Optimal Scheduling of Hydrothermal System with Network and Ramping via SCE-UA Method

Prevention and remediation strategies for groundwater pollution can be successfully carried out if the location, concentration, and release history of contaminants can be accurately identified. This, however, presents a challenge due to complex groundwater systems. To address this issue, a simulation-optimization (S/O) model by integrating MODFLOW and MT3DMS into a shuffled complex evolution (SCE-UA) optimization algorithm was proposed; this coupled model can identify the unknown groundwater pollution source characteristics. Moreover, the Grids Traversal algorithm was used for automatically searching all possible combinations of pollution source location. The performance of the proposed S/O model was tested by three hypothetical scenarios with various combinations of mixed situations (i.e., single and multiple pollution source locations, known and unknown pollution source locations, steady-state flow and transient flow). The field measurement errors was additionally considered and analyzed. Our results showed that this proposed S/O model performed reasonably well. The identified locations and concentrations of contaminants fairly matched with the imposed inputs with average normalized deviations less than 1% after sufficient generations. We further assessed the impact of generation number on the performance of the S/O model. The performance could be significantly improved by increasing generation number, which yet resulted in a heavy computational burden. Furthermore, the proposed S/O model performed more efficiently and robustly than the traditionally used artificial neural network (ANN)-based model. This is due to the internal linkage of numerical simulation in the S/O model that promotes the data exchange from external files to programming variables. This new model allows for solving the source-identification problems considering complex conditions, and thus for providing a platform for groundwater pollution prevention and management.

Section: Sce-ua Algorithmmentioning

confidence: 99%

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Huang

Wang

Zhang

et al. 2018

Comparison of Performance between Genetic Algorithm and SCE-UA for Calibration of SCS-CN Surface Runoff Simulation

Jeon

Park

Engel

2014

Global optimization methods linked with simulation models are widely used for automated calibration and serve as useful tools for searching for cost-effective alternatives for environmental management. A genetic algorithm (GA) and shuffled complex evolution (SCE-UA) algorithm were linked with the Long-Term Hydrologic Impact Assessment (L-THIA) model, which employs the curve number (SCS-CN) method. The performance of the two optimization methods was compared by automatically calibrating L-THIA for monthly runoff from 10 watersheds in Indiana. The selected watershed areas ranged from 32.7 to 5844.1 km 2 . The SCS-CN values and total five-day rainfall for adjustment were optimized, and the objective function used was the Nash-Sutcliffe value (NS value). The GA method rapidly reached the optimal space until the 10th generating population (generation), and after the 10th generation solutions increased dispersion around the optimal space, called a cross hair pattern, because of mutation rate increase. The number of looping executions influenced the performance of model calibration for the SCE-UA and GA method. The GA method performed better for the case of fewer loop executions than the SCE-UA method. For most watersheds, calibration performance using GA was better than for SCE-UA until the 50th generation when the number of model loop executions was OPEN ACCESSWater 2014, 6 3434 around 5150 (one generation has 100 individuals). However, after the 50th generation of the GA method, the SCE-UA method performed better for calibrating monthly runoff compared to the GA method. Optimized SCS-CN values for primary land use types were nearly the same for the two methods, but those for minor land use types and total five-day rainfall for AMC adjustment were somewhat different because those parameters did not significantly influence calculation of the objective function. The GA method is recommended for cases when model simulation takes a long time and the model user does not have sufficient time for an optimization program to search for the best values of calibration parameters. For other cases, the SCE-UA program is recommended for automatic model calibration.

Study on Applicability of Conceptual Hydrological Models for Flood Forecasting in Humid, Semi-Humid Semi-Arid and Arid Basins in China

Kan

Ding

et al. 2017

Flood simulation and forecasting in various types of watersheds is a hot issue in hydrology. Conceptual hydrological models have been widely applied to flood forecasting for decades. With the development of economy, modern China faces with severe flood disasters in all types of watersheds include humid, semi-humid semi-arid and arid watersheds. However, conceptual model-based flood forecasting in semi-humid semi-arid and arid regions is still challenging. To investigate the applicability of conceptual hydrological models for flood forecasting in the above mentioned regions, three typical conceptual models, include Xinanjiang (XAJ), mix runoff generation (MIX) and northern Shannxi (NS), are applied to 3 humid, 3 semi-humid semi-arid, and 3 arid watersheds. The rainfall-runoff data of the 9 watersheds are analyzed based on statistical analysis and information theory, and the model performances are compared and analyzed based on boxplots and scatter plots. It is observed the complexity of drier watershed data is higher than that of the wetter watersheds. This indicates the flood forecasting is harder in drier watersheds. Simulation results indicate all models perform satisfactorily in humid watersheds and only NS model is applicable in arid watersheds. Model with consideration of saturation excess runoff generation (XAJ and MIX) perform better than the infiltration excess-based NS model in semi-humid semi-arid watersheds. It is concluded more accurate mix runoff generation theory, more stable and efficient numerical solution of infiltration equation and rainfall data with higher spatial-temporal resolution are main obstacles for conceptual model-based flood simulation and forecasting.