Coastal ‘Big Data’ and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric

Millie, David F.; Weckman, Gary R.; Young, William A.; Ivey, James E.; Fries, David; Ardjmand, Ehsan; Fahnenstiel, Gary L.

doi:10.1016/j.ecss.2013.04.001

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…Finally, predicting Chla or other water quality parameters requires models based on either statistics (empirical approach, e.g., for Chesapeake Bay [30] and Millie et al (2013) for Sarasota Bay [31]) or coupled hydrodynamics and biology (physics-based approach, e.g., Popova et al (2002) for the northeast Atlantic [32]; Hu et al (2006) for Taihu Lake [33]). The latter approach has physical-and biological-governing equations explicitly built into the models, making it easier to test the connections between physical/biological forces and the biological responses.…”

Section: Introductionmentioning

confidence: 99%

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

Duan

et al. 2014

Remote Sensing

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For near real-time water applications, the Moderate Resolution Imaging Spectroradiometers (MODIS) on Terra and Aqua are currently the only satellite instruments that can provide well-calibrated top-of-atmosphere (TOA) radiance data over the global aquatic environments. However, TOA radiance data in the MODIS ocean bands over turbid atmosphere in east China often saturate, leaving only four land bands to use. In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate chlorophyll a concentrations (Chla, μg/L) in surface waters of Taihu Lake, the third largest freshwater lake in China. The EOF approach analyzed the spectral variance of normalized Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm, and subsequently related that variance to Chla using 28 concurrent MODIS and field measurements. This empirical algorithm was then validated using another 30 independent concurrent MODIS and field measurements. Image analysis and radiative transfer simulations indicated that the algorithm appeared to be tolerant to aerosol perturbations, with unbiased RMS uncertainties of <80% for Chla ranging between 3 and 100 μg/L. Application OPEN ACCESSRemote Sens. 2014, 6 10695 of the algorithm to a total of 853 MODIS images between 2000 and 2013 under cloud-free conditions revealed spatial distribution patterns and seasonal changes that are consistent to previous findings based on floating algae mats. The current study can provide additional quantitative estimates of Chla that can be assimilated in an existing forecast model, which showed improved performance over the use of a previous Chla algorithm. However, the empirical nature, relatively large uncertainties, and limited number of spectral bands all point to the need of further improvement in data availability and accuracy with future satellite sensors.

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Section: Introductionmentioning

confidence: 99%

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

Duan

et al. 2014

Remote Sensing

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“…Previous discussions about working with "big" ecological data have focused largely on cyber-infrastructure capabilities, data management (Michener and Jones, 2012;Gilbert et al, 2014), and the need for datadriven approaches (Kelling et al, 2009). While novel analytical techniques such as machine learning and crowd-sourcing for processing large and complex ecological data sets are increasingly reported in the terrestrial literature (Kelling et al, 2013;Peters et al, 2014), marine examples are limited (Wiley et al, 2003;Dugan et al, 2013;Millie et al, 2013;Shamir et al, 2014). Given this paucity and the need to use "big" biological oceanography and marine ecology data for rapid assessment of ocean health and adaptive management of ecosystems, we present here an evolution of approaches applied to the problem of efficiently classifying tens of millions of images of individual plankters generated by ISIIS.…”

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confidence: 99%

A Tale of Two Crowds: Public Engagement in Plankton Classification

et al. 2017

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"Big data" are becoming common in biological oceanography with the advent of sampling technologies that can generate multiple, high-frequency data streams. Given the need for "big" data in ocean health assessments and ecosystem management, identifying and implementing robust, and efficient processing approaches is a challenge for marine scientists. Using a large plankton imagery data set, we present two crowd-sourcing approaches applied to the problem of classifying millions of organisms. The first used traditional crowd-sourcing by asking the public to identify plankton through a web-interface. The second challenged the data science community to develop algorithms via an industry partnership. We found traditional crowd-sourcing was an excellent way to engage and educate the public while crowd-sourcing data scientists rapidly generated multiple, effective solutions. As the need to process and visualize large and complex marine data sets is expected to grow over time, effective collaborations between oceanographers and computer and data scientists will become increasingly important.

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“…Multilayer perceptrons (MLPs) utilizing a backpropagation learning algorithm were originated (NeuroSolutions version 6.0 software; NeuroDimension, Inc., Gainesville, Florida USA), as follows: [CHL a] or biovolumesϭ f{W P 1 ,P 3 [ f(W X 1 ,P 1 ·X 1 ϩW X 2 ,P 1 ·X 2 …W X i ,P 1 ·X i ϩ 1 )]} ϩ f{W P 2 ,P 3 [ f(W X 1 ,P 2 ·X 1 ϩW X 2 ,P 2 ·X 2 …W X i ,P 2 ·X i ϩ 2 )]} ϩ f{W P j ,P 3 [f(W X 1 ,P j ·X 1 ϩW X 2 ,P j ·X 2 …W X i ,P j ·X i ϩ j )]} where X 1,2,…,i are predictor variables, P 1,2,3,…,j are processing elements, W X 1,2,...,i ,P 1,2,3,...,j are scalar weights, and 1,2,…,j is the error (after Principe et al 2000). Topologies were optimized for the number of processing elements within hidden layers and the types of transfer functions (e.g., sigmoid, hyperbolic tangent) and learning rules (e.g., conjugate gradient, momentum; see Millie et al 2012Millie et al , 2013. Data vectors were assigned randomly to subsets for network training (to "fit" the data), cross-validation (to provide unbiased estimation of prediction), and testing (to assess performance) of 60%, 15%, and 25% of data, respectively.…”

