Assessing the Effect of Upstream Dam Outflows and River Water Uses on the Inflows to the Paldang Dam

Kim, Chul Gyum; Kim, Nam Won; Lee, Jeong Eun

doi:10.3741/jkwra.2014.47.11.1017

Journal of Korea Water Resources Association

2014

DOI: 10.3741/jkwra.2014.47.11.1017

|View full text |Cite

Assessing the Effect of Upstream Dam Outflows and River Water Uses on the Inflows to the Paldang Dam

Chul Gyum Kim¹,

Nam Won Kim²,

Jeong Eun Lee³

Abstract: This resulted an increase of low-flow at the Paldang Dam, the end of Pukhangang, and the Yangpyeong stage station by 100.57 m³/s, 33.01 m³/s, and 49.66 m³/s, respectively. Whereas, the impact of river water use was hardly found in the Pukhangang, and also was not significant in the (Nam)hangang. Therefore, the effect of small dam such as the Hoengseong Dam or river water use would be able be excluded for long-term runoff analysis. But, in the case of the areas with a large amount of water use, a sufficient inf… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 7 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Selection of Input Factors and Comparison of Machine Learning Models for Prediction of Dissolved Oxygen in Gyeongan Stream

Kim¹,

Byeon²,

Kim³

et al. 2021

J Korean Soc Environ Eng

View full text Add to dashboard Cite

Objectives : In this study, we select input factors for machine learning models to predict dissolved oxygen (DO) in Gyeongan Stream and compare results of performance evaluation indicators to find the optimal model.Methods : The water quality data from the specific points of Gyeongan Stream were collected between January 15, 1998 and December 30, 2019. The pretreatment data were divided into train and test data with the ratio of 7:3. We used random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU) among machine learning. RF and ANN were tested by both random split and time series data, while CNN and GRU conducted the experiment using only time series data. Performance evaluation indicators such as square of the correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to compare the optimal results for the models.Results and Discussion : Based on the RF variable importance results and references, water temperature, pH, electrical conductivity, PO4-P, NH4-N, total phosphorus, suspended solids, and NO3-N were used as input factors. Both RF and ANN performed better with time series data than random split. The model performance was good in order of RF > CNN > GRU > ANN.Conclusions : The eight input factors (water temperature, pH, electrical conductivity, PO4-P, NH4-N, total phosphorus, suspended solids, and NO3-N) were selected for machine learning models to predict DO in Gyeongan Stream. The best model for DO prediction was the RF model with time series data. Therefore, we suggest that the RF with the eight input factors could be used to predict the DO in streams.

show abstract

Selection of Input Factors and Comparison of Machine Learning Models for Prediction of Dissolved Oxygen in Gyeongan Stream

Kim¹,

Byeon²,

Kim³

et al. 2021

J Korean Soc Environ Eng

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Assessing the Effect of Upstream Dam Outflows and River Water Uses on the Inflows to the Paldang Dam

Cited by 1 publication

References 7 publications

Selection of Input Factors and Comparison of Machine Learning Models for Prediction of Dissolved Oxygen in Gyeongan Stream

Selection of Input Factors and Comparison of Machine Learning Models for Prediction of Dissolved Oxygen in Gyeongan Stream

Contact Info

Product

Resources

About