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A COMPARATIVE STUDY OF LINEAR AND NONLINEAR TIME SERIES MODELS FOR WATER QUALITY 1
Author(s) -
Jian Xiaodong,
Yu YunSheng
Publication year - 1998
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1998.tb00962.x
Subject(s) - water quality , environmental science , eutrophication , hydrology (agriculture) , drainage basin , autoregressive model , series (stratigraphy) , phosphorus , time series , nonlinear system , aquatic ecosystem , surface water , variance (accounting) , nutrient , water resource management , statistics , mathematics , environmental engineering , ecology , geography , environmental chemistry , business , engineering , chemistry , geology , paleontology , accounting , biology , quantum mechanics , physics , geotechnical engineering , cartography , organic chemistry
Surface water quality data are routinely collected in river basins by state or federal agencies. The observed quality of river water generally reflects the overall quality of the ecosystem of the river basin. Advanced statistical methods are often needed to extract valuable information from the vast amount of data for developing management strategies. Among the measured water quality constituents, total phosphorus is most often the limiting nutrient in freshwater aquatic systems. Relatively low concentrations of phosphorus in surface waters may create eutrophication problems. Phosphorus is a non‐conservative constituent. Its time series generally exhibits nonlinear behavior. Linear models are shown to be inadequate. This paper presents a nonlinear state‐dependent model for the phosphorous data collected at DeSoto, Kansas. The nonlinear model gives significant reductions in error variance and forecasting error as compared to the best linear autoregressive model identified.