Section: Data Characterization and Modelingmentioning

confidence: 99%

“…Based on sensitivity analysis, select (pairs of) predictors were varied across their data distributions, with three-dimensional (3D) response surfaces for CHL a concentrations and Microcystis biovolumes generated via reproduced network computations (after Millie et al 2012Millie et al , 2013. Two-dimensional (2D) response plots for select predictors and concentrations-biovolumes were derived (via averaging across contrasting variables within 3D surfaces), with predictor valuations relating to half-maximal concentrationsbiovolumes (akin to EC 50 /IC 50 metrics) calculated via four-parameter logistic equations; SigmaPlot software).…”

Section: Assessing and Visualizing Predictor-response Relationshipsmentioning

confidence: 99%

“…Response surfaces are utilized by researchers to (i) assess predictive capability or uncertainty of a conceptual model for biotic distributions, (ii) visualize functional environmental, climatic, and (or) spatial influences upon biota operating on dissimilar scales, and (iii) support conservation prioritization and resource management (Austin 2002;Guisan and Thuiller 2005;Peterson 2006). Such an "informatics" approach to data-driven modeling (see Millie et al 2011Millie et al , 2013Michener and Jones 2012) affords the means to collectively mine, model, and visualize biota-environmental associations within aquatic survey data. Yet, predictive success for and knowledge derivation arising from such models are "driven" by technical innovation, contingent upon the suitability and robustness of modeling software (Elith and Leathwick 2009;Thuiller et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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Using artificial intelligence for CyanoHAB niche modeling: discovery and visualization of Microcystis–environmental associations within western Lake Erie

Millie

Weckman

Fahnenstiel

et al. 2014

Can. J. Fish. Aquat. Sci.

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Cyanobacterial harmful algal blooms (CyanoHABs), mainly composed of the genus Microcystis, occur frequently throughout the Laurentian Great Lakes. We used artificial neural networks (ANNs) involving 31 hydrological and meteorological predictors to model total phytoplankton (as chlorophyll a) and Microcystis biomass from 2009 to 2011 in western Lake Erie. Continuous ANNs provided modeled-measured correspondences (and modeling efficiencies) ranging from 0.87 to 0.97 (0.75 to 0.94) and 0.71 to 0.90 (0.45 to 0.88) for training-cross-validation and test data subsets of chlorophyll a concentrations and Microcystis biovolumes, respectively. Classification ANNs correctly assigned up to 94% of instances for Microcystis presenceabsence. The influences of select predictors on phytoplankton and CyanoHAB niches were visualized using biplots and threedimensional response surfaces. These then were used to generate mathematical expressions for the relationships between modeled CyanoHAB outcomes and the direct and interactive influences of environmental factors. Based on identified conditions (ϳ40 to 50 g total phosphorus (TP)·L −1 , 22 to 26°C, and prolonged wind speeds less than ϳ19 km·h −1 ) underlying the likelihood of occurrence and accumulation of phytoplankton and Microcystis, a "target" concentration of 30 g TP·L −1 appears appropriate for alleviating blooms. ANNs generated robust ecological niche models for Microcystis, providing a predictive framework for quantitative visualization of nonlinear CyanoHAB-environmental interactions. Résumé: Les fleurs d'eau de cyanobactéries nuisibles (CyanoHAB), constituées principalement du genre Microcystis, sont fréquentes dans tous les Grands Lacs laurentiens. Nous avons utilisé des réseaux neuronaux artificiels (« ANNs ») incluant 31 prédicteurs hydrologiques et météorologiques pour modéliser la biomasse de phytoplancton totale (chlorophylle a) et de Microcystis, de 2009 à 2011, dans la partie occidentale du lac Érié. Des ANNs continus ont fourni des correspondances modèle-mesures (et des valeurs d'efficacité de la modélisation) allant de 0,87 à 0,97 (0,75 à 0,94) et de 0,71 à 0,90 (0,45 à 0,88) pour des sous-ensembles de données d'entraînement-de contrevalidation et d'essai des concentrations de la chlorophylle a et des biovolumes de Microcystis, respectivement. Les ANNs de classification ont affecté correctement jusqu'à 94 % des cas d'absence ou de présence de Microcystis. Les influences de prédicteurs sélectionnés sur les niches de phytoplancton et de CyanoHAB ont été visualisées à l'aide de diagrammes de double projection et de surfaces de réponse tridimensionnelles, qui ont ensuite été utilisés pour produire des expressions mathématiques pour les relations entre les CyanoHAB modélisées et les influences directes et interactives de facteurs environnementaux. À la lumière des conditions cernées (ϳ40 à 50 g phosphore total (PT)·L −1 , de 22 à 26°C et des vitesses du vent soutenues inférieures à ϳ19 km·h −1 ) sous-tendant la probabilité de présence et d'accumulation de phytoplancto...

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Coastal ‘Big Data’ and nature-inspired computation: Prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric

Cited by 15 publications

References 29 publications

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

An EOF-Based Algorithm to Estimate Chlorophyll a Concentrations in Taihu Lake from MODIS Land-Band Measurements: Implications for Near Real-Time Applications and Forecasting Models

A Tale of Two Crowds: Public Engagement in Plankton Classification

Using artificial intelligence for CyanoHAB niche modeling: discovery and visualization of Microcystis–environmental associations within western Lake Erie

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