Keywords
pear, multi-objective optimization, model calibration, swat, sce
Start Date
1-7-2010 12:00 AM
Abstract
Recent work involving attempts to calibrate the Soil and Water Assessment Tool (SWAT) non-point source model has led to application of machine learning techniques (specifically the Apriori algorithm) to test runs of the model for particular watersheds. A new Parsimonious Explicit Apriori Reduction (PEAR) method for model calibration is evinced, and details of experiments demonstrating the improved efficiency and accuracy, as opposed to both the manual approach and a well-known Genetic Algorithm (GA), are outlined. The PEAR method overcomes difficulties intrinsic to three current classes of multi-objective optimization.
A Novel Model Calibration Technique Through Application of Machine Learning Association Rules
Recent work involving attempts to calibrate the Soil and Water Assessment Tool (SWAT) non-point source model has led to application of machine learning techniques (specifically the Apriori algorithm) to test runs of the model for particular watersheds. A new Parsimonious Explicit Apriori Reduction (PEAR) method for model calibration is evinced, and details of experiments demonstrating the improved efficiency and accuracy, as opposed to both the manual approach and a well-known Genetic Algorithm (GA), are outlined. The PEAR method overcomes difficulties intrinsic to three current classes of multi-objective optimization.
