Application of particle swarm optimization to water management: an introduction and overview.

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.

Hydrodynamic modelling of a flood-prone tidal river using the 1D model MIKE HYDRO River: calibration and sensitivity analysis.

Hydrodynamic modelling is a powerful tool to gain understanding of river conditions. However, as widely known, models vary in terms of how they respond to changes and uncertainty in their input parameters. A hydrodynamic river model (MIKE HYDRO River) was developed and calibrated for a flood-prone tidal river located in South East Queensland, Australia. The model was calibrated using Manning's roughness coefficient for the normal dry and flood periods. The model performance was assessed by comparing observed and simulated water level, and estimating performance indices. Results indicated a satisfactory agreement between the observed and simulated results. The hydrodynamic modelling results revealed that the calibrated Manning's roughness coefficient ranged between 0.011 and 0.013. The impacts of tidal variation at the river mouth and the river discharge from upstream are the major driving force for the hydrodynamic process. To investigate the impacts of the boundary conditions, a new sensitivity analysis approach, based on adding stochastic terms (random noise) to the time series of boundary conditions, was conducted. The main purpose of such new sensitivity analysis was to impose changes in magnitude and time of boundary conditions randomly, which is more similar to the real and natural water level variations compared to impose constant changes of water level. In this new approach, the possible number of variations in simulated results was separately evaluated for both downstream and upstream boundaries under 5%, 10%, and 15% perturbation. The sensitivity analysis results revealed that in the river under study, the middle parts of the river were shown to be more sensitive to downstream boundary condition as maximum water level variations can reach 8%, 12%, and 15% under 5%, 10%, and 15% changes in the downstream boundary, respectively. The outcomes of the present paper will benefit future modelling efforts through provision of a robust tool to enable prediction of water levels at ungauged points of the river under various scenarios of flooding and climate change for the purpose of city planning and decision-making.

Review of climate change impacts on predicted river streamflow in tropical rivers.

Tropical regions are characterized by hydrological extreme events, which are likely to be exacerbated by climate change. Therefore, quantifying the extent to which climate change may damage a hydrological system becomes crucial. This paper aims to evaluate the findings from previous research on projected impacts of climate change on hydrological systems located in regions bounded by the Tropic of Cancer and the Tropic of Capricorn. It intends to provide an in-depth understanding of the climatic conditions, applied approaches, climate change impacts on future streamflow, and measures to reduce prediction uncertainty in the tropics. The review revealed that there is a significant variation in the magnitude of climate change impacts on streamflow in the tropics. The reason for the inconsistent trend prediction is that projections are heavily dependent on the trajectory of greenhouse gas emissions, climate model structural differences, and uncertainty of downscaling methods and hydrological models. Therefore, to minimize the uncertainty and maximize confidence in streamflow projections, it is essential to apply multi-member model ensembles and to clarify the adaptation strategy (coping, adjusting, or transforming).

Application of non-animal-inspired evolutionary algorithms to reservoir operation: an overview.

Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.