Parameter estimation of nonlinear nitrate prediction model using genetic algorithm
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.
Genetic algorithm, Model, Nitrate, Prediction
Wu, Rui, Jose T. Painumkal, John M. Volk, Siming Liu, Sushil J. Louis, Scott Tyler, Sergiu M. Dascalu, and Frederick C. Harris. "Parameter estimation of nonlinear nitrate prediction model using genetic algorithm." In 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1893-1899. IEEE, 2017.
2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings