Modeling microalgal abundance with artificial neural networks: Demonstration of a heuristic ‘Grey-Box’ to deconvolve and quantify environmental influences

Millie, David F.; Weckman, Gary R.; Young, William A.; Ivey, James E.; Carrick, Hunter J.; Fahnenstiel, Gary L.

doi:10.1016/j.envsoft.2012.04.009

Cited by 49 publications

(23 citation statements)

References 61 publications

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“…The power of machine-learning non-parametric regression is based on the fact that it does not depend on any priori assumptions about the data. On the other hand, the resulting models may have a higher number of parameters and may lack the parametric models' simplicity and clarity (Millie et al, 2012). In addition, locally collected fieldwork datasets are usually reduced and can have a high level of error (Kitahara et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary feature selection to estimate forest stand variables using LiDAR

García-Gutiérrez

González-Ferreiro

Santos

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tLight detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing interest in machine learning and evolutionary computation has recently arisen to improve regression use in LiDAR data processing. Although evolutionary machine learning has already proven to be suitable for regression, evolutionary computation may also be applied to improve parametric models such as MLR. This paper provides a hybrid approach based on joint use of MLR and a novel genetic algorithm for the estimation of the main forest stand variables. We show a comparison between our genetic approach and other common methods of selecting predictors. The results obtained from several LiDAR datasets with different pulse densities in two areas of the Iberian Peninsula indicate that genetic algorithms perform better than the other methods statistically. Preliminary studies suggest that a lack of parametric conditions in field data and possible misuse of parametric tests may be the main reasons for the better performance of the genetic algorithm. This research confirms the findings of previous studies that outline the importance of evolutionary computation in the context of LiDAR analisys of forest data, especially when the size of fieldwork datatasets is reduced.

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Section: Introductionmentioning

confidence: 99%

Evolutionary feature selection to estimate forest stand variables using LiDAR

García-Gutiérrez

González-Ferreiro

Santos

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

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“…Artificial neural networks have been utilised for several applications in research of different types of surface waters, both inland (Singh et al, 2009;He et al, 2011) and marine waters (Lee et al, 2003;Millie et al, 2012). These techniques have frequently been used for major groups of aquatic organisms, i.e., fish (Suryanarayana et al, 2008;Penczak et al, 2012), macroinvertebrates (Lencioni et al, 2007;Kim et al, 2008), algae (Lee et al, 2003;Jeong et al, 2006) whereas macrophyte data have been treated by ANNs relatively rarely (Samecka-Cymerman et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

et al. 2013

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The study focused on modelling of macropyte indices against physico-chemical parameters of waters by artificial neural networks. Several macrophyte diversity indices were analysed (species richness-N, the Shannon index-H 0 , the Simpson index-D, and the Pielou index-J) as well as the ecological status index (the Macrophyte Index for Rivers-MIR). The aim of the study was to verify knowledge about potential application of macrophytes in the environmental monitoring. A Multi-Layer Perceptron type of network was used in the analyses. The study included 260 river sites located throughout Poland. Alkalinity, conductivity, pH, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand were used as the explanatory variables. The quality of the constructed models was assessed using calculated errors (RMSE and NRMSE) and r Pearson's linear correlation coefficient. The neural network for the MIR index was characterised by the highest quality. Neural networks for other diversity indices (N, H 0 , D, and J) did not provide adequate results for modelling, which shows their ineffectiveness biological monitoring. Sensitivity analysis revealed the influence of each variable to the models. It indicated that modelled values of MIR are most strongly influenced by total phosphorus and alkalinity.

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“…Besides, the typically modelled water quality parameters are pH, salinity or algal growth (e.g. DeSilet et al, 1992;Bastarache et al, 1997;Zhang and Stanley, 1997;Whitehead et al, 1997;Millie et al, 2012) with limited focus on nutrients. Additionally, few studies have attempted to forecast these parameters more than one day ahead in the future.…”

Section: Existing Water Quality Parameters Prediction Models and Dssmentioning

confidence: 99%

An autonomous decision support system for manganese forecasting in subtropical water reservoirs

Bertone

Stewart

Zhang

et al. 2015

Environmental Modelling & Software

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Modeling microalgal abundance with artificial neural networks: Demonstration of a heuristic ‘Grey-Box’ to deconvolve and quantify environmental influences

Abstract: openAccessArticle: FalsePage Range: 27-27doi: 10.1016/j.envsoft.2012.04.009Harvest Date: 2016-01-12 15:13:47issueName:cover date: 2012-12-01pubType

Cited by 49 publications

References 61 publications

Evolutionary feature selection to estimate forest stand variables using LiDAR

Evolutionary feature selection to estimate forest stand variables using LiDAR

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

An autonomous decision support system for manganese forecasting in subtropical water reservoirs

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