scholarly journals Selection of Genetic Algorithm Operators and Application for Calibration of Dissolved Oxygen Simulation in the River Water Quality Model

10.5109/9334 ◽  
2007 ◽  
Vol 52 (2) ◽  
pp. 439-444
Author(s):  
Nguyen Van Tuan ◽  
Ken Mori ◽  
Yasumaru Hirai
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Selection Of

River Water Quality Model no. 1 (RWQM1): III. Biochemical submodel selection

2001 ◽  
Vol 43 (5) ◽  
pp. 31-40 ◽  
Author(s):  
P. Vanrolleghem ◽  
D. Borchardt ◽  
M. Henze ◽  
W. Rauch ◽  
P. Reichert ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Conversion Model ◽  
General Rules ◽  
Model Component ◽  
Model Features ◽  
Selection Of

The new River Water Quality Model no.1 introduced in the two accompanying papers by Shanahan et al. and Reichert et al. is comprehensive. Shanahan et al. introduced a six-step decision procedure to select the necessary model features for a certain application. This paper specifically addresses one of these steps, i.e. the selection of submodels of the comprehensive biochemical conversion model introduced in Reichert et al. Specific conditions for inclusion of one or the other conversion process or model component are introduced, as are some general rules that can support the selection. Examples of simplified models are presented.

Identifiability and uncertainty analysis of the River Water Quality Model No. 1 (RWQM1)

2001 ◽  
Vol 43 (7) ◽  
pp. 329-338 ◽  
Author(s):  
P. Reichert ◽  
P. Vanrolleghem
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Model Parameters ◽  
Water Quality Modelling ◽  
Quality Model ◽  
Model Predictions ◽  
Selection Of

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.

Parameter identification of river water quality models using a genetic algorithm

2013 ◽  
Vol 69 (4) ◽  
pp. 687-693 ◽  
Author(s):  
Xiaodong Liu ◽  
Yuanyuan Zhou ◽  
Zulin Hua ◽  
Kejian Chu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Parameter Identification ◽  
River Water ◽  
Unsteady Flows ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Quality Models ◽  
Water Quality Models

For solving the multi-parameter identification problem of a river water quality model, analytical methods for solving a river water quality model and traditional optimization algorithms are very difficult to implement. A new parameter identification model based on a genetic algorithm (GA) coupled with finite difference method (FDM) was constructed for the determination of hydraulic and water quality parameters such as the longitudinal dispersion coefficient, the pollutant degradation coefficient, velocity, etc. In this model, GA is improved to promote convergence speed by adding the elite replacement operator after the mutation operator, and FDM is applied for unsteady flows. Moreover the influence of observation noise on identified parameters was discussed for the given model. The method was validated by two numerical cases (in steady and unsteady flows respectively) and one practical application. The computational results indicated that the model could give good identification precision results and showed good anti-noise abilities for water quality models when the noise level ≤10%.

scholarly journals Water Quality Modeling of Bhima River by using HEC-RAS Software

2020 ◽  
Vol 9 (3) ◽  
pp. 2886-2889
Keyword(s):  
Water Quality ◽  
River Water ◽  
Hydrodynamic Model ◽  
Algal Blooms ◽  
Agricultural Runoff ◽  
Water Quality Modeling ◽  
Water Quality Model ◽  
River Water Quality ◽  
Water Levels ◽  
Quality Model

The River has got religious importance in India. The Bhima River is beginning from Bhimashankar hill and it flows through some parts of Maharashtra and Karnataka state. The assessment of water quality for the development of the places near the bank of River is important. These is controlled by various manmade activities. The quality of river water resources is facing problems because of the continuous agricultural runoff, development and urbanization. Due to mixing of nutrients causes algal blooms, which results eutrophication. The modeling of water quality can be deliberated as useful tool for assessing river water. Bhima River is demarcated as a major and important water body located in Pandharpur, dist. Solapur, Maharashtra. As Pandharpur is having historical background and known as one of the famous Holly places in Maharashtra, this place is facing huge population fluctuation due to migrated pilgrims and rapid growth of urbanization. These two things detrimentally affect River water quality. The main objective of current study was to develop a hydrodynamic model combined with river water quality model for the Bhima River to measure and recognize the processes harmful for the River. For Bhima River a hydrodynamic model was constructed using the HEC-RAS 4.1 software combined with a river water quality model to estimate the amount, distribution and sources of algae, nitrate and temperature. The river model has standardized with the help of previous water levels near the Pandharpur region. It has standardized and calibrated for the assessed parameters by competing them with the present data. The result showed a relationship between DO and temperature range. DO level in Pandharpur and Gopalpur were observed to be fluctuating with respective temperature and during Vari season. However, wastewater discharge from Nalha in sample station 3 i.e. Goplapur shows slit changes in DO and due to this there is necessity to learn other parameters also.

Identifiability analysis of the CSTR river water quality model

2006 ◽  
Vol 53 (1) ◽  
pp. 93-99 ◽  
Author(s):  
J. Chen ◽  
Y. Deng
Keyword(s):  
Water Quality ◽  
River Water ◽  
Sampling Frequency ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Identifiability Analysis ◽  
Quality Models ◽  
Observational Errors ◽  
Water Quality Models

Conceptual river water quality models are widely known to lack identifiability. The causes for that can be due to model structure errors, observational errors and less frequent samplings. Although significant efforts have been directed towards better identification of river water quality models, it is not clear whether a given model is structurally identifiable. Information is also limited regarding the contribution of different unidentifiability sources. Taking the widely applied CSTR river water quality model as an example, this paper presents a theoretical proof that the CSTR model is indeed structurally identifiable. Its uncertainty is thus dominantly from observational errors and less frequent samplings. Given the current monitoring accuracy and sampling frequency, the unidentifiability from sampling frequency is found to be more significant than that from observational errors. It is also noted that there is a crucial sampling frequency between 0.1 and 1 day, over which the simulated river system could be represented by different illusions and the model application could be far less reliable.

scholarly journals Extension of the River Water Quality Model No. 1 with the fate of pesticides

2011 ◽  
Vol 14 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Veerle C. J. De Schepper ◽  
Katrijn M. A. Holvoet ◽  
Lorenzo Benedetti ◽  
Piet Seuntjens ◽  
Peter A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Water Column ◽  
River Water ◽  
Bulk Water ◽  
Transport Processes ◽  
Water Quality Model ◽  
River Water Quality ◽  
Model Parameters ◽  
Quality Model ◽  
Run Off

The existing River Water Quality Model No. 1 (RWQM1) was extended with processes determining the fate of non-volatile pesticides in the water phase and sediments. The exchange of pesticides between the water column and the sediment is described by three transport processes: diffusion, sedimentation and resuspension. Burial of sediments is also included. The modified model was used to simulate the concentrations of diuron and chloridazon in the river Nil. A good agreement was found between the simulated pesticide concentrations and measured values resulting from a four-month intensive monitoring campaign. The simulation results indicate that pesticide concentrations in the bulk water are not sensitive to the selected biochemical model parameters. It seems that these concentrations are mainly determined by the imposed upstream concentrations, run-off and direct losses. The high concentrations in the bulk water were not observed in the sediment pore water due to a limited exchange between the water column and the sediment. According to a sensitivity analysis, the observed pesticide concentrations are highly sensitive to the diffusion and sorption coefficients. Therefore, model users should determine these parameters with accuracy in order to reduce the degree of uncertainty in their results.

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