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.

Document Type

Conference Proceeding

DOI

https://doi.org/10.1109/CEC.2017.7969532

Keywords

Genetic algorithm, Model, Nitrate, Prediction

Publication Date

7-5-2017

Journal Title

2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings

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