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Breaking wave loads on coastal structures depend primarily on the type of wave breaking at the instant of impact. When a wave breaks on a vertical wall with an almost vertical front face called the “perfect breaking”, the greatest impact forces are produced. The correct prediction of impact forces from perfect breaking of waves on seawalls and breakwaters is closely dependent on the accurate determination of their configurations at breaking. The present study is concerned with the determination of the geometrical properties of perfect breaking waves on composite-type breakwaters by employing artificial neural networks. Using a set of laboratory data, the breaker crest height, hb, breaker height, Hb, and water depth in front of the wall, dw, from perfect breaking of waves on composite breakwaters are predicted using the artificial neural network technique and the results are compared with those obtained from linear and multi-linear regression models. The comparisons of the predicted results from the present models with measured data show that the hb, Hb and dw values, which represent the geometry of waves breaking directly on composite breakwaters, can be predicted more accurately by artificial neural networks compared to linear and multi-linear regressions. 相似文献
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Geodesy utilizes state of the art data collection techniques such as GPS (Global Positioning System) to acquire locations of points. Traditionally, the coordinates of these points are estimated using the Least Squares (LS) method. Nevertheless, Robust Estimation (RE) yields more accurate results than LS method in the presence of blunders (gross errors) among the data set. For example, the Least Trimmed Squares (LTS) method and the Least Median Squares (LMS) method can be used for this purpose. The first method aims to minimize the sum of the squared residuals by trimming away observations with large residuals. On the other hand, the second method involves the minimization of the median of the squared residuals. Both methods can be implemented using an optimization method, i.e., Artificial Bee Colony (ABC) algorithm. The ABC algorithm is a swarm intelligence (a branch of artificial intelligence) technique that can be used for the solution of minimization or maximization problems. In this paper, using the LTS and LMS methods for GPS data by employing the ABC, a new approach is put forward. Firstly, some discussions about the theoretical principals of RE and ABC are given. Then, a numerical example is used to demonstrate the validity of the proposed approach. Numerical results show that application of the robust estimation to GPS data can easily be carried out by ABC and this approach helps to enhance the reliability of geospatial data for any application of geodesy. 相似文献
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Using survey and interview data gathered from educators and educational administrators, we investigate school and community impacts of unconventional gas extraction within Pennsylvania's Marcellus Shale region. Respondents in areas with high levels of drilling are significantly more likely to perceive the effects of local economic gains, but also report increased inequality, heightened vulnerability of disadvantaged community members, and pronounced strains on local infrastructure. As community stakeholders in positions of local leadership, school leaders in areas experiencing Marcellus Shale natural gas extraction often face multiple decision-making dilemmas. These dilemmas occur in the context of incomplete information and rapid, unpredictable community change involving the emergence of both new opportunities and new insecurities. 相似文献
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Mevlut Uyan 《Environmental Earth Sciences》2014,71(4):1629-1639
Landfill site selection is a critical issue in the urban planning process because of its enormous impact on the economy, ecology, and the environmental health of the region. Landfill site selection process aims to locate the areas that will minimize hazards to the environment and public health. Multi-criteria evaluation methods are often used for different site selection studies. The purpose of this study was to determine suitable landfill site selection by using the geographical information system and the analytic hierarchy process in the study area. The final index model was grouped into four categories as “low suitable”, “moderate”, “suitable” and “best suitable” with an equal interval classification method. As a result, 12.69 % of the study area was low suitable, 7.27 % was moderately suitable, 13.79 % was suitable, and 15.52 % was the best suitable for landfilling; 50.72 % of the study area is not suitable for a landfilling. 相似文献
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