scholarly journals Estimating chlorophyll-A concentration in the Caspian Sea from MODIS images using artificial neural networks

2019 ◽  
Vol 25 (4) ◽  
pp. 515-521
Author(s):  
Siamak Boudaghpour ◽  
Hajar Sadat Alizadeh Moghadam ◽  
Mohammadreza Hajbabaie ◽  
Seyed Hamidreza Toliati
Keyword(s):  
Neural Networks ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Caspian Sea ◽  
Training Data ◽  
The Caspian Sea ◽  
Feed Forward ◽  
Chlorophyll A Concentration ◽  
Hidden Layer ◽  
Mean Square Errors

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1(<math xmlns="http://www.w3.org/1998/Math/MathML"><mfrac><mrow><mi>&#x3BC;</mi><mi>g</mi></mrow><mi>l</mi></mfrac></math>), however, the four-layer NNs proved superior in terms of R<sup>2</sup> and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

scholarly journals A study on distribution of chlorophyll-<I>a</I> in the coastal waters of Anzali Port, south Caspian Sea

2011 ◽  
Vol 8 (1) ◽  
pp. 435-451 ◽  
Author(s):  
S. Jamshidi ◽  
N. Bin Abu Bakar
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Caspian Sea ◽  
Thermal Stratification ◽  
Nutrient Supply ◽  
Sea Surface ◽  
Food Chains ◽  
The Caspian Sea ◽  
Nutrient Enhancement ◽  
Iranian Coast

Abstract. Phytoplankton as chlorophyll-containing organisms is the first step of production in most marine processes and food chains. Nutrient enhancement in the seawater due to the discharge of agricultural, industrial, and urban wastes threatens the Caspian Sea environment. Increasing concentrations of chlorophyll-a in seawater, in reaction to the elevation of nutrient supply can have severely damaging effects on the marine environment of the Caspian. In this research, seasonal variability of the chlorophyll-a concentrations in the western part of the southern coastal waters of the Caspian Sea near Iranian coast was examined using field observations. The data showed that the most chlorophyll-a was found below the sea surface. The thermal stratification in water column and outflow of the Anzali Lagoon affect the chlorophyll-a concentrations in the region. Concentrations of chlorophyll-a were recorded in midsummer in a range of 0.2–3.4 mg m−3.

THE NUMBER AND QUALITATIVE CHARACTERISTICS OF FRY FISH IN SHALLOW COASTAL WATERS OF THE VOLGA IN THE SUMMER-AUTUMN PERIOD OF 2012

2017 ◽  
pp. 65-73
Author(s):  
Edward Vladimirovich Nikitin
Keyword(s):  
Fish Species ◽  
Coastal Waters ◽  
Caspian Sea ◽  
The Caspian Sea ◽  
Volga River ◽  
Commercial Fish ◽  
Autumn Period ◽  
Fish Juveniles ◽  
Qualitative Characteristics ◽  
Shallow Coastal Waters

Shallow coastal waters of the Volga river is a flooded feeding area for fish juveniles of nonmigratory fish species. There takes place annual downstream migration of fluvial anadromous fish species from spawning grounds of the Volga river to the Northern Caspian Sea. The most important factors determining the number and qualitative characteristics of fry fishes are the level of the Caspian Sea (currently having a tendency to the lowering), hydrological and thermal regimes of the Volga river. Researches were carried out in definite periods of time. In the summer-autumn period of 2012 fry fishes were presented by 19 species (13 of them were commercial species), which belonged to 9 families. The article gives data on all the commercial fish species. In the first decade of July the maximum number of fry fish was registered in the western part of the Volga outfall offshore - in box 247 (19.86 mln specimens/km2), in the eastern part - in box 142 (20.4 mln specimens/km2). The most populous were roach, red-eye, silver bream and bream; size-weight characteristics were better in the areas remoted from the Volga delta. In the third decade of July the quantitative indicators of fry fish on these areas decreased, size-weight characteristics greatly increased. In the second decade of October in the western part of the seaside there were registered increased pre-wintering concentrations of fish juveniles, their qualitative indicators increased, which is evidence to favorable feeding conditions in 2012.

