지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.10
- 수록면
- 1 - 13 (13page)
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초록· 키워드
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In this study, water quality factors (water temperature, turbidity, T-P, T-N, Chl-a) in the downstream of the Kumho River were simulated through multiple regression analysis using remote sensing data of the Landsat 8 satellite. The approach for simulating water quality factors in Kumho River using Landsat 8 satellite data showed reliable results since the R values of turbidity, T-N, T-P, and Chl-a were 0.797, 0.877, 0.804, and 0.889, respectively, within the significance level (p) 0.01 for the calibration processes and those were 0.933, 0.663, 0.845 and 0.931 within the significance level (p) of 0.01, respectively, for the validation processes. The water temperature calculation using Band 10 of the TIRS sensor of the Landsat 8 satellite also showed stable results with an R value of 0.884 and an RMSE of 1.306 ℃. Overall, the approach for estimating water quality factors using satellite data was reliable, so water quality prediction using satellite data is considered to be a very useful method for water quality management of the Kumho River. As a result, the monitoring system using satellite data is expected to support for managing the water quality of the Kumho River.
목차
ABSTRACT
1. Introduction
2. Methods
3. Results and Discussion
4. Conclusions
Reference
참고문헌 (22)
참고문헌 신청
Chung SW, Jung Y, Ko IH, Kim N. Development and Validation of A Decision Support System for the Real-time Monitoring and Management of Reservoir Turbidity Flows: A Case Study for Daecheong Dam, J. Korea Water Resour. Assoc. 2008;41:293-303. DOI:10.3741/JK
Jung UJ. Study of Real Time Water Quality Monitoring and Modeling Systems, Report 13, Korea Environment Institute; 2009. p. 1-2.
Alilou H, Nia AM, Keshtkar H, Han D, Bray M. A cost-effective and efficient framework to determine water quality monitoring network locations. Sci.Total. Environ. 2018;624:283-293. https://doi.org/10.1016/j.scitotenv.2017.12.121.
Bae HK. The Modelling Approach for Predicting Coastal Pollutions using Rainfall Distributions over Different Land Use/Land Cover. J. Coast. Res. 2018;SI85:11-15. http://doi.org/10.2112/SI85-003.1.
Chandrika KC, Niranjana PT, Siva KRR, Hari KR. Applications of artificial neural network and Box-Behnken Design for modelling malachite green dye degradation from textile effluents using TiO2 photocatalyst. Environ. Eng. Res. 2022;27:200553. http://doi.or
Jung UJ. Study of Real Time Water Quality Monitoring and Modeling Systems, Report 13, Korea Environment Institute; 2009. p. 1-2.
Alilou H, Nia AM, Keshtkar H, Han D, Bray M. A cost-effective and efficient framework to determine water quality monitoring network locations. Sci.Total. Environ. 2018;624:283-293. https://doi.org/10.1016/j.scitotenv.2017.12.121.
Bae HK. The Modelling Approach for Predicting Coastal Pollutions using Rainfall Distributions over Different Land Use/Land Cover. J. Coast. Res. 2018;SI85:11-15. http://doi.org/10.2112/SI85-003.1.
Chandrika KC, Niranjana PT, Siva KRR, Hari KR. Applications of artificial neural network and Box-Behnken Design for modelling malachite green dye degradation from textile effluents using TiO2 photocatalyst. Environ. Eng. Res. 2022;27:200553. http://doi.or
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