China
Africa
Explore chapters and articles related to this topic
Hydraulic modelling of steady state and transients
Published in Bogumil Ulanicki, Kalanithy Vairavamoorthy, David Butler, Peter L.M. Bounds, Fayyaz Ali Memon, Water Management Challenges in Global Change, 2020
Bogumil Ulanicki, Kalanithy Vairavamoorthy, David Butler, Peter L.M. Bounds, Fayyaz Ali Memon
Sampling of the roughness PDF provides a vector of possible friction coefficients that are then used by a hydraulic model in the Monte Carlo framework to generate a corresponding matrix of head values at the measurement sites. For example, a sample of 1000 roughness values furnishes an m x l000 nodal head matrix, where m is the number of measurement nodes. The resulting entities then provide EPR with the data points necessary to construct candidate symbolic regression equations relating conductivity to observed pressure head. These are then ranked according to a prioritization trade-off between accuracy (such as sum of squared errors, SSE) and parsimony. Typically, validation of the approach would derive from actual field data. For the purpose of this preliminary exposition, however, synthetic data is used for both the determination of the EPR-retumed roughness formulas and method Verification. Thus, sampling of possible roughness values is undertaken to produce two data streams, one for the training and the other for the test set.
Definitions and Classifications
Published in Malchus B. Baker, Peter F. Ffolliott, Leonard F. DeBano, Daniel G. Neary, Riparian Areas of the Southwestern United States, 2003
Leonard F. DeBano, Larry J. Schmidt
Geographic Information Systems (GIS) and remote sensing and mapping techniques can provide a large amount of data before beginning field validation.22 Special sampling techniques and analysis can be developed to adequately represent and interpret narrow, variable-width gallery forests having steep environmental gradi- ents.23 However, on-the-ground information must be collected using field inventory and measurement techniques specific to the classification criteria established. Field data can then be analyzed using appropriate statistical and mathematical techniques. GIS can be used for hydrological modeling of riparian areas in addition to being used as a classification tool.12
Detection of abandoned mine workings at KCGM’s open pit operations
Published in T. Szwedzicki, Geotechnical Instrumentation and Monitoring in Open Pit and Underground Mining, 2020
The transmitter and receiver are linked with fibre optic cables to signal recording and storage hardware. The depth to a given anomaly is determined by recording the two way travel time in material with a known velocity. GPR surveys are conducted quickly and do not suffer the detector coupling problems experienced by seismic techniques. High resolution is achieved in near surface applications. Various signal processing techniques may be applied to enhance the interpretation of field data. A comprehensive description of radar physics pertinent to this application is reported by Siggins, 1990.
An interactive 4D spatio-temporal visualization system for hydrometeorological data in natural disasters
Published in International Journal of Digital Earth, 2020
Xuequan Zhang, Mingda Zhang, Liangcun Jiang, Peng Yue
4DSVS provides an interactive 4D visualization framework for hydrometeorological data to support decision-making in natural disasters. The following advantages can be achieved from the design and development of the system: (1) General applicability. Considering the different scales, dimensions, and time, hydrometeorological data are organized, processed, and visualized in a uniform way. The system can be applied to different meteorological disasters including flood, typhoon (storm), and hot/cold wave. (2) Flexible field model selection and cooperation. Ten most commonly used field models are integrated into the system. Since different models have different characteristics and advantages, model selection and cooperation are beneficial for the specific disaster analysis. (3) Smooth visual effects. As the hydrometeorological data have the characteristics of multidimension, time variation, heterogeneity, and large amount, technologies, including multilevel data scheduling, multithread computing, LOD strategy, and GPU acceleration, are adopted to improve the efficiency and effects. Meanwhile, the rendering models are designed and optimized for the efficient field visualization. The FPS of all the field models is higher than 30. (4) User-friendly interactions. The field data can be interactively visualized by the template configuration and analyzed at a specific location and time, which are very important for decision-makers to make judgments.
Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations
Published in Transportation Letters, 2018
Dewen Kong, George F. List, Xiucheng Guo, Dingxin Wu
Statistical analysis and error test were employed to find out how well the field data can be explained by simulation quantitatively. The RMSE and adjusted R2 used in microscopic validation were used here for validation. The results from the improved CA model were also compared with other models (NaSch model, Brake light model, and Gipps model) to evaluate its performance. The values of RMSE and adjusted R2 for each simulation model are presented in Table 6. For the improved CA model, the adjusted R2 of average speed is the highest i.e. 0.84. The adjusted R2 of average density is 0.83, which is nearly the same as the average speed. For volume, the value of adjusted R2 is less than speed and density, which is 0.79, since it is the product of speed and density. In general, results of adjusted R2 indicate that the improved CA model could well explain the field data. Compared with other three models, the improved CA model always has a higher adjusted R2 for average speed, density, and volume. The results show a better performance of the proposed model. The RMSE of average speed for the improved CA model is the smallest, only 2.18 km h−1, followed by density and volume, which is 3.08 and 107.11 veh h−1, respectively. According to Bham and Benekohal (2004), the proposed CA model can be thought to pass the error test. The RMSE of average speed, density, and volume for NaSch model, Brake light model and Gipps model are also very small and have the same characteristics as that of the improved CA model, but the values are all larger than the proposed model. This means the improved CA model has a better accuracy.
Design Parameter Changeover for a Shell and Tube Direct Expansion Evaporator after Installation on Board a Commercial Fishing Vessel
Published in Heat Transfer Engineering, 2020
Zahid Ayub, Cabrera Daniel, Ayub Adnan, Khan Nasir, Mehmood Tahir
Always confirm the actual design parameters with the end user prior to embarking on the design, fabrication, and installation phases.Collect as much information as possible especially actual field data.Analyze data carefully and then work out the best possible solution.