Genetic programming for analysis and real-time prediction of coastal algal blooms

Muttil, Nitin; Lee, Joseph Hun Wei

doi:10.1016/j.ecolmodel.2005.03.018

Cited by 113 publications

(70 citation statements)

References 15 publications

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“…Similar conclusions on the variables affecting algal bloom modeling has been arrived at by other researchers, for example, Recknagel et al (2002), Bobbin and Recknagel (1999), Lee et al (2003) and Muttil and Lee (2005). Figure 4 shows the plot of prediction obtained with model 10 in Table 5 (presented as Eq (7) above) and the actual Chy t+14 .…”

supporting

confidence: 81%

Prediction of algal blooms using genetic programming

Chandrasekaran

Muttil

Muthukumar

et al. 2010

Marine Pollution Bulletin

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In this study an attempt is made to mathematically model and predict algal blooms in Tolo Harbour (Hong Kong) using Genetic Programming (GP). Chlorophyll plays a vital role and is taken as a measure of algal bloom biomass and 8 other variables are taken as input for its prediction. It is observed that GP evolves multiple models with almost same values of errors -of -measure. Previous studies on GP modeling primarily focused on comparing the GP results with actual values. In contrast, in this study, the main aim is to propose a systematic procedure for identifying the most appropriate GP model from a list of feasible models (with almost same errorof measure) using physical understanding of the process aided by data interpretation. The study of the GP-evolved equations shows that they correctly identify the ecologically significant variables.Analysis of final GP evolved mathematical model indicates that of the 8 variables assumed to affect the algal bloom, the most significant effect is due to chlorophyll, total inorganic nitrogen and dissolved oxygen, as far as one week prediction is concerned. For higher lead prediction (biweekly), secchi disc depth and temperature appears as significant variables in addition to chlorophyll.

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supporting

confidence: 81%

Prediction of algal blooms using genetic programming

Chandrasekaran

Muttil

Muthukumar

et al. 2010

Marine Pollution Bulletin

Self Cite

View full text Add to dashboard Cite

show abstract

“…Type I parameters (see Table II) are those whose values are known and universally accepted. [5] Type II parameters (see Table III) are those for which a range of values have been reported in the literature. The values of the Type III parameters (see Table IV) are mostly unknown, but they concern certain important biological processes incorporated in the above model.…”

Section: A Algal Growth Model Descriptionmentioning

confidence: 99%

Calibration of Algal Growth Model Using Multi-objective Genetic Algorithm

Prakash¹,

Ratn²,

Kumar³

et al. 2015

IJESD

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“…ANNS require the identification of the network structure and then the coefficients (weights) are calculated during the learning process. In GP, the terminal and function sets are defined initially, and then both the optimal form of the model and the coefficients are calculated by GP algorithm (Muttil & Lee, 2005). The GP models can provide additional information about the problem by finding the best fit analytic function.…”

Section: Genetic Programmingmentioning

confidence: 99%

“…Like many other areas of computer sciences, GP has been widely utilized in the real world condition. GP creates numerous random populations in the large space of possible solutions (computer programs) to avoid the likelihood of stopping in a "local optimum" (Muttil & Lee, 2005). The functions or programs are called organisms or chromosomes.…”

Section: Genetic Programmingmentioning

confidence: 99%

A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Ghomi¹,

Ansarinejad²,

Razaghi³

et al. 2014

MAS

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This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn't need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

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Genetic programming for analysis and real-time prediction of coastal algal blooms

Cited by 113 publications

References 15 publications

Prediction of algal blooms using genetic programming

Prediction of algal blooms using genetic programming

Calibration of Algal Growth Model Using Multi-objective Genetic Algorithm

A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

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