Prediction of algal blooms using genetic programming

Chandrasekaran, S.; Muttil, Nitin; Muthukumar, S.; Arun, Vijayan

doi:10.1016/j.marpolbul.2010.05.020

Cited by 49 publications

(14 citation statements)

References 10 publications

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“…Same with related studies [6] and [12], the data from the most weakly flushed monitoring station, TM3 (see Fig. 1), are used in order to isolate the hydrodynamic effects as much as possible.…”

Section: B Data Sourcementioning

confidence: 99%

Harmful algal blooms prediction with machine learning models in Tolo Harbour

Jia

et al. 2014

2014 International Conference on Smart Computing

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Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on backpropagation (BP) neural network, generalized regression neural network (GRNN) and support vector machine (SVM) respectively. The experimental results show that the improved BP algorithm and SVM work better than GRNN methods, and the models based on SVM present the best performance in terms of goodness-of-fit measures, but need to be further improved in the running time. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably, but it is not ideally suited for exact predictions. The use of higher frequency data may improve the accuracy of the predictions. Keywords-harmful algal blooms; machine learnning; artificial neural network; back-propagation neural network; generalized regression neural network; support vector machineI.

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Section: B Data Sourcementioning

confidence: 99%

Harmful algal blooms prediction with machine learning models in Tolo Harbour

Jia

et al. 2014

2014 International Conference on Smart Computing

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“…ANN is a SML approach widely used to predict the algal abundance [60][61][62][63][64] while SVM is used much less in algal research [65][66][67]. Some studies used genetic algorithms (GA) to create prediction model [68,69]. The basic concept of these models lies in the combined effects of a set of explanatory variables (Xi) on one or some target variables (Yi),…”

Section: Deterministic Versus Probabilisticmentioning

confidence: 99%

An Overview of Cyanobacteria Harmful Algal Bloom (CyanoHAB) Issues in Freshwater Ecosystems

Benayache¹,

Nguyen-Quang²,

Hushchyna³

et al. 2019

Limnology - Some New Aspects of Inland Water Ecology

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This chapter will present an overview of cyanobacterial harmful algal blooms (cyano-HABs) and biotic and abiotic factors, as well as various aspects associated with these worldwide ecological bursts. The exact causes of the cyanoHABs are still not well defined, but eutrophication and climate change (temperature increase, light intensity variation, etc.) are the two assumed main factors that may promote the proliferation and expansion of cyanobacterial blooms. However, these premises need to be profoundly investigated as the optimal combination of all factors such as increased nutrient loading, physiological characteristics of cyanobacterial species, and climate effects which could lead to the blooming pattern will require robust modeling approaches to predict the phenomena. Negative issues associated with cyanoHABs are diverse including the toxic products (cyanotoxins) released by certain taxa which can damage the health of humans and animal habitats around the related watershed as well as generate a huge water quality problem for aquatic industries.

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“…Nowadays, cyanobacterial harmful algal bloom (HAB) has become a serious problem, contributes seriously to the degradation of the drinking water quality and affects human health and the aquatic life with long-lasting effects (Sivapragasam et al 2010), including bad odors and tastes, reduction in water clarity and oxygen depletion (hypoxia or anoxia) during bloom decay (Sharaf et al 2019). Monitoring cyanobacteria also known as blue-green algae (CBG) is of great importance for freshwater ecosystems; however, it has been very difficult over the years to ensure effective and adequate monitoring of cyanobacteria in freshwater (Backer 2002).…”

Section: Introductionmentioning

confidence: 99%

Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

2019

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In the present investigation, the usefulness and capabilities of four artificial intelligence (AI) models, namely feedforward neural networks (FFNNs), gene expression programming (GEP), adaptive neuro-fuzzy inference system with grid partition (ANFIS-GP) and adaptive neuro-fuzzy inference system with subtractive clustering (ANFIS-SC), were investigated in an attempt to evaluate their predictive ability of the phycocyanin pigment concentration (PC) using data from two stations operated by the United States Geological Survey (USGS). Four water quality parameters, namely temperature, pH, specific conductance and dissolved oxygen, were utilized for PC concentration estimation. The four models were evaluated using root mean square errors (RMSEs), mean absolute errors (MAEs) and correlation coefficient (R). The results showed that the ANFIS-SC provided more accurate predictions in comparison with ANFIS-GP, GEP and FFNN for both stations. For USGS 06892350 station, the R, RMSE and MAE values in the test phase for ANFIS-SC were 0.955, 0.205 μg/L and 0.148 μg/L, respectively. Similarly, for USGS 14211720 station, the R, RMSE and MAE values in the test phase for ANFIS-SC, respectively, were 0.950, 0.050 μg/L and 0.031 μg/L. Also, using several combinations of the input variables, the results showed that the ANFIS-SC having only temperature and pH as inputs provided good accuracy, with R, RMSE and MAE values in the test phase, respectively, equal to 0.917, 0.275 μg/L and 0.200 μg/L for USGS 06892350 station. This study proved that artificial intelligence models are good and powerful tools for predicting PC concentration using only water quality variables as predictors.

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Prediction of algal blooms using genetic programming

Cited by 49 publications

References 10 publications

Harmful algal blooms prediction with machine learning models in Tolo Harbour

Harmful algal blooms prediction with machine learning models in Tolo Harbour

An Overview of Cyanobacteria Harmful Algal Bloom (CyanoHAB) Issues in Freshwater Ecosystems

Application of artificial intelligence to estimate phycocyanin pigment concentration using water quality data: a comparative study

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