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

2002 ◽  
Author(s):  
Mohamad T. Musavi ◽  
Richard L. Miller ◽  
Habtom Ressom ◽  
Padma Natarajan
Keyword(s):  
Neural Network ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Chlorophyll A Concentration

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

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

Assessment and analysis of the chlorophyll-a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002–2012)

2017 ◽  
Vol 190 ◽  
pp. 217-232 ◽  
Author(s):  
Hubert Loisel ◽  
Vincent Vantrepotte ◽  
Sylvain Ouillon ◽  
Dat Dinh Ngoc ◽  
Marine Herrmann ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Ocean Color ◽  
Chlorophyll A Concentration ◽  
Color Sensor

scholarly journals VARIABILITY OF CHLOROPHYLL-a CONCENTRATION OF THE NORTHERN SUMBAWA WATERS BASED ON Sea WiFS SATELLITE DATA

2009 ◽  
Vol 1 (2) ◽  
Author(s):  
Bisman Nababan ◽  
Diki Zulkarnaen ◽  
Jonson Lumban Gaol
Keyword(s):  
Chlorophyll A ◽  
Satellite Data ◽  
Coastal Waters ◽  
Low Temperatures ◽  
Water Mass ◽  
The West ◽  
Chlorophyll A Concentration ◽  
Transition Season ◽  
Goddard Space Flight Center ◽  
Second Peak

<p>Variability of chlorophyll-a concentrations of the northern Sumbawa waters was investigated based on SeaWiFS satellite data for the period of January 1998-December 2007. Chlorophyll-aconcentration was estimated employing OC4v4 algorithm. Chlorophyll-a concentrations of,SeaWiFS satellite data were obtained from the Goddard Space Flight Center, NASA archieve data. In general, fluctuations of chlorophyll-a concentration of the northern Sumbawa waters had three patterns i.e., the maximum with a range of 0,21-0,74 mg/m3 occurred during the West Season (November-February), the minimum with a range of 0.12-0.15 mg/m3 occurred during Transition Season I (March-April), and relatively high (second peak) with a range of 0.21-0.36 mg/m3 occurred during the mid-East Season until the beginning of Transition Season II (July-September).High chlorophyll-a concentration occurred during the West Season was closely related to the high rainfall, the possibility of vertical water mass mixing,and upwelling process in the northern coastal waters of Sumbawa. Meanwhile, the relatively high (second peak) of chlorophyll-aconcentration occurred in July-September was caused by the movement of water masses from the South of Makassar Strait containing relatively high chlorophyll-a concentrations and relatively low temperatures since upwelling processes occurred at this location in the same period.</p> <p>Keywords:Chlorophyll-a,northern Sumbawa waters, SeaWiFS, OC4v4, upwelling</p> <p> </p>

Estimation of chlorophyll-a concentration in complex coastal waters from satellite imagery

2021 ◽  
Author(s):  
Alexander Gilerson ◽  
Mateusz Malinowski ◽  
Eder Herrera ◽  
Michelle C. Tomlinson ◽  
Richard P. Stumpf ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Satellite Imagery ◽  
Coastal Waters ◽  
Chlorophyll A Concentration
Sign in / Sign up

Export Citation Format

Share Document