Neural network modelling of coastal algal blooms

Lee, Joseph Hun Wei; Huang, Yan Yan Shery; Dickman, Mike; Jayawardena, A. W.

doi:10.1016/s0304-3800(02)00281-8

Cited by 230 publications

(147 citation statements)

References 14 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%

“…Lee et al (2003) found that the algal concentration in the samples from Tolo Harbour is dependent primarily on their antecedent concentrations in the immediate past weeks, and the result was supported by interpretation of the neural networks' weights. Coad et al (2005) observed that with antecedent chlorophyll-a, feed forward neural network with logistic function is able to predict the future chlorophyll-a reasonably well, indicating the sufficiency of historical values of chlorophyll-a in its future modeling.…”

Section: Introductionmentioning

confidence: 70%

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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%

Section: Introductionmentioning

confidence: 70%

Prediction of algal blooms using genetic programming

Chandrasekaran

Muttil

Muthukumar

et al. 2010

Marine Pollution Bulletin

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“…Study of input flow amount into the reservoir (Ichiyanagi 1995), hydraulic specifications (Salmasi 2010) and sediments estimation (Jothiprakash et al 2009 Lee et al (2003) used this method for Tolo Dam in Hong Kong in addition to challenge the previous studies on using artificial neural networks in modeling eutrophication phenomena in the dam reservoirs. Joseph thinks that, most studies up to that time had no appropriate processing on the data and virtually all available parameters have been used as input.…”

Section: Study Area Dez Dam Water Basin Is Located In the Zagrosmentioning

confidence: 99%

“…Hence, in their study, the input data have been chosen with greater sensitivity and precision. Joseph in this study used three-layer back propagation neural network with transfer function sigmoid (Lee et al 2003). Muhittin Aslan (2008) simulated Eymir Dam in Turkey using artificial neural networks and fuzzy inference systems and compared the obtained data.…”

Section: Study Area Dez Dam Water Basin Is Located In the Zagrosmentioning

confidence: 99%

Investigation on trophic state index by artificial neural networks (case study: Dez Dam of Iran)

2014

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Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that artificial neural network (ANN) is a suitable tool for quality modeling of reservoir of dam and increment and decrement of nutrients in trend of eutrophication. Therefore, ANN is a suitable tool for quality modeling of reservoir of dam.

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“…Recientemente, se han empleado RNA para simular dinámicas caóticas y complejas, como pueden ser el clima (Lennon & Turner, 1995;Lee et al, 1998;Bryan & Adams, 2001;Pérez-Llera, 2002), o las mareas rojas (Lee et al, 2003), que de otra forma serían difícilmente predecibles.…”

Section: Introductionunclassified

Predicción de la potencialidad de los bosques esclerófilos españoles mediante redes neuronales artificiales

Garzón¹,

Ruiz²,

Dios³

et al. 2003

GRAELLSIA

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RESUMENEncinares y alcornocales son dos de las formaciones esclerófilas más importantes de la Iberia mediterránea. Para conocer cual es su potencialidad en el territorio español se ha empleado un modelo generado mediante redes neuronales artificiales con un algoritmo de retropropagación de errores que conduce la información siempre hacia delante. Las variables bioclimáticas empleadas como predictores son: altitud, continentalidad, insolación, precipitación total, temperatura media anual, temperatura media de las mínimas del mes más frío y temperatura media de las máximas del mes más cálido, con una resolución de 10 km. Las redes neuronales se perfilan como una herramienta de gran poder predictivo. Se aprecian patrones de respuesta diferente para las formaciones estudiadas. Mientras que para la encina se simula un área potencial continua y extensa, para el alcornoque se obtiene un área fragmentada y restringida, que se ajusta bastante a su presencia actual. La principal discrepancia del modelo presentado con esquemas de vegetación potencial anteriores radica en la ausencia de encinares y alcornocales en zonas térmicas del Levante y sur peninsular. Palabras clave: vegetación potencial, redes neuronales artificiales, Quercus ilex, Quercus suber, encina, alcornoque ABSTRACT Predicting spanish sclerophyllous forests potentiality using artificial neural networksHolm oak and cork oak forests are between the most important sclerophyllous formations in the Mediterranean Iberia. In order to study their potentiality, an artificial neural network model, with a feedforward BP algorithm, has been applied. The elevation, continentality, insolation, annual rainfall, annual mean temperature, mean temperature of the coldest month and mean temperature of the warmest month are the used bioclimatic variables with a 10 km resolution. The neural networks seem a highly predictive powerful tool. Different patterns in the response of the studied forests have been shown. The holm oak presents a continuous and wide potential simulate range. Meanwhile the cork oak potential area is fragmented and restricted, in accordance with its actual distribution area. The lack of both forests in the eastern and southern warm zones of Iberian Peninsula is the main discrepancy with previous potential vegetation proposals.

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Neural network modelling of coastal algal blooms

Cited by 230 publications

References 14 publications

Prediction of algal blooms using genetic programming

Prediction of algal blooms using genetic programming

Investigation on trophic state index by artificial neural networks (case study: Dez Dam of Iran)

Predicción de la potencialidad de los bosques esclerófilos españoles mediante redes neuronales artificiales

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