Experience of model integration and Pareto frontier visualization in the search for preferable water quality strategies

2005 ◽  
Vol 20 (2) ◽  
pp. 243-260 ◽  
Author(s):  
A.V. Lotov ◽  
L.V. Bourmistrova ◽  
R.V. Efremov ◽  
V.A. Bushenkov ◽  
A.L. Buber ◽  
...  
Keyword(s):  
Water Quality ◽  
Pareto Frontier ◽  
Model Integration ◽  
Pareto Frontier Visualization ◽  
Frontier Visualization

scholarly journals Designing an Optimized Water Quality Monitoring Network with Reserved Monitoring Locations

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 713 ◽  
Author(s):  
Xiaohui Zhu ◽  
Yong Yue ◽  
Prudence Wong ◽  
Yixin Zhang ◽  
Hao Ding
Keyword(s):  
Water Quality ◽  
Detection Probability ◽  
Pareto Frontier ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Detection Time ◽  
Monitoring Networks ◽  
Pollution Detection ◽  
Potential Monitoring

The optimized design of water quality monitoring networks can not only minimize the pollution detection time and maximize the detection probability for river systems but also reduce redundant monitoring locations. In addition, it can save investments and costs for building and operating monitoring systems as well as satisfy management requirements. This paper aims to use the beneficial features of multi-objective discrete particle swarm optimization (MODPSO) to optimize the design of water quality monitoring networks. Four optimization objectives: minimum pollution detection time, maximum pollution detection probability, maximum centrality of monitoring locations and reservation of particular monitoring locations, are proposed. To guide the convergence process and keep reserved monitoring locations in the Pareto frontier, we use a binary matrix to denote reserved monitoring locations and develop a new particle initialization procedure as well as discrete functions for updating particle’s velocity and position. The storm water management model (SWMM) is used to model a hypothetical river network which was studied in the literature for comparative analysis of our work. We define three pollution detection thresholds and simulate pollution events respectively to obtain all the pollution detection time for all the potential monitoring locations when a pollution event occurs randomly at any potential monitoring locations. Compared to the results of an enumeration search method, we confirm that our algorithm could obtain the Pareto frontier of optimized monitoring network design, and the reserved monitoring locations are included to satisfy the management requirements. This paper makes fundamental advancements of MODPSO and enables it to optimize the design of water quality monitoring networks with reserved monitoring locations.

Towards improving the Pareto frontier in environmental models using multi-dataset calibrations coupled with metaheuristic methods

2021 ◽  
Author(s):  
David C. Finger ◽  
Anna E. Sikorska-Senoner
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Model Calibration ◽  
Pareto Frontier ◽  
Model Performance ◽  
Model Parameters ◽  
Hydrological Models ◽  
Quality Models ◽  
Environmental Models ◽  
Water Quality Models

<p>Environmental models, such as hydrological models or water quality models, are incorporate numerical algorithms that describe either empirically or physical-based the large variety of natural processes that govern the flow of water (or other variables) and its components. The purposes of these models range from improving our understanding of the principles of hydrological processes at a catchment scale to making predictions about how anthropogenic activities will influence future water resources. To be applicable, these models require calibration with observed output data, which is most often streamflow for hydrological models. Yet, the complex nature of hydrological processes, on the one hand, and the limited observed data to inform model parameters, on the other hand, evoke the unavoidable equifinality issue in the calibration of these models. This equifinality issue is expressed with the presence of several optimal model parameters that have different values but lead to similar model performance. One way of dealing with this issue is through providing a parameter ensemble with optimal solutions instead of a single parameter set, reported often as parametric model uncertainty.</p><p>However, this equifinality issue is far from being solved, as also highlighted by one of 23 Unsolved Problems in Hydrology (UPH). This is particularly the case if more variables than only streamflow are of interest. Our hypothesis is that using more than one dataset for calibrating any environmental model helps reducing the equifinality issue during model calibration and thus improves the identifiability of model parameters. In this review-based study, we present recent examples of hydrological (and water quality) models from literature that have been calibrated within a multiple dataset framework to reduce the equifinality issue. We demonstrate that a multi-dataset calibration yields a better model performance regardless of the complexity of the model. Finally, we show that coupling a multi-dataset model calibration with metaheuristics (such as Monte Carlo or Genetic Algorithm) can help reducing the equifinality of model parameters and improving the Pareto frontier. At the bottom of this study, we outline how such a multi-dataset calibration can lead to better model predictions and how it can help emerging water resources problems due to an emerging climate crisis.</p><p>This work contributes to one of the seven major themes of 23 UPH, i.e., Modelling methods. It paths a way forward towards reducing parameter uncertainty in hydrological predictions (UPH question #20) and thus towards improving modelling of hydrologic responses in the extrapolation phase, i.e., under changed catchment conditions (UPH question #19).</p>

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