Time filter

Source Type

Li W.-N.,Pearl River Water Resources Research Institute | Li Z.-Y.,China Earthquake Administration | Chen Z.-Y.,China Earthquake Administration | Zhao B.,China Earthquake Administration
2nd International Conference on Information Science and Engineering, ICISE2010 - Proceedings | Year: 2010

The paper data has been collected in published regional GPS velocity of the results in journals worldwide. Based on this GPS data by different combination of data, established nearly 20 years in uniform reference frame ITRF2005 global level under the velocity field; the velocity field results are in with the global IGS stations show good agreement. © 2010 IEEE.

Guo J.,Huazhong University of Science and Technology | Guo J.,Hubei Key Laboratory of Digital Valley Science and Technology | Guo J.,Hunan Electric Power Test and Research Institute | Zhou J.,Huazhong University of Science and Technology | And 6 more authors.
Water Resources Management | Year: 2013

Practice experience suggests that the traditional calibration of hydrological models with single objective cannot properly measure all of the behaviors of the hydrological system. To circumvent this problem, in recent years, a lot of studies have looked into calibration of hydrological models with multi-objective. In this paper, we propose a novel multi-objective evolution algorithm entitled multi-objective shuffled complex differential evolution (MOSCDE) algorithm, which is an extension of the famous single objective algorithm, shuffled complex evolution (SCE-UA) algorithm, to the multi-objective framework. This new proposed algorithm replaces the simplex search used in SCE-UA with the differential evolution (DE) algorithm and can more thoroughly utilize the information of the individuals in the evolutionary population and improve the search ability of the algorithm. Meanwhile, the Cauchy mutation (CM) operator is employed to prevent the algorithm from falling into the local optimal region of the feasible space. Moreover, two types of archive sets are employed to further improve the performance of the algorithm. The efficacy of the MOSCDE algorithm is first tested on five benchmark problems. After achieving satisfactory performance on the test problems, the MOSCDE is applied to multi-objective parameter optimization of a hydrological model for daily runoff forecasting. The results show that the MOSCDE algorithm can be a viable alternative for multi-objective parameter optimization of hydrological model. © 2013 Springer Science+Business Media Dordrecht.

Guo J.,Huazhong University of Science and Technology | Zhou J.,Huazhong University of Science and Technology | Song L.,Pearl River Water Resources Research Institute | Zou Q.,Huazhong University of Science and Technology | Zeng X.,Huazhong University of Science and Technology
Stochastic Environmental Research and Risk Assessment | Year: 2013

Assessment of parameter and predictive uncertainty of hydrologic models is an essential part in the field of hydrology. However, during the past decades, research related to hydrologic model uncertainty is mostly done with conceptual models. As is accepted that uncertainty in model predictions arises from measurement errors associated with the system input and output, from model structural errors and from problems with parameter estimation. Unfortunately, non-conceptual models, such as black-box models, also suffer from these problems. In this paper, we take the artificial neural network (ANN) rainfall-runoff model as an example, and the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) is employed to analysis the parameter and predictive uncertainty of this model. Furthermore, based on the results of uncertainty assessment, we finally arrive at a simpler incomplete-connection artificial neural network (ICANN) model as well as with better performance compared to original ANN rainfall-runoff model. These results not only indicate that SCEM-UA can be a useful tool for uncertainty analysis of ANN model, but also prove that uncertainty does exist in ANN rainfall-runoff model. Additionally, in some way, it presents that the ICANN model is with smaller uncertainty than the original ANN model. © 2012 Springer-Verlag.

Bi S.,Huazhong University of Science and Technology | Bi S.,Hubei Key Laboratory of Digital Valley Science and Technology | Zhou J.,Huazhong University of Science and Technology | Zhou J.,Hubei Key Laboratory of Digital Valley Science and Technology | And 3 more authors.
Mathematical Problems in Engineering | Year: 2014

A second-order accurate, Godunov-type upwind finite volume method on dynamic refinement grids is developed in this paper for solving shallow-water equations. The advantage of this grid system is that no data structure is needed to store the neighbor information, since neighbors are directly specified by simple algebraic relationships. The key ingredient of the scheme is the use of the prebalanced shallow-water equations together with a simple but effective method to track the wet/dry fronts. In addition, a second-order spatial accuracy in space and time is achieved using a two-step unsplit MUSCL-Hancock method and a weighted surface-depth gradient method (WSDM) which considers the local Froude number is proposed for water depths reconstruction. The friction terms are solved by a semi-implicit scheme that can effectively prevent computational instability from small depths and does not invert the direction of velocity components. Several benchmark tests and a dam-break flooding simulation over real topography cases are used for model testing and validation. Results show that the proposed model is accurate and robust and has advantages when it is applied to simulate flow with local complex topographic features or flow conditions and thus has bright prospects of field-scale application. © 2014 Sheng Bi et al.

Liu Y.,Huazhong University of Science and Technology | Liu Y.,Hubei Engineering University | Zhou J.,Huazhong University of Science and Technology | Song L.,Pearl River Water Resources Research Institute | And 3 more authors.
Natural Hazards and Earth System Sciences | Year: 2014

In recent years, an important development in flood management has been the focal shift from flood protection towards flood risk management. This change greatly promoted the progress of flood control research in a multidisciplinary way. Moreover, given the growing complexity and uncertainty in many decision situations of flood risk management, traditional methods, e.g., tight-coupling integration of one or more quantitative models, are not enough to provide decision support for managers. Within this context, this paper presents a beneficial methodological framework to enhance the effectiveness of decision support systems, through the dynamic adaptation of support regarding the needs of the decision-maker. In addition, we illustrate a loose-coupling technical prototype for integrating heterogeneous elements, such as multi-source data, multidisciplinary models, GIS tools and existing systems. The main innovation is the application of model-driven concepts, which put the system in a state of continuous iterative optimization. We define the new system as a model-driven decision support system (MDSS ). Two characteristics that differentiate the MDSS are as follows: (1) it is made accessible to non-technical specialists; and (2) it has a higher level of adaptability and compatibility. Furthermore, the MDSS was employed to manage the flood risk in the Jingjiang flood diversion area, located in central China near the Yangtze River. Compared with traditional solutions, we believe that this model-driven method is efficient, adaptable and flexible, and thus has bright prospects of application for comprehensive flood risk management.© Author(s) 2014.

Discover hidden collaborations