Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
An application of multi-objective particle swarm optimization （MOPSO） algorithm for optimization of the hydrological model （HYMOD） is presented in this paper. MOPSO algorithm is used to find non-dominated solution...An application of multi-objective particle swarm optimization （MOPSO） algorithm for optimization of the hydrological model （HYMOD） is presented in this paper. MOPSO algorithm is used to find non-dominated solutions with two objectives： high flow Nash-Sutcliffe efficiency and low flow Nash- Sutcliffe efficiency. The two sets＇ coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms. MOPSO algorithm surpasses multi-objective shuffled complex evolution metcopolis （MOSCEM_UA） algorithr~, in terms of the two sets＇ coverage rate. But when we come to Pareto front spacing rate, the non-dominated solutions of MOSCEM_ UA algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40 000. In addition, there are obvious conflicts between the two objectives. But a compromise solution can be acquired by adopting the MOPSO algorithm.展开更多
In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental ...In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.展开更多
The aim of this paper is to present graphically the behaviour of a simulation model to the varying parameters and to establish the suitability of this representation as a valid tool for the analysis of the same parame...The aim of this paper is to present graphically the behaviour of a simulation model to the varying parameters and to establish the suitability of this representation as a valid tool for the analysis of the same parameters. In this paper, we define parameter combinatorial diagram as the joint graphical representation of all box plots related to the adjustment between real and simulated data, by setting and/or changing the parameters of the simulation model. To do this, we start with a box plot representing the values of an objective adjustment function, achieving these results when varying all the parameters of the simulation model, Then we draw the box plot when setting all the parameters of the model, for example, using the median or average. Later, we get all the box plots when carrying out simulations combining fixed or variable values of the model parameters. Finally, all box plots obtained are represented neatly in a single graph. It is intended that the new parameter combinatorial diagram is used to examine and analyze simulation models useful in practice. This paper presents combinatorial diagrams of different examples of application as in the case of hydrologic models of one, two, three, and five parameters.展开更多
Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bot...Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process,resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve.The classical approaches to global parameter optimization are usually characterized by being time consuming,and having a high computation cost.For this reason,an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed,and applied within this study to optimize hydrological model parameter estimation.Meta-modeling was used to determine the optimization range for all parameters,following which the SCE-UA method was applied to achieve global parameter optimization.The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model.In this study,the daily distributed time-variant gain model(DTVGM) applied to the Huaihe River Basin,China,was chosen as a case study.The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance.The case study shows that the integrated method can efficiently complete the multi-parameter optimization process,and also demonstrates that the method is a powerful tool for efficient parameter optimization.展开更多
Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization...Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization for DHM calibration.Latin Hypercube-once at a time （LH-OAT） was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO（SA-MO）.WetSpa model with non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis （MOSCEM-UA）algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case Ⅰ showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case Ⅱ indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.展开更多
Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tuna...Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.展开更多
复杂物理分布式水文模型计算成本高昂,传统全局优化算法因需大量物理模型运算而难以适用于此类优化问题。以较少次数的物理模型运行寻找最优参数,对于复杂模型的优化迭代求解具有重要意义。本文提出基于大语言模型(Large Language Model...复杂物理分布式水文模型计算成本高昂,传统全局优化算法因需大量物理模型运算而难以适用于此类优化问题。以较少次数的物理模型运行寻找最优参数,对于复杂模型的优化迭代求解具有重要意义。本文提出基于大语言模型(Large Language Models,LLMs)的智能交互式参数优化框架,以HBV和VIC模型为例系统评估了6种主流LLMs在水文模型参数优化中的表现。结果表明:①LLMs凭借对参数物理含义和反馈指标的深度理解,平均仅需45次迭代即可达到95%最优解,显著优于传统算法(100次以上);②LLMs在低中维参数空间(参数数量≤6)表现优异,在高维参数任务中其水文模型参数优化性能衰减严重,但推理型模型展现出更强鲁棒性;③专家知识引导策略下VIC模型平均纳什效率系数较零知识策略提升0.14,上下文记忆机制有效增强了优化稳定性。本文将LLMs引入水文模型参数优化过程,证明LLMs“诊断—反馈—调整”在模型参数优化中的有效性,可为大语言模型赋能科学研究的范式创新提供参考。展开更多
