Automatic calibration of the two-dimensional hydrodynamic and water quality model using sequential uncertainty fitting approach.

Reservoir hydrodynamic and water quality modeling, in conjunction with the monitoring programs, is one of the essential tools for controlling the pollution of these types of water bodies. The complexity of the model, data scarcity, and the variable nature of natural phenomena lead to uncertainty in models, which should be considered in the calibration process of these models. Uncertainty-based automatic calibration is one of the methods that can be effective in achieving a high-reliability model.

Handling uncertainty in optimal design of reservoir water quality monitoring systems.

In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed methodology is based on the concept of nonlinear interval number programming and information theory, while handling uncertainties of temperature, reservoir inflow, and inflow constituent concentration. A reference-point-based non-dominated sorting genetic algorithm (NSGA-III) is used to deal with the many-objective optimization problem.

Semivariance analysis and transinformation entropy for optimal redesigning of nutrients monitoring network in San Francisco bay.

This paper introduces a Semivariance-Transinformation (S-T) based method for designing an optimum bay water nutrients monitoring network in San Francisco bay (S.F. bay), USA.

Selective withdrawal optimization in river-reservoir systems; trade-offs between maximum allowable receiving waste load and water quality criteria enhancement.

In this paper, a new systematic approach is designed to maximize the demand coverage and receiving waste load by river-reservoir systems while enhancing water quality criteria. The approach intends to control the reservoir eutrophication while developing a trade-off between the maximum receiving load and shortage on demand coverage. To simulate the system, a hybrid process-based and data-driven model is tailored.

Optimal redesign of groundwater quality monitoring networks: a case study.

Assessment and redesign of water quality monitoring networks is an important task in water quality management. This paper presents a new methodology for optimal redesign of groundwater quality monitoring networks. The measure of transinformation in discrete entropy theory and the transinformation-distance (T-D) curves are used to quantify the efficiency of sampling locations and sampling frequencies in a monitoring network.