Pictorial Simulation Applied to Water Quality Modeling

1991 ◽  
Vol 24 (6) ◽  
pp. 275-281 ◽  
Author(s):  
A. S. Câmara ◽  
F. C. Ferreira ◽  
J. E. Fialho ◽  
E. Nobre
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Simulation Models ◽  
Water Quality Modeling ◽  
Waste Stabilization Ponds ◽  
Future Developments ◽  
Waste Stabilization ◽  
Quality Modeling ◽  
Potential Applications

Pictorial simulation models considering pictorial entities and operations are introduced. Pictorial entities are defined by their shape, size, color and position. Pictorial operators include reproduction (copy of a pictorial entity), mutation (expansion, rotation, translation, change in color), fertile encounters (intersection, reunion) and sterile encounters (absorption). Pictorial simulation is applied to two water quality management problems to illustrate its potential applications: oil spill accidents and waste stabilization ponds ecology. Future developments tied to cellular automata modeling are also discussed.

scholarly journals Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide

2019 ◽  
Vol 36 ◽  
pp. 39-48 ◽  
Author(s):  
Ting Tang ◽  
Maryna Strokal ◽  
Michelle T.H. van Vliet ◽  
Piet Seuntjens ◽  
Peter Burek ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Local Scale ◽  
Water Quality Modeling ◽  
Quality Modeling

Innovative Tools for Water Quality Management and Policy Analysis: Desert and Streamplan

1999 ◽  
Vol 40 (10) ◽  
pp. 103-110
Author(s):  
Carlo De Marchi ◽  
Pavel Ivanov ◽  
Ari Jolma ◽  
Ilia Masliev ◽  
Mark Griffin Smith ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Policy Analysis ◽  
River Basin ◽  
Emission Reduction ◽  
Water Quality Management ◽  
Water Quality Modeling ◽  
Detailed Model ◽  
Basin Scale ◽  
Alternative Water

This paper presents the major features of two decision support systems (DSS) for river water quality modeling and policy analysis recently developed at the International Institute of Applied Systems Analysis (IIASA), DESERT and STREAMPLAN. DESERT integrates in a single package data management, model calibration, simulation, optimization and presentation of results. DESERT has the flexibility to allow the specification of both alternative water quality models and flow hydraulics for different branches of the same river basin. Specification of these models can be done interactively through Microsoft® Windows commands and menus and an easy to use interpreted language. Detailed analysis of the effects of parameter uncertainty on water quality results is integrated into DESERT. STREAMPLAN, on the other hand, is an integrated, easy-to-use software system for analyzing alternative water quality management policies on a river basin level. These policies include uniform emission reduction and effluent standard based strategies, ambient water quality and least-cost strategies, total emission reduction under minimized costs, mixed strategies, local and regional policies, and strategies with economic instruments. A distinctive feature of STREAMPLAN is the integration of a detailed model of municipal wastewater generation with a water quality model and policy analysis tools on a river basin scale.

Process-based or Probabilistic Models?

2020 ◽  
Author(s):  
Craig Stow
Keyword(s):  
Water Quality ◽  
Analytical Solutions ◽  
Probabilistic Models ◽  
Likelihood Function ◽  
Simulation Models ◽  
Water Quality Modeling ◽  
Process Models ◽  
Model Parameters ◽  
Bayesian Approaches ◽  
Quality Modeling

<p>The historical adoption of Bayesian approaches was limited by two main impediments: 1) the requirement for subjective prior information, and 2) the unavailability of analytical solutions for all but a few simple model forms. However, water quality modeling has always been subjective; selecting point values for model parameters, undertaking some “judicious diddling” to adjust them so that model output more closely matches observed data, and declaring the model to be “reasonable” is a long-standing practice. Water quality modeling in a Bayesian framework can actually reduce this subjectivity as it provides a rigorous and transparent approach for model parameter estimation. The second impediment, lack of analytical solutions, has for many applications, been largely reduced by the increasing availability of fast, cheap computing and concurrent evolution of efficient algorithms to sample the posterior distribution. In water quality modeling, however, the increasing computational availability may be reinforcing the dichotomy between probabilistic and “process-based” models. When I was a graduate student we couldn’t do both process and probability because computers weren’t fast enough. However, current computers unimaginably faster and we still rarely do both. It seems that our increasing computational capacity has been absorbed either in more complex and highly resolved, but still deterministic, process models, or more structurally complex probabilistic models (like hierarchical models) that are still light process. In principal, Bayes Theorem is quite general; any model could constitute the likelihood function, but practically, running Monte Carlo-based methods on simulation models that require hours, days, or even longer to run is not feasible. Developing models that capture the essential (and best understood processes) and that still allow a meaningful uncertainty analysis is an area that invites renewed attention.</p>

scholarly journals Water quality modeling of the São Joaquim stream, Brazil

2019 ◽  
Vol 41 ◽  
pp. 14
Author(s):  
Julio Cesar de Souza Inácio Gonçalves ◽  
Murilo Senhuki Esposto
Keyword(s):  
Water Quality ◽  
Uncertainty Analysis ◽  
Flow Reactor ◽  
Water Quality Management ◽  
Plug Flow ◽  
Domestic Wastewater ◽  
Water Quality Modeling ◽  
Modeling Process ◽  
Quality Modeling ◽  
Electronic Spreadsheet

Water quality modeling is applied as a supporting tool for water quality management. It is useful in identifying environmental impacts from pollutants discharged into rivers and in predicting self-depuration capacity. This study aimed to simulate the water quality along a stretch in São Joaquim stream basin, in order to identify the main polluting sources in the stream and to propose measures to control pollution. The mathematical model, based on the mass balance in plug flow reactor, was implemented in an electronic spreadsheet. The modeling process involved the following stages: collecting the input data, calibration, sensitivity analysis, uncertainty analysis, and the generation of the scenarios. The calibration of the model has generated r2 above 0.68, and it was the indication that the model can explain most of the variance found in the measured data. The wastewater and the stream flow were considered the most sensitive parameters in the model. The uncertainty analysis has shown the probability of the dissolved-oxygen to be higher than or equal to 2 mg L-1, the minimum value allowed for the class 4, is 5.3 %. The main pollution sources in stream are the discharge of untreated domestic wastewater from São Joaquim City, and the surface runoff from the agricultural area. The study has shown that a wastewater treatment station must installed in the basin, in order to remove at least 93% of the organic matter currently discharged in the stream.

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