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Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach
Author(s) -
Qian Song S.,
Reckhow Kenneth H.,
Zhai Jun,
McMahon Gerard
Publication year - 2005
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2005wr003986
Subject(s) - bayesian probability , computer science , water quality , streams , bayesian inference , hydrology (agriculture) , bayesian network , data mining , environmental science , engineering , machine learning , artificial intelligence , ecology , computer network , geotechnical engineering , biology
A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed.