Mr. Habtom Ressom Profile

Habtom Ressom

at Univ of Maine

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Proceedings Article | 14 January 2002 Paper

Neural network-based prediction of phytoplankton primary production

Habtom Ressom, Mohamad Musavi, Padma Natarajan

Proceedings Volume 4488, (2002) https://doi.org/10.1117/12.452816

KEYWORDS: Neural networks, Data modeling, Carbon, Databases, Magnesium, Biological research, Error analysis, Water, MODIS, Artificial neural networks

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Empirical models have been used to estimate primary production based on phytoplankton biomass and light intensity. In this paper, an alterative approach for estimating primary production using neural networks is proposed. The inputs to the neural network are chlorophyll, surface irradiance, sea surface temperature, and day length. The output of the network is the estimated primary production. The back-propagation learning algorithm is used to train the neural network. A single step learning with random presentation sequence is selected as the learning strategy. The data set used for this experiment is extracted form the Ocean Primary Productivity Working Group database. The results show a significant decrease in the mean squared error of the log transformed primary production compared to the estimation obtained using linear model and the vertically generalized production model. The neural network- based models can deal with non-linear relationships more accurately, can effectively include variables that tend to co-vary non-linearly with the output variable, are flexible towards the choice of inputs, and are tolerant to noise. Hence to improve the estimation of primary production, additional parameters can be easily incorporated in the neural network model, even though no a prior knowledge about het effect of these parameters is available. These important features of neural networks make them an ideal candidate for constructing primary production models for both case 1 and case 2 waters.

Proceedings Article | 14 January 2002 Paper

Neural network-based estimation of chlorophyll-a concentration in coastal waters

Mohamad Musavi, Richard Miller, Habtom Ressom, Padma Natarajan

Proceedings Volume 4488, (2002) https://doi.org/10.1117/12.452814

KEYWORDS: Neural networks, Data modeling, Remote sensing, Reflectivity, Neurons, Coastal modeling, Algorithm development, Ocean optics, Scattering, Absorption

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The estimation of chlorophyll-a is one of the key indices of monitoring the phytoplankton populations. In this paper, an approach for estimating chlorophyll-a concentration using a neural network model is prose. A dat set assembled form various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop containing coincident in-situ chlorophyll and remote sensing reflectance measurements is used to evaluate the efficacy of the proposed neural network model. The data comprises of 919 stations and has chlorophyll-a concentrations ranging between 0.019 and 32.79 (mu) g/l. There are approximately 20 observations form more turbid coastal waters. A feed-forward neural network model with 10 noes in the hidden layer has been constructed to estimate chlorophyll-a concentration. The remote sensing reflectances form five SeaWiFS wavelengths are used as inputs to our model. The network is trained using the Levenberg-Marquardt algorithm. A neural network model can deal with non-linear relationships more accurately. Neural networks can effectively include variables that tend to co-vary non- linearly relationships more accurately. Neural networks can effectively include variables that tend to co-vary non- linearly with the output variable. They are flexible towards the choice of inputs and are tolerant to noise and require no a priori knowledge about the effect of these parameters. This makes them an ideal candidate for estimating chlorophyll-a concentration in coastal waters, where the presence of suspended sediments, detritus, and dissolved organic matter creates an optically complex situation. By allowing the neural network model to include several optical parameters as additional inputs to account for the scattering and absorption phenomena the model can be extended to estimate chlorophyll-a concentration turbid coastal waters.

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