The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort.In this paper,an effective optimization method is proposed to reduce the water contamin...The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort.In this paper,an effective optimization method is proposed to reduce the water contamination on the side windows of automobiles.The accuracy and the efficiency of the numerical simulation are improved by using the lattice Boltzmann method,and the Lagrangian particle tracking method.Optimized parameters are constructed on the basis of the occurrence of the water deposition on a vehicle’s side window.The water contamination area of the side window and the aerodynamic drag are considered simultaneously in the design process;these two factors are used to form the multi-objective optimization function in the genetic algorithm(GA)method.The approximate model,the boundary-seeded domain method,and the GA method are combined in this study to enhance the optimization efficiency.After optimization,the optimal parameters for the A-pillar section are determined by setting the boundary to an area of W=7.77 mm,L=1.27 mm and H=11.22 mm.The side window’s soiling area in the optimized model is reduced by 66.93%,and the aerodynamic drag is increased by 0.41%only,as compared with the original model.It is shown that the optimization method can effectively solve the water contamination problem of side windows.展开更多
Reservoir is an efficient way for flood control and improving all sectors related to water resources in the integrated water resources management.Moreover,multiobjective reservoir plays a significant role in the devel...Reservoir is an efficient way for flood control and improving all sectors related to water resources in the integrated water resources management.Moreover,multiobjective reservoir plays a significant role in the development of a country’s economy especially in developing countries.All multi-objective reservoirs have conflicts and disputes in flood control and water use,which makes the operator a great challenge in the decision of reservoir operation.For improved multi-objective reservoir operation,an integrated modeling system has been developed by incorporating a global optimization system(SCE-UA)into a distributed biosphere hydrological model(WEB-DHM)coupled with the reservoir routing module.The new integrated modeling system has been tested in the Da River subbasin of the Red River and showed the capability of reproducing observed reservoir inflows and optimizing the multi-objective reservoir operation.First,the WEB-DHM was calibrated for the inflows to the Hoa Binh Reservoir in the Da River.Second,the WEB-DHM coupled with the reservoir routing module was tested by simulating the reservoir water level,when using the observed dam outflows as the reservoir release.Third,the new integrated modeling system was evaluated by optimizing the operation rule of the Hoa Binh Reservoir from 1 June to 28 July 2006,which covered the annual largest flood peak.By using the optimal rule for the reservoir operation,the annual largest flood peak at downstream control point(Ben Ngoc station)was successfully reduced(by about 2.4 m for water level and 2500 m^(3)·s^(-1) for discharge);while after the simulation periods,the reservoir water level was increased by about 20 m that could supply future water use.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金NSFC Innovation Team Project,China(NO.50721006)National Key Technologies R&D Program of China during the llth Five-Year Plan Period(NO.2008BAB29B08)
文摘An application of multi-objective particle swarm optimization （MOPSO） algorithm for optimization of the hydrological model （HYMOD） is presented in this paper. MOPSO algorithm is used to find non-dominated solutions with two objectives： high flow Nash-Sutcliffe efficiency and low flow Nash- Sutcliffe efficiency. The two sets＇ coverage rate and Pareto front spacing metric are two criterions to analyze the performance of the algorithms. MOPSO algorithm surpasses multi-objective shuffled complex evolution metcopolis （MOSCEM_UA） algorithr~, in terms of the two sets＇ coverage rate. But when we come to Pareto front spacing rate, the non-dominated solutions of MOSCEM_ UA algorithm are better-distributed than that of MOPSO algorithm when the iteration is set to 40 000. In addition, there are obvious conflicts between the two objectives. But a compromise solution can be acquired by adopting the MOPSO algorithm.
基金supported by the National Basic Research Program of China (the 973 Program,Grant No.2010CB951102)the National Supporting Plan Program of China (Grants No.2007BAB28B01 and 2008BAB42B03)the National Natural Science Foundation of China (Grant No. 50709042),and the Regional Water Theme in the Water for a Healthy Country Flagship
文摘In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.
