uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. (Miljöanalys)
Geosigma, Vattholmav 8, S-75108 Uppsala, Sweden.
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
Abstract [en]

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.
Keyword [en]
Chlorophyll-a, Lake, Multiple regression, Genetic programming
National Category
Earth and Related Environmental Sciences Environmental Sciences
Research subject
Earth Science with specialization in Environmental Analysis
URN: urn:nbn:se:uu:diva-263171DOI: 10.1007/s10666-015-9480-4ISI: 000371612600005OAI: oai:DiVA.org:uu-263171DiVA: diva2:857120
Available from: 2015-09-28 Created: 2015-09-28 Last updated: 2016-04-13Bibliographically approved
In thesis
1. Predictions Within and Across Aquatic Systems using Statistical Methods and Models
Open this publication in new window or tab >>Predictions Within and Across Aquatic Systems using Statistical Methods and Models
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Prediktioner inom och mellan akvatiska system med statistiska metoder och modeller
Abstract [en]

Aquatic ecosystems are an essential source for life and, in many regions, are exploited to a degree which deteriorates their ecological status. Today, more than 50 % of the European lakes suffer from an ecological status which is unsatisfactory. Many of these lakes require abatement actions to improve their status, and mathematical models have a great potential to predict and evaluate different abatement actions and their outcome. Several statistical methods and models exist which can be used for these purposes; however, many of the models are not constructed using a sufficient amount or quality of data, are too complex to be used by most managers, or are too site specific. Therefore, the main aim of this thesis was to present different statistical methods and models which are easy to use by managers, are general, and provide insights for the development of similar methods and models.

To reach the main aim of the thesis several different statistical and modelling procedures were investigated and applied, such as genetic programming (GP), multiple regression, Markov Chains, and finally, well-used criteria for the r2 and p-value for the development of a method to determine temporal-trends. The statistical methods and models were mainly based on the variables chlorophyll-a (chl-a) and total phosphorus (TP) concentrations, but some methods and models can be directly transferred to other variables.

The main findings in this thesis were that multiple regressions overcome the performance of GP to predict summer chl-a concentrations and that multiple regressions can be used to generally describe the chl-a seasonality with TP summer concentrations and the latitude as independent variables. Also, it is possible to calculate probabilities, using Markov Chains, of exceeding certain chl-a concentrations in future months. Results showed that deep water concentrations were in general closely related to the surface water concentrations along with morphometric parameters; these independent variables can therefore be used in mass-balance models to estimate the mass in deep waters. A new statistical method was derived and applied to confirm whether variables have changed over time or not for cases where other traditional methods have failed. Finally, it is concluded that the statistical methods and models developed in this thesis will increase the understanding for predictions within and across aquatic systems.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 59 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1300
Lake, Water quality, Chlorophyll-a, Total phosphorus, Seasonality, Morphometry, Regression model, Probability, Markov chain, Genetic programming, Temporal-trend
National Category
Earth and Related Environmental Sciences Environmental Sciences Oceanography, Hydrology, Water Resources Probability Theory and Statistics
Research subject
Earth Science with specialization in Environmental Analysis
urn:nbn:se:uu:diva-263283 (URN)978-91-554-9362-2 (ISBN)
Public defence
2015-11-27, Hambergsalen, Villavägen 16, Uppsala, 10:00 (English)
Available from: 2015-11-05 Created: 2015-09-30 Last updated: 2015-11-10

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Dimberg, Peter H.
By organisation
In the same journal
Environmental Modelling and Assessment
Earth and Related Environmental SciencesEnvironmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 343 hits
ReferencesLink to record
Permanent link

Direct link