Natural resource data are typically non-linear and complex, yet
modeling methods often utilize statistical analysis techniques, such as
regression, that are insufficient for use on such data. This research proposes
an innovative modeling method based on pattern recognition techniques
borrowed from the field of machine learning. These techniques make no data
distribution assumptions, can fit non-linear data, can be effective on a small
data set, and can be weighted to include relative costs of different predictive
errors.
Rapid Assessment Protocols (RAPs) are commonly used to collect,
analyze, and interpret stream data to assist diverse management decisions. A
modeling method was developed to predict the outcome of a RAP in an effort
to improve accurate prediction, weighted for cost-effectiveness and safety,
while prioritizing investigations and improving monitoring. This method was
developed using channel stability data collected from 58 high-elevation
streams in the Upper Colorado River Basin. The purpose of the research was
to understand the relationships of channel stability to several
hydrobiogeomorphic features, easily derived from paper or electronic maps, in
an effort to predict channel stability. Given that the RAP used was developed
to evaluate channel stability, the research determined: 1) relationships
between channel stability and major land-use and hydrobiogeomorphic
features, and 2) if a predictive model could be developed to aid in identifying
unstable channel reaches while minimizing costs, for the purpose of land
management.
This research used Pearson's and chi-squared correlations to
determine associative relationships between channel stability and major land-use
and hydrobiogeomorphic features. The results of the Pearson's
correlations were used to build and test classification models using randomly
selected training and test sets. The modeling techniques assessed were
regression, single decision trees, and bagged (bootstrap aggregated) decision
trees. A cost analysis / prediction (CAP) model was developed to incorporate
cost-effectiveness and safety into the models. The models were compared
based on their 1) performance and 2) operational advantages and
disadvantages. A reliable predictive model was developed by integrating a
CAP model, receiving operator characteristic curves, and bagged decision
trees. This system can be used in conjunction with a GIS to produce maps to
guide field investigations. / Graduation date: 2001
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/32595 |
| Date | 16 March 2001 |
| Creators | Mor��t, Stephanie L. |
| Contributors | Klingeman, Peter C. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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