Nonlinear stochastic modeling of river dissolved-oxygen

Thesis/Dissertation · Sun Jan 01 04:00:00 EST 1984

OSTI ID:6898554

Tabios, III, G Q

An important aspect of water quality modeling is forecasting water quality variables for real-time management and control applications to enhance, maintain and sustain desirable water qualities. The major objective of this research is to develop daily time series models for forecasting river dissolved-oxygen (DO). The modeling approach adopted herein combines deterministic and stochastic concepts for determining properties of the DO process based on time series data and dynamic mechanisms governing the said process. This is accomplished by deriving a general DO stochastic model structure based on a modified Streeter-Phelps DO-BOD dynamic model. Then some types of nonlinear models namely, self-exciting threshold autoregressive-moving average (SETARMA), amplitude-dependent autoregressive (ADAR) and bilinear (BL) models, and the class of linear autoregressive-moving average (ARMA) models are adopted for model identification and parameter estimation. Six stream-water quality gaging stations located in the eastern portion of the continental U.S.A. are utilized in this study. Various orders of ARMA, SETARMA, ADAR and BL models are fitted to the data. Results obtained indicated that ADAR and BL models are superior for one-step ahead forecasts, while SETARMA models are better for forecasts of longer lead times. In general, the SETARMA, ADAR and BL models show promise as alternative models for river DO time series modeling and forecasting with unique advantages in each.

• Research Organization: Colorado State Univ., Fort Collins, CO (USA)
• OSTI ID: 6898554
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
540310* -- Environment
Aquatic-- Basic Studies-- (1990-)
DISSOLVED GASES
ELEMENTS
ENVIRONMENTAL QUALITY
FLUIDS
FORECASTING
GASES
MATHEMATICAL MODELS
MATHEMATICS
NONMETALS
OXYGEN
RIVERS
SOLUTES
STOCHASTIC PROCESSES
STREAMS
SURFACE WATERS
TIME-SERIES ANALYSIS
WATER QUALITY