Optical water quality model of Lake Ontario 1: Determination of the optical cross sections of organic and inorganic particulates in Lake Ontario

1981 ◽  
Vol 20 (9) ◽  
pp. 1696 ◽  
Author(s):  
R. P. Bukata ◽  
J. H. Jerome ◽  
J. E. Bruton ◽  
S. C. Jain ◽  
H. H. Zwick
2012 ◽  
Vol 610-613 ◽  
pp. 1705-1709
Author(s):  
Bing Xiang Liu ◽  
Xiao Liang Lai ◽  
Qun Cao ◽  
Xiang Cheng

Determination of parameters is an important work for establishing the water quality model to perform the mathematical simulation. In this paper, the improved genetic algorithm is applied in evaluation of the parameters of S-P water quality model. This method has overcome the shortages of parameters estimation as before. The computed results have indicated this method with a very high precision and easy realized by the computer.


2001 ◽  
Vol 43 (7) ◽  
pp. 329-338 ◽  
Author(s):  
P. Reichert ◽  
P. Vanrolleghem

State of the art models as used in activated sludge modelling and recently proposed for river water quality modelling integrate the knowledge in a certain field. If applied to data from a specific site, such models are nearly always overparameterised. This raises the question of how many parameters can be fitted in a given context and how to find identifiable parameter subsets given the experimental layout. This problem is addressed for the kinetic parameters of a simplified version of the recently published river water quality model no. 1 (RWQM1). The selection of practically identifiable parameter subsets is discussed for typical boundary conditions as a function of the measurement layout. Two methods for identifiable subset selection were applied and lead to nearly the same results. Assuming upstream and downstream measurements of dissolved substances to be available, only a few (5-8) model parameters appear to be identifiable. Extensive measurement campaigns with dedicated experiments seem to be required for successful calibration of RWQM1. The estimated prior uncertainties of the model parameters are used to estimate the uncertainty of model predictions. Finally an estimate is provided for the maximum possible decrease in prediction uncertainty achievable by a perfect determination of the values of the identifiable model parameters.


Author(s):  
Soobin Kim ◽  
Yong Sung Kwon ◽  
JongChel Pyo ◽  
Mayzonee Ligaray ◽  
Joong-Hyuk Min ◽  
...  

2021 ◽  
Vol 193 (1) ◽  
Author(s):  
Cássia Monteiro da Silva Burigato Costa ◽  
Izabel Rodrigues Leite ◽  
Aleska Kaufmann Almeida ◽  
Isabel Kaufmann de Almeida

2021 ◽  
Vol 13 (1) ◽  
pp. 454-468
Author(s):  
Yumeng Song ◽  
Jing Zhang

Abstract We integrated hyperspectral and field-measured chlorophyll-a (Chl-a) data from the Kristalbad constructed wetland in the Netherlands. We developed a best-fit band ratio empirical algorithm to generate a distribution map of Chl-a concentration (C chla) from SPOT 6 imagery. The C chla retrieved from remote sensing was compared with a water quality model established for a wetland pond system. The retrieved satellite results were combined with a water quality model to simulate and predict the changes in phytoplankton levels. The regression model provides good retrievals for Chl-a. The imagery-derived C chla performed well in calibrating the simulation results. For each pond, the modeled C chla showed a range of values similar to the Chl-a data derived from SPOT 6 imagery (10–25 mg m−3). The imagery-derived and prediction model results could be used as the guiding analytical tools to provide information covering an entire study area and to inform policies.


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