文摘The aim of this paper is to present graphically the behaviour of a simulation model to the varying parameters and to establish the suitability of this representation as a valid tool for the analysis of the same parameters. In this paper, we define parameter combinatorial diagram as the joint graphical representation of all box plots related to the adjustment between real and simulated data, by setting and/or changing the parameters of the simulation model. To do this, we start with a box plot representing the values of an objective adjustment function, achieving these results when varying all the parameters of the simulation model, Then we draw the box plot when setting all the parameters of the model, for example, using the median or average. Later, we get all the box plots when carrying out simulations combining fixed or variable values of the model parameters. Finally, all box plots obtained are represented neatly in a single graph. It is intended that the new parameter combinatorial diagram is used to examine and analyze simulation models useful in practice. This paper presents combinatorial diagrams of different examples of application as in the case of hydrologic models of one, two, three, and five parameters.
基金supported by the National Natural Science Foundation of China(40901023)the National Basic Research Program of China (2010CB428403)
文摘Parameter optimization of a hydrological model is an indispensable process within model development and application.The lack of knowledge regarding the efficient optimization of model parameters often results in a bottle-neck within the modeling process,resulting in the effective calibration and validation of distributed hydrological models being more difficult to achieve.The classical approaches to global parameter optimization are usually characterized by being time consuming,and having a high computation cost.For this reason,an integrated approach coupling a meta-modeling approach with the SCE-UA method was proposed,and applied within this study to optimize hydrological model parameter estimation.Meta-modeling was used to determine the optimization range for all parameters,following which the SCE-UA method was applied to achieve global parameter optimization.The multivariate regression adaptive splines method was used to construct the response surface as a surrogate model to a complex hydrological model.In this study,the daily distributed time-variant gain model(DTVGM) applied to the Huaihe River Basin,China,was chosen as a case study.The integrated objective function based on the water balance coefficient and the Nash-Sutcliffe coefficient was used to evaluate the model performance.The case study shows that the integrated method can efficiently complete the multi-parameter optimization process,and also demonstrates that the method is a powerful tool for efficient parameter optimization.
基金National Basic Research Program(973)of China(No.2010CB951102)Innovative Research Groups of the National Natural Science Foundation,China(No.51021006)National Natural Science Foundation of China(No.51079028)
文摘Increasing complexity of distributed hydrological model （DHM） has lowered the efficiency of convergence.In this study,global sensitivity analysis （SA） was introduced by combining multiobjective （MO） optimization for DHM calibration.Latin Hypercube-once at a time （LH-OAT） was adopted in global parameter SA to obtain relative sensitivity of model parameter,which can be categorized into different sensitivity levels.Two comparative study cases were conducted to present the efficiency and feasibility by combining SA with MO（SA-MO）.WetSpa model with non-dominated sorting genetic algorithm-Ⅱ （NSGA-Ⅱ） algorithm and EasyDHM model with multi-objective sequential complex evolutionary metropolis-uncertainty analysis （MOSCEM-UA）algorithm were adopted to demonstrate the general feasibility of combining SA in optimization.Results showed that the LH-OAT was globally effective in selecting high sensitivity parameters.It proves that using parameter from high sensitivity groups results in higher convergence efficiency.Study case Ⅰ showed a better Pareto front distribution and convergence compared with model calibration without SA.Study case Ⅱ indicated a more efficient convergence of parameters in sequential evolution of MOSCEM-UA under the same iteration.It indicates that SA-MO is feasible and efficient for high dimensional DHM calibration.
基金supported by the Hunan Provincial Natural Science Foundation of China(No.2023JJ40686).
文摘Enhancing the adaptability of Unmanned Aerial Vehicle(UAV)swarm control models to cope with different complex working scenarios is an important issue in this research field.To achieve this goal,control model with tunable parameters is a widely adopted approach.In this article,an improved UAV swarm control model with tunable parameters namely Multi-Objective O-Flocking(MO O-Flocking)is proposed.The MO O-Flocking model is a combination of a multi rule control system and a virtual-physical-law based control model with tunable parameters.To achieve multi-objective parameter tuning,a multi-objective parameter tuning method namely Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2)is designed.Simulation experiment scenarios include six target orientation scenarios with different kinds of objectives.Experimental results show that both the ISPEA2 algorithm and MO O-Flocking control model have good performance in their experiment scenarios.
