Artificial neural network approach for modelling and prediction of algal blooms

Recknagel, Friedrich; French, Mark; Harkonen, Pia; Yabunaka, Ken-ichi

doi:10.1016/s0304-3800(96)00049-x

Cited by 311 publications

(122 citation statements)

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“…Parameter sets not considered behavioural because a threshold performance is not achieved are discarded from further analysis. The Bayesian framework within which the procedure has been developed (Romanowicz et al, 1996) effectively allows for the estimation of model uncertainties by constraining prior information on the parameters in the form of prior distributions using the data available. The posterior parameter distributions which result can be used to make model predictions where the spread, or uncertainty, due to the parameter distributions is shown.…”

Section: Introductionmentioning

confidence: 99%

Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

Khu

Werner

2003

Hydrol. Earth Syst. Sci.

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Monte-Carlo (MC) simulation based techniques are often applied for the estimation of uncertainties in hydrological models due to uncertain parameters. One such technique is the Generalised Likelihood Uncertainty Estimation technique (GLUE). A major disadvantage of MC is the large number of runs required to establish a reliable estimate of model uncertainties. To reduce the number of runs required, a hybrid genetic algorithm and artificial neural network, known as GAANN, is applied. In this method, GA is used to identify the area of importance and ANN is used to obtain an initial estimate of the model performance by mapping the response surface. Parameter sets which give non-behavioural model runs are discarded before running the hydrological model, effectively reducing the number of actual model runs performed. The proposed method is applied to the case of a simple two-parameter model where the exact parameters are known as well as to a widely used catchment model where the parameters are to be estimated. The results of both applications indicated that the proposed method is more efficient and effective, thereby requiring fewer model simulations than GLUE. The proposed method increased the feasibility of applying uncertainty analysis to computationally intensive simulation models.

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Section: Introductionmentioning

confidence: 99%

Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

Khu

Werner

2003

Hydrol. Earth Syst. Sci.

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“…Maier et al [49] used ANNs to predict optimal alum doses and treated water quality parameters. However, most of the studies were undertaken for limnological systems [50][51][52][53] or riverine systems [54][55] whilst report on ANN modelling of a coastal system has been very scarce [56]. Moreover, in most of the studies, the effectiveness of ANN as a predictive tool has not been fully addressed.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

A review on integration of artificial intelligence into water quality modelling

Chau

2006

Marine Pollution Bulletin

214

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With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

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“…Chau and Muttil (2007) studied the ecological and related water quality data from different periods from several monitoring stations in Tolo Harbour, Hong Kong by descriptive data mining techniques and the results from box plots reveals the spatial, temporal patterns, which in turn helps to find out the stations which are most susceptible to eutrophication, its nutrient source and control measures. Recknagel et al (1997) did a study on predictive potential of phytoplankton models by ANN and compared with other models such as AD HOC inductive models and found that predictive accuracy improved with increased event and time resolution of data. Recknagel et al (2002) compare the potential of ANN and GA in terms of forecasting and understanding of algal blooms in Lake Kasumigaura, Japan and found that models evolved by GA performs better than ANN models and provide more transparency for physical explanation as well.…”

Section: Introductionmentioning

confidence: 99%

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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Artificial neural network approach for modelling and prediction of algal blooms

Cited by 311 publications

References 10 publications

Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

A review on integration of artificial intelligence into water quality modelling

Prediction of algal blooms using genetic programming

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