Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials

Chen, Qiuwen; Guan, Tiesheng; Liu, Yun; Li, Ruonan; Recknagel, Friedrich

doi:10.1016/j.hal.2015.01.002

Cited by 59 publications

(27 citation statements)

References 36 publications

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“…The problem of forecasting algal blooms has been approached via ARIMA models. Chen et al (2015) [27] applied an ARIMA model to daily chlorophyll-a (chl-a) concentrations to obtain short-term predictions of algal blooms in Taihu Lake, China. The authors compared the proposed model to a multivariate linear regression (MVLR) model, which used several input variables, such as water temperature, water transparency, total inorganic nitrogen concentration, and phosphate concentration.…”

Section: Autoregressive Modelsmentioning

confidence: 99%

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

Cruz

Costa

Vinga

et al. 2021

JMSE

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Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.

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Section: Autoregressive Modelsmentioning

confidence: 99%

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

Cruz

Costa

Vinga

et al. 2021

JMSE

View full text Add to dashboard Cite

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“…Feasibility of application is improved because specific conditions of the physical world such as construction of weir and regulation of flow, do not need to be addressed. Furthermore, development of a commercially available online sensor for Chl-a also provides an additional strength to univariate model for early warning of algal blooms (Chen et al, 2015).…”

Section: Structural Strengths Of Mpum Compared With Other Modelsmentioning

confidence: 99%

“…This is mainly due to the uncertainty associated with the kinetic coefficients and the structural complexity of two-and threedimensional (3D) models which require a substantial amount of input data and demanding calibration and validation procedures (U. S. Environmental Protection Agency, 2015). Alternatively, predictions of algae concentrations have been explored using several statistical methods or artificial neural networks (Lee et al, 2003;Marsili-Libelli, 2004;Hamilton et al, 2009;Cha et al, 2014;Coad et al, 2014;Chen et al, 2015;Muttil and Lee, 2005;Oh et al, 2007;Malek et al, 2011). Depending on data availability, meta-heuristic approaches do not always produce consistent results, which may indicate a violation of the stationary process assumption in time series modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Transfer function approaches were used to predict biochemical oxygen demand (BOD) (Novotny and Olem, 1994) in a combined sewer system and to model d 18 O in hillslope runoff responses (Weiler et al, 2003;Iorgulescu et al, 2007) using hydrological components (e.g., rainfall and groundwater) as the input. When used to predict Chl-a concentrations, an Autoregressive Moving Integrated Moving Average (ARIMA) model showed the potential and feasibility of using a simple univariate model (Chen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A multiple process univariate model for the prediction of chlorophyll-a concentration in river systems

Kim

2016

Ann. Limnol. - Int. J. Lim.

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The concentration of chlorophyll-a (Chl-a) in river systems is dependent on various hydrometric and biochemical factors, including an intricate array of corresponding growth and extinction mechanisms. This complex and interactive assortment of factors makes prediction of algal blooms difficult. This paper introduces an innovative time-series model structure that predicts Chl-a concentration in inland waters. To improve the prediction accuracy of existing models, we assume that the predicting variable is determined by multiple and independent drivers. An enhanced stochastic model, namely a multiple process univariate model (MPUM), is developed to address the impacts of the distinct mechanisms associated with each regulator (e.g., hydrometeorological factors and anthropogenic activities). Observations of the algae concentration at 16 weirs along four major river systems in South Korea are used to model Chl-a concentration. Comparisons between traditional models and the proposed method demonstrate the strengths of the MPUM, both in making predictions and in the parsimony of the model structure. The robustness of the developed model was further validated by modeling algae concentration before and after river-flow regulation procedures.

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“…In addition, considering that the residual sequence contains certain noise and periodic components, a modified modeling method by extracting periodic component from the residual sequence of conventional statistical model is proposed in the study. Inspired by the application of singular spectrum analysis (SSA) in data processing [27][28][29][30] and the application of autoregressive integrated moving average (ARIMA) model in time series analysis, [31][32][33][34] the residual sequence obtained by conventional statistical model is processed and forecasted by SSA and ARIMA. Firstly, the conventional statistical model is established with stepwise regression model, and the residual sequence is reconstructed using the trend and periodic components extracted by SSA.…”

Section: Introductionmentioning

confidence: 99%

Modified dam deformation monitoring model considering periodic component contained in residual sequence

Yuan

Wei

Xie

et al. 2020

Struct Control Health Monit

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Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials

Cited by 59 publications

References 36 publications

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

A Review of Recent Machine Learning Advances for Forecasting Harmful Algal Blooms and Shellfish Contamination

A multiple process univariate model for the prediction of chlorophyll-a concentration in river systems

Modified dam deformation monitoring model considering periodic component contained in residual sequence

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