Model Parameter Optimization Method Research in Heihe River Open Modeling Environment (HOME)

2017 ◽  
Vol 31 (09) ◽  
pp. 1759017 ◽  
Author(s):  
Jiuyuan Huo ◽  
Yaonan Zhang ◽  
Lihui Luo ◽  
Yinping Long ◽  
Zhengfang He ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Hydrological Model ◽  
Optimization Method ◽  
Heihe River ◽  
Optimization Strategy ◽  
Runoff Simulation ◽  
Simulation Accuracy ◽  
Model Parameter ◽  
Modeling Environment ◽  
Model Parameter Optimization

How to make the existing models from different disciplines effectively interoperate and integrate is one of the primary challenges for scientists and decision-makers. Heihe river Open Modeling Environment (HOME) provides a convenient model coupling platform that enables researchers concentrate on the theory and applications of ecological and hydrological watershed models. The model parameter optimization is an important component and key step that links models and simulation of watershed. In this paper, through integration modules of existing models, an improved ABC algorithm (ORABC) based on optimization strategy and reservation strategy of the best individuals was introduced into HOME as a hydrological model parameter optimization module, and coupled with the Xinanjiang hydrological model to complete automatically task of model parameter optimization. The runoff simulation experiments in Heihe river watershed were taken to verify the parameter optimization in HOME, and the simulation results testified the efficiency and effectiveness of the method. It can significantly improve simulation accuracy and efficiency of hydrological and ecological models, and promote the scientific researches for watershed issues.

scholarly journals Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Chao Zhang ◽  
Ru-bin Wang ◽  
Qing-xiang Meng
Keyword(s):  
Global Optimization ◽  
Parameter Optimization ◽  
Hydrological Model ◽  
Optimization Method ◽  
Difficult Problem ◽  
Rainfall Runoff ◽  
True Parameter ◽  
Shuffled Complex Evolution ◽  
Structure Lead ◽  
Parameter Values

Parameter optimization for the conceptual rainfall-runoff (CRR) model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the Xinanjiang (XAJ) model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years) and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.

scholarly journals Evaluation Method of Multiobjective Functions’ Combination and Its Application in Hydrological Model Evaluation

2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Jiuyuan Huo ◽  
Liqun Liu
Keyword(s):  
Objective Function ◽  
Parameter Optimization ◽  
Optimization Problem ◽  
Hydrological Model ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Hydrological Forecasting ◽  
Model Parameter Optimization

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions’ combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions’ combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

Marginal DCS events: their relation to decompression and use in DCS models

2009 ◽  
Vol 107 (5) ◽  
pp. 1539-1547 ◽  
Author(s):  
Laurens E. Howle ◽  
Paul W. Weber ◽  
Richard D. Vann ◽  
Mark C. Campbell
Keyword(s):  
Parameter Optimization ◽  
Probabilistic Model ◽  
Decompression Sickness ◽  
Trial Data ◽  
Model Optimization ◽  
Model Parameter ◽  
Likelihood Functions ◽  
Combining Data ◽  
Model Predictions ◽  
Model Parameter Optimization

We consider the nature and utility of marginal decompression sickness (DCS) events in fitting probabilistic decompression models to experimental dive trial data. Previous works have assigned various fractional weights to marginal DCS events, so that they contributed to probabilistic model parameter optimization, but less so than did full DCS events. Inclusion of fractional weight for marginal DCS events resulted in more conservative model predictions. We explore whether marginal DCS events are correlated with exposure to decompression or are randomly occurring events. Three null models are developed and compared with a known decompression model that is tuned on dive trial data containing only marginal DCS and non-DCS events. We further investigate the technique by which marginal DCS events were previously included in parameter optimization, explore the effects of fractional weighting of marginal DCS events on model optimization, and explore the rigor of combining data containing full and marginal DCS events for probabilistic DCS model optimization. We find that although marginal DCS events are related to exposure to decompression, empirical dive data containing marginal and full DCS events cannot be combined under a single DCS model. Furthermore, we find analytically that the optimal weight for a marginal DCS event is 0. Thus marginal DCS should be counted as no-DCS events when probabilistic DCS models are optimized with binomial likelihood functions. Specifically, our study finds that inclusion of marginal DCS events in model optimization to make the dive profiles more conservative is counterproductive and worsens the model's fit to the full DCS data.

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