A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes
2016 (English)In: Environmental Modelling and Assessment, ISSN 1420-2026, E-ISSN 1573-2967, Vol. 21, no 2, 221-232 p.Article in journal (Refereed) Published
Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.
Place, publisher, year, edition, pages
2016. Vol. 21, no 2, 221-232 p.
Chlorophyll-a, Lake, Multiple regression, Genetic programming
Earth and Related Environmental Sciences Environmental Sciences
Research subject Earth Science with specialization in Environmental Analysis
IdentifiersURN: urn:nbn:se:uu:diva-263171DOI: 10.1007/s10666-015-9480-4ISI: 000371612600005OAI: oai:DiVA.org:uu-263171DiVA: diva2:857120