文摘复杂物理分布式水文模型计算成本高昂,传统全局优化算法因需大量物理模型运算而难以适用于此类优化问题。以较少次数的物理模型运行寻找最优参数,对于复杂模型的优化迭代求解具有重要意义。本文提出基于大语言模型(Large Language Models,LLMs)的智能交互式参数优化框架,以HBV和VIC模型为例系统评估了6种主流LLMs在水文模型参数优化中的表现。结果表明:①LLMs凭借对参数物理含义和反馈指标的深度理解,平均仅需45次迭代即可达到95%最优解,显著优于传统算法(100次以上);②LLMs在低中维参数空间(参数数量≤6)表现优异,在高维参数任务中其水文模型参数优化性能衰减严重,但推理型模型展现出更强鲁棒性;③专家知识引导策略下VIC模型平均纳什效率系数较零知识策略提升0.14,上下文记忆机制有效增强了优化稳定性。本文将LLMs引入水文模型参数优化过程,证明LLMs“诊断—反馈—调整”在模型参数优化中的有效性,可为大语言模型赋能科学研究的范式创新提供参考。
基金Project supported by the National Science Foundation of China(Grant No.51875238).
文摘The water contamination on the side windows of moving vehicles is a crucial issue in improving the driving safety and the comfort.In this paper,an effective optimization method is proposed to reduce the water contamination on the side windows of automobiles.The accuracy and the efficiency of the numerical simulation are improved by using the lattice Boltzmann method,and the Lagrangian particle tracking method.Optimized parameters are constructed on the basis of the occurrence of the water deposition on a vehicle’s side window.The water contamination area of the side window and the aerodynamic drag are considered simultaneously in the design process;these two factors are used to form the multi-objective optimization function in the genetic algorithm(GA)method.The approximate model,the boundary-seeded domain method,and the GA method are combined in this study to enhance the optimization efficiency.After optimization,the optimal parameters for the A-pillar section are determined by setting the boundary to an area of W=7.77 mm,L=1.27 mm and H=11.22 mm.The side window’s soiling area in the optimized model is reduced by 66.93%,and the aerodynamic drag is increased by 0.41%only,as compared with the original model.It is shown that the optimization method can effectively solve the water contamination problem of side windows.
基金This study was funded by the Japan Aerospace Exploration AgencyThe second author is also supported by grants from the Asian Development Bank(ADB).
文摘Reservoir is an efficient way for flood control and improving all sectors related to water resources in the integrated water resources management.Moreover,multiobjective reservoir plays a significant role in the development of a country’s economy especially in developing countries.All multi-objective reservoirs have conflicts and disputes in flood control and water use,which makes the operator a great challenge in the decision of reservoir operation.For improved multi-objective reservoir operation,an integrated modeling system has been developed by incorporating a global optimization system(SCE-UA)into a distributed biosphere hydrological model(WEB-DHM)coupled with the reservoir routing module.The new integrated modeling system has been tested in the Da River subbasin of the Red River and showed the capability of reproducing observed reservoir inflows and optimizing the multi-objective reservoir operation.First,the WEB-DHM was calibrated for the inflows to the Hoa Binh Reservoir in the Da River.Second,the WEB-DHM coupled with the reservoir routing module was tested by simulating the reservoir water level,when using the observed dam outflows as the reservoir release.Third,the new integrated modeling system was evaluated by optimizing the operation rule of the Hoa Binh Reservoir from 1 June to 28 July 2006,which covered the annual largest flood peak.By using the optimal rule for the reservoir operation,the annual largest flood peak at downstream control point(Ben Ngoc station)was successfully reduced(by about 2.4 m for water level and 2500 m^(3)·s^(-1) for discharge);while after the simulation periods,the reservoir water level was increased by about 20 m that could supply future water use.
