lag time and baseflow. However, the improved model, called the L-THIA asymptotic curve number (ACN) model cannot simulate pollutant loads from a watershed or instream water quality. In this study, a module for calculating pollutant loads from fields and through stream networks was developed, and the L-THIA ACN model was combined with the QUAL2E model (The enhanced stream water quality model) to predict instream water quality at a watershed scale. The new model (L-THIA ACN-WQ) was applied to two watersheds within the Korean total maximum daily loads management system. To evaluate the model, simulated results of total nitrogen (TN) and total phosphorus (TP) were compared with observed water quality data collected at eight-day intervals. Between simulated and observed data for TN pollutant loads in Dalcheon A watershed, the R 2 and Nash-Sutcliffe efficiency (NSE) were 0.81 and 0.79, respectively, and those for TP were 0.79 and 0.78, respectively. In the Pyungchang A watershed, the R 2 and NSE were 0.66 and 0.64, respectively, for TN and both statistics were 0.66 for TP, indicating that model performed satisfactorily for both watersheds. Thus, the L-THIA ACN-WQ model can accurately simulate streamflow, instream pollutant loads, and water quality.

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“…Of the 802 responses, a majority of the respondents (78%) believed that the conservation of cultural and religious values of temples is important or very important. Respondents' environmentally responsible behaviors in national parks (ERB) were asked using 12 five-point scale items developed by Ryu et al [46] and the results of exploratory factor analysis (EFA) confirmed a single factor. The variable of ERB was created based on the aggregation of these items.…”

Section: Profiles Of the Respondents' Perception And Behaviorsmentioning

confidence: 99%

Economic Valuation of Conservation of Inholdings in Protected Areas for the Institution of Payments for Ecosystem Services

Lee

Kim

2019

Forests

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Ecosystem services, as public goods, are often undersupplied because private markets do not fully take into account the social cost of production. To alleviate the concern about this imbalance situation, payments for ecosystem services (PES) have emerged as a preferable alternative. While temples in Korea have owned a considerable part of the national parks, a PES approach can be used as a viable option to alleviate the conflicts among visitors, non-visitors, and temples. The purpose of this paper is to assess the economic values of ecosystem services provided by temple forests as a compensation mechanism. Using a contingent valuation method, an online survey was conducted with 1000 respondents. Study results showed that the economic benefits of the conservation of temple forests were estimated to be substantial, ranging from ₩5980 (US $5.42) to ₩7709 ($7.08) per household per year. The results also confirmed the effects of social factors such as individuals’ trust in the government’s environmental policies and importance on the conservation of temples’ cultural and religious values on the willingness to pay. With a growing interest in securing ecosystem services through a PES approach, estimating economic benefits of the conservation of inholdings in public protected areas will be a valuable piece of information as an important policy decision-making tool.

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Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

Cited by 19 publications

References 43 publications

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Development of Field Pollutant Load Estimation Module and Linkage of QUAL2E with Watershed-Scale L-THIA ACN Model

Economic Valuation of Conservation of Inholdings in Protected Areas for the Institution of Payments for Ecosystem Services

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