Parameter estimation of nonlinear nitrate prediction model using genetic algorithm

Abstract

We attack the problem of predicting nitrate concentrations in a stream by using a genetic algorithm to minimize the difference between observed and predicted concentrations on hydrologic nitrate concentration model based on a US Geological Survey collected data set. Nitrate plays a significant role in maintaining ecological balance in aquatic ecosystems and any advances in nitrate prediction accuracy will improve our understanding of the non-linear interplay between the factors that impact aquatic ecosystem health. We compare the genetic algorithm tuned model against the LOADEST estimation tool in current use by hydrologists, and against a random forest, generalized linear regression, decision tree, and gradient booted tree and show that the genetic algorithm does statistically significantly better. These results indicate that genetic algorithms are a viable approach to tuning such non-linear, hydrologic models.