scholarly journals Exploiting heterogeneity in operational neural networks by synaptic plasticity

2021 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Junaid Malik ◽  
Habib Ben Abdallah ◽  
Turker Ince ◽  
Alexandros Iosifidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Network Model ◽  
Neuron Model ◽  
Linear Operators ◽  
Training Data ◽  
Learning Performance ◽  
Minimal Network ◽  
Hidden Layer ◽  
Hidden Neurons

AbstractThe recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the “Synaptic Plasticity” paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an “elite” ONN can then be configured using the top-ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result, the performance gap over the CNNs further widens.

Trace element accumulation in fishes collected from coastal waters of the Caspian Sea

2005 ◽  
Vol 51 (8-12) ◽  
pp. 882-888 ◽  
Author(s):  
Yasumi Anan ◽  
Takashi Kunito ◽  
Shinsuke Tanabe ◽  
Igor Mitrofanov ◽  
David G. Aubrey
Keyword(s):  
Trace Element ◽  
Coastal Waters ◽  
Caspian Sea ◽  
The Caspian Sea ◽  
Element Accumulation ◽  
Trace Element Accumulation

scholarly journals Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

2018 ◽  
Vol 13 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Ehsan Jafari Nodoushan ◽  
Jason E. Adolf ◽  
Azizah Abdul Manaf ◽  
Amir Mosavi ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Chlorophyll A Concentration ◽  
Ensemble Models

scholarly journals MICROALGAE FROM THE COASTAL WATERS OF TYULENY ISLAND IN THE CASPIAN SEA

2019 ◽  
Vol 14 (2) ◽  
pp. 120-131
Author(s):  
R. M. Barkhalov ◽  
A. A. Abdurakhmanova ◽  
F. Sh. Amaeva
Keyword(s):  
Coastal Waters ◽  
Caspian Sea ◽  
Biomass Concentration ◽  
Water Area ◽  
Trophic Chain ◽  
The Caspian Sea ◽  
Standard Procedures ◽  
Water Surface Layer ◽  
Rich Flora ◽  
Positive Effect

Aim. In this work, we set out to study the composition of a phytoplankton community in an important fishery area, the coastal water area of Tyuleny Island in the Caspian Sea.Methods. We present the results of seasonal observations (2016) on the state of phytoplankton in the coastal waters of Tyuleny Island in the Caspian Sea. In total, 120 phytoplankton samples were collected at four stations from the water surface layer (May–October) using the Nansen bottle and subsequent fixation by Lugolʹs solution. Sedimentation and concentration were carried out using standard procedures. The samples were processed in the Nageotte chamber with a volume of 0.1 ml under a light microscope.Results. According to the research results (2016), 103 species and varieties of microalgae were found in the phytoplankton samples collected from the water area of Tyuleny Island. The microalgae were represented by four divisions: Bacillariophyta – 49 species, Cyano‐ phyta – 24 species, Chlorophyta – 23 species and Pyrrophyta – 7 species. The greatest species diversity of phytoplankton in the studied water area was noted during the autumn period (61 spe‐ cies). In general, phytoplankton was found to be distributed homogeneously throughout the coastal area of the island, with the biomass concentration not reaching 1 g/m3.Conclusion. In 2016, favorable hydrological and hydrochemical conditions for the development of microalgae were observed. The desalinated water around Tyuleny Island, which is well warmed in the summer and does not freeze in the winter, contributed to the development of rich flora. Although bi‐ omass values were not high due to the prevalence of small‐celled microalgae in phytoplankton, in general, it should have a positive effect on the development of subsequent links in a trophic chain, as well as promote an increase in the productivity of waters of the Northern Caspian Sea. 

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