Using Regression Tree Analysis to Improve Predictions of Low-Flow Nitrate and Chloride in Willamette River Basin Watersheds |
| |
Authors: | Cara J Poor Jeffrey L Ullman |
| |
Institution: | (1) Department of Civil and Environmental Engineering, Washington State University, 118 Sloan Hall, Pullman, WA 99164-2910, USA;(2) Department of Biological Systems Engineering, Washington State University, 202 LJ Smith Hall, Pullman, WA 99164-6120, USA |
| |
Abstract: | The use of regression tree analysis is examined as a tool to evaluate hydrologic and land use factors that affect nitrate
and chloride stream concentrations during low-flow conditions. Although this data mining technique has been used to assess
a range of ecological parameters, it has not previously been used for stream water quality analysis. Regression tree analysis
was conducted on nitrate and chloride data from 71 watersheds in the Willamette River Basin to determine whether this method
provides a greater predictive ability compared to standard multiple linear regression, and to elucidate the potential roles
of controlling mechanisms. Metrics used in the models included a variety of watershed-scale landscape indices and land use
classifications. Regression tree analysis significantly enhanced model accuracy over multiple linear regression, increasing
nitrate R
2 values from 0.38 to 0.75 and chloride R
2 values from 0.64 to 0.85 and as indicated by the ΔAIC value. These improvements are primarily attributed to the ability for
regression trees to more effectively handle interactions and manage non-linear functions associated with watershed heterogeneity
within the basin. Whereas hydrologic factors governed the conservative chloride tracer in the model, land use dominated control
of nitrate concentrations. Watersheds containing higher agricultural activity did not necessarily yield high nitrate concentrations,
but agricultural areas combined with either small proportions of forested land or greater urbanization generated nitrate levels
far exceeding water quality standards. Although further refinements are recommended, we conclude that regression tree analysis
presents water resource managers a promising tool that improves on the predictive ability of standard statistical methods,
provides insight into controlling mechanisms, and helps identify catchment characteristics associated with water quality impairment. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|