The speed and accuracy of conservation planning could be improved if maps indicating areas where grassed waterways should be placed to reduce erosion could be easily created. For five central Kentucky fields, elevation data were obtained with real time kinematic (RTK) global positioning system (GPS) and from US Geological Survey (USGS) digital elevation models (DEMs). Terrain attributes were calculated from these datasets which were used as predictor variables for neural network and logistic regression analyses. Grassed waterway prediction models were developed with these analyses. The type of activation function, type of standardization procedure, number of neurons, number of preliminary runs, and number of hidden layers had little impact on the results of the neural network analysis. Logistic regression and neural network analyses produced similar erosion prediction maps. The type of flow direction algorithm used to calculate terrain attributes did not change prediction maps substantially. Grassed waterways could be predicted in most cases with the RTK data but only in some cases with the USGS data. This modeling approach was robust and could aid conservation planners in identifying suitable areas for waterways more efficiently if accurate elevation data can be acquired.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_theses-1548 |
| Date | 01 January 2008 |
| Creators | Pike, Adam Clellon |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of Kentucky Master's Theses |
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