Predicting water quality under uncertainty using stochastic modeling techniques

• Main Author: Novem, Lavanya.
• Other Authors: School of Civil and Environmental Engineering
• Format: Final Year Project
• Language: English
• Published: 2010
• Subjects: DRNTU::Engineering::Civil engineering::Water resources
• Online Access: http://hdl.handle.net/10356/38908
• Institution: Nanyang Technological University

Water quality affects our lifestyles in several ways. As such, water quality modeling becomes essential for human survival. They allow decision makers to make informed decisions about their assets, water resources and quality management issues. In water quality modeling, a certain level of inaccuracy can be anticipated due to space and time dimensions, the random nature of the system behaviors and other reasons. As such, it is essential for decision-makers to assess the amount of uncertainty or risk associated with a model. Often, stochastic models are chosen for uncertainty analysis. Monte Carlo simulation is a popular stochastic modeling technique which utilizes random number generation to produce results via repeated stimulations. Given the success of Monte Carlo simulation for water quality modeling, it was used in a case study to evaluate the water quality at the Changsha section of Xiangjiang River. For this report, a one dimensional water quality model, the Streeter Phelps model, was employed to model predict the biochemical oxygen demand and dissolved oxygen levels at various segments of this river. Predicted values provided basis for assessing the water quality of the river and the risk it poses of exceeding national water quality requirements. Sensitivity analysis was also conducted to evaluate the impact of water quality parameters, such as discharge rate, deoxygenation rate constant and reaeration rate constant, on the final water quality of the river. These assessments enabled solutions and recommendations to improve water qualities at the river.