Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique

Fernández, J.R. Alonso; Muñiz, C. Díaz; Nieto, P.J. Garcı́a; Juez, Francisco Javier de Cos; Lasheras, Fernando Sánchez; Roqueñí, M.N.

doi:10.1016/j.ecoleng.2012.12.015

Cited by 39 publications

(15 citation statements)

References 29 publications

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“…The increasing number of eddy-covariance sites across the globe has encouraged the application of data-driven models by machine learning (ML) methods such as artificial neural networks (ANNs, Papale and Valentini, 2003), random forest (RF, Tramontana et al, 2015), model trees ensemble (MTE, Jung et al, 2009;Xiao et al, 2008Xiao et al, , 2010 or support vector regression (SVR, Yang et al, 2006Yang et al, , 2007 to estimate land surface-atmosphere fluxes from site level to regional or global scales (e.g., Beer et al, 2010Kondo et al, 2015;Schwalm et al, 2010Schwalm et al, , 2012Yang et al, 2007;Xiao et al, 2008Xiao et al, , 2010. The ML upscaled outputs are also increasingly used to evaluate process-based land surface models (e.g., Anav et al, 2013;Bonan et al, 2011;Ichii et al, 2009;Piao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

et al. 2016

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There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R 2 < 0.5), ecosystem respiration (R 2 > 0.6), gross primary production (R 2 > 0.7), latent heat (R 2 > 0.7), sensible heat (R 2 > 0.7), and net radiation (R 2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R 2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R 2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Introductionmentioning

confidence: 99%

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

et al. 2016

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“…After this step, the regression technique is made over each cluster. Prior researches like [26][27][28][29] solve other physical problems by using similar techniques.…”

Section: Introductionmentioning

confidence: 99%

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

et al. 2017

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Batteries are one of the principal components in electric vehicles and mobile electronic devices. They operate based on electrochemical reactions, which are exhaustively tested to check their behavior and to determine their characteristics at each working point. One remarkable issue of batteries is their complex behavior. The power cell type under analysis in this research is a LFP (Lithium Iron Phosphate LiFePO4). The purpose of this research is to predict the power cell State of Charge (SOC) by creating a hybrid intelligent model. All the operating points measured from a real system during a capacity confirmation test make up the dataset used to obtain the model. This dataset is clustered to obtain different behavior groups, which are used to develop the final model. Different regression techniques such as polynomial regression, support vector regression (SVR), and artificial neural networks (ANN) have been implemented for each cluster. A combination of these methods is performed to achieve an intelligent model. The SOC of the power cell can be predicted by this hybrid intelligent model, and good results are achieved.

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“…The selection of the basis functions can be done with a two-at-a-time forward stepwise procedure [15]. This forward stepwise selection of basis function leads to an over-fitted model; this means that although it fits the training data well, it becomes a very complex model that is not able to make predictions accurately with new objects.…”

Section: Methodsmentioning

confidence: 99%

A Parametric Model of the LARCODEMS Heavy Media Separator by Means of Multivariate Adaptive Regression Splines

et al. 2017

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Modeling of a cylindrical heavy media separator has been conducted in order to predict its optimum operating parameters. As far as it is known by the authors, this is the first application in the literature. The aim of the present research is to predict the separation efficiency based on the adjustment of the device’s dimensions and media flow rates. A variety of heavy media separators exist that are extensively used to separate particles by density. There is a growing importance in their application in the recycling sector. The cylindrical variety is reported to be the most suited for processing a large range of particle sizes, but optimizing its operating parameters remains to be documented. The multivariate adaptive regression splines methodology has been applied in order to predict the separation efficiencies using, as inputs, the device dimension and media flow rate variables. The results obtained show that it is possible to predict the device separation efficiency according to laboratory experiments performed and, therefore, forecast results obtainable with different operating conditions.

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Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique

Cited by 39 publications

References 29 publications

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

A Parametric Model of the LARCODEMS Heavy Media Separator by Means of Multivariate Adaptive Regression Splines

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