Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter

Lui, Gilbert C.S.; Li, Wai Keung; Leung, Kenneth M.Y.; Lee, Joseph Hun Wei; Jayawardena, A. W.

doi:10.1016/j.ecolmodel.2006.06.017

Cited by 34 publications

(12 citation statements)

References 19 publications

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“…While this suggests that generic solutions for the management of HABs may be difficult to achieve, predicting the occurrence of blooms in specific areas would be of obvious benefit to enable the development and implementation of appropriate mitigation strategies. Consequently, several studies have used statistical models to make species and site specific HAB predictions for a variety of species (e.g., Lee et al 2003, Chau 2005, Muttil and Chau 2006, Lui et al 2007.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model averaging for harmful algal bloom prediction

Hamilton

McVinish

Mengersen

2009

Ecological Applications

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Abstract. Harmful algal blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations, and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian model averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate, average monthly minimum temperature, showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilized the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

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Section: Introductionmentioning

confidence: 99%

Bayesian model averaging for harmful algal bloom prediction

Hamilton

McVinish

Mengersen

2009

Ecological Applications

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“…Globally, increasing human impacts on coastal areas have generally resulted in decreasing water quality (WRI, 2003;Codd et al, 2005). In association with decreasing water quality, increased human exploitation of estuarine resources and habitats has led to detrimental consequences for the trophic structure of estuaries, thereby promoting favourable conditions for algal blooms (Lui et al, 2007). In most cases, algal blooms do not damage estuarine ecosystems in the long term; however they may be dangerous to human health and deleterious for commercial activities in an estuary (Zingone and Oksfeldt Enevoldsen, 2000).…”

Section: Introductionmentioning

confidence: 99%

Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network

Coad¹,

Cathers

Ball

et al. 2014

Environmental Modelling & Software

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“…Such models normally employ data to model the evolution of oceanographic parameters or the eutrophication process, which are considered possible causes of algal blooms. Examples of datadriven models include artificial neural networks (Corchado & Lees, 2001;Lee, Huang, Dickman, & Jayawardena, 2003), the radial basis function network (Florentino & Corchado, 2003), genetic programming (Muttil & Lee, 2005), the hidden Markov model with adaptive weighting (Jiang, Liu, Zhang, & Yuan, 2016), and the vector autoregressive model (Lui, Li, Leung, Lee, & Jayawardena, 2007).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian structural model for predicting algal blooms

Sun

Liu

Wang

2019

Journal of Forecasting

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A Bayesian structural model with two components is proposed to forecast the occurrence of algal blooms, multivariate mean‐reverting diffusion process (MMRD), and a binary probit model with latent Markov regime‐switching process (BPMRS). The model has three features: (a) forecast of the occurrence probability of algal bloom is directly based on oceanographic parameters, not the forecasting of special indicators in traditional approaches, such as phytoplankton or chlorophyll‐a; (b) augmentation of daily oceanographic parameters from the data collected every 2 weeks is based on MMRD. The proposed method solves the problem of unavailability of daily oceanographic parameters in practice; (c) BPMRS captures the unobservable factors which affect algal bloom occurrence and therefore improve forecast accuracy. We use panel data collected in Tolo Harbour, Hong Kong, to validate the model. The model demonstrates good forecasting for out‐of‐sample rolling forecasts, especially for algal bloom appearing for a longer period, which severely damages fisheries and the marine environment.

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Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter

Cited by 34 publications

References 19 publications

Bayesian model averaging for harmful algal bloom prediction

Bayesian model averaging for harmful algal bloom prediction

Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network

A Bayesian structural model for predicting algal blooms

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