AI SearchRiver water quality modeling under a stochastic environment
Call Number: AIT RSPR no. IE-86-20 Material type:
SeriesSeries: Asian Institute of Technology. Research studies project report ; no. IE-86-20Publication details: Bangkok : Asian Institute of Technology, 1986Description: 53, C-16 pSubject(s): Online resources: Dissertation note: Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 1986 Summary: A stochastic programming model for river water quality management has been developed. The proposed model recognizes explicitly the stochastic phenomena of streamflow and wastewater flowrates. Furthermore, the uncertainties present in physical and chemical parameters (i.e., reaeration constant, time of flow ) in a water body due to the stochastic flows are taken into account by the model. A chance cons trained programming technique is employed in this study because of its advantage in the replacement of probabilistic constraints with their deterministic equivalents. A simulation model is used together with an optimization model in order to characterize uncertain elements and to predict water quality at any point in a river basin.
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22-AIT Thesis (Replacement)
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40-Archives
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Asian Institute of Technology Library Archives | AIT RSPR no. IE-86-20 (Browse shelf(Opens below)) | Available | 30050160109442 |
A research study report submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Engineering and Technology
Research Studies Project Report (M. Eng.) - Asian Institute of Technology, 1986
A stochastic programming model for river water quality management has been developed. The proposed model recognizes explicitly the stochastic phenomena of streamflow and wastewater flowrates. Furthermore, the uncertainties present in physical and chemical parameters (i.e., reaeration constant, time of flow ) in a water body due to the stochastic flows are taken into account by the model. A chance cons trained programming technique is employed in this study because of its advantage in the replacement of probabilistic constraints with their deterministic equivalents. A simulation model is used together with an optimization model in order to characterize uncertain elements and to predict water quality at any point in a river basin.
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