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.
이용수
초록· 키워드
오류제보하기
For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen (NH3-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream NH3-N control.
In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted NH3-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the NH3-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and NH3-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.
목차
등록된 정보가 없습니다.
참고문헌 (26)
참고문헌 신청
Ambrose R.B. / 1993 / The Water Quality Analysis Simulation Program, WASP5: Model Documentation / Environmental Research Laboratory
B. Bhattacharya / 2002 / Application of artificial neural networks and M5 model trees to modelling stage-discharge relationship / Proceedings of the Second International Symposium
B. Bhattacharya / 2003 / Neural networks and M5 model trees in modelling water leveld ischarge relationship for an Indian river / Proceedings of the 11th European Symposium on Art
Brown L.and Barnwell T. / 1987 / The Enhanced Stream Water Quality Models QUAL2E and QUAL2E-UNCAS: Documentation and User’s Manual
Chung S. W / 2002 / Development of artificial neural network models supporting reservoir operation for the control of downstream water quality / Wat.Engr.Res.,Korea Water Re
B. Bhattacharya / 2002 / Application of artificial neural networks and M5 model trees to modelling stage-discharge relationship / Proceedings of the Second International Symposium
B. Bhattacharya / 2003 / Neural networks and M5 model trees in modelling water leveld ischarge relationship for an Indian river / Proceedings of the 11th European Symposium on Art
Brown L.and Barnwell T. / 1987 / The Enhanced Stream Water Quality Models QUAL2E and QUAL2E-UNCAS: Documentation and User’s Manual
Chung S. W / 2002 / Development of artificial neural network models supporting reservoir operation for the control of downstream water quality / Wat.Engr.Res.,Korea Water Re
이 논문의 저자 정보
최근 본 자료
댓글(0)
0