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Combined 1D Modelling with HEC-RAS for Delineation Floodplain Area: A Case Study of Hennops River in the Centurion Area
Issue:
Volume 6, Issue 4, December 2021
Pages:
55-62
Received:
16 May 2021
Accepted:
9 June 2021
Published:
23 November 2021
Abstract: Flooding is among the most extreme weather events, endangering lives and causing significant property damage each year. Flood events in the Centurion area occur every year during the rainy season. This causes considerable damage to road infrastructure in both residential and commercial areas. The objective of this study is to incorporate hydraulic/hydrological (i.e., HEC-RAS/HEC-GeoRAS) models with geo-spatial techniques to predict flood extent and depth along Hennops River in Centurion area, Tshwane Metropolitan Municipality, Gauteng Province. In this study, floodplain inundation areas with different return periods were predicted in a 3.1 km distance of the Hennops River that passes through the Centurion area. Flood hazard analysis indicated that areas at close proximity to the Hennops River were submerged by a minimum and maximum flood depth of 0.4 m to more than 1.1 m for both 50 and 100 year flood recurrence interval. The study’s findings show that integrating GIS with HEC-RAS/HEC-GeoRAS techniques is a useful tool for floodplain mapping and analysis. Hence, the findings of this study are expected to be used as a foundation for the identification of causative factors of flash floods and the prediction of flash floods within the study area in future. The floodplain delineation maps developed in this study will be useful to policy-makers and the relevant authorities, as well as to local residents, in finding suitable measures for residential development along the floodplain while reducing flood risk in the study area.
Abstract: Flooding is among the most extreme weather events, endangering lives and causing significant property damage each year. Flood events in the Centurion area occur every year during the rainy season. This causes considerable damage to road infrastructure in both residential and commercial areas. The objective of this study is to incorporate hydraulic/...
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Goodness of Fit Indices for Different Cases
Ahmed Mohamed Mohamed Elsayed,
Nevein Nagy Aneis
Issue:
Volume 6, Issue 4, December 2021
Pages:
63-75
Received:
17 October 2021
Accepted:
8 November 2021
Published:
25 November 2021
Abstract: Path analysis is used to estimate a system of equations of the observed variables. These models assume perfect measurement of the observed variables. The relationships between observed variables are modeled. These models are used when one or more variables is mediating the relationship between two others. Structural equation modeling is a methodology for representing, estimating, and testing the relationships between measured and latent variables. This paper provides a combination between the path Analysis and the structural equation modeling to analyze three practical data: Hunua, Respiratory and Iris data, using AMOS program. In each case, the numerical results are constructed and compared according to nature of analysis and methods. Regression weights between all variables are estimated using the maximum likelihood estimation, and its tests are constructed for each data. From the regression weights, and the network of relationships, we constructed the structural equation modeling for all data. The estimated errors are indicated for the endogenous variables. Many indices, which indicate the goodness of fit of all models, are presented and compared. The best indices of goodness of fit of the models are Chi-Square, Root Mean Squared Error Approximately, and Normal Fit Index. These indices are consistent together.
Abstract: Path analysis is used to estimate a system of equations of the observed variables. These models assume perfect measurement of the observed variables. The relationships between observed variables are modeled. These models are used when one or more variables is mediating the relationship between two others. Structural equation modeling is a methodolo...
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A Dynamical Systems Model for Face Perception
Abraham Boyarsky,
Paweł Góra
Issue:
Volume 6, Issue 4, December 2021
Pages:
76-80
Received:
29 November 2021
Accepted:
11 December 2021
Published:
24 December 2021
Abstract: The fusiform face area, or FFA, is a small region found on the inferior (bottom) surface of the temporal lobe. It is located in a gyrus called the fusiform gyrus.Studies in humans have shown that the FFA is sensitive to both face parts and face configurations. Recoding activity in the FFA showed that most of the neurons in the FFA are active in response to facial imagery, but not in response to images of other body parts or objects. Visual sensory neurons sensitive to a face feature and possessing a related firing rate activate an associated cluster of neurons in the FFA. This results in a partition of the FFA into clusters that respond to the various facial features. Once an entire face stimulus activates the FFA, interneurons redistribute the initial activation via the neural network. In this article a novel approach to modelling the function of the network is presented. We define by a transition matrix that describes probabilistically how one cluster, firing at a synchronous rate, affects the others in the FFA. The initial face stimulation in the FFA together with the transition matrix defines a dynamical system which possesses a stationary probability function. We claim that a stationary probability function uniquely represents a face. Among the properties of this probability function are: 1) response magnitude invariance, 2) repurposing of clusters to define new stationary probability function on the FFA partition; 3) stability of stationary probabilities under perturbations.
Abstract: The fusiform face area, or FFA, is a small region found on the inferior (bottom) surface of the temporal lobe. It is located in a gyrus called the fusiform gyrus.Studies in humans have shown that the FFA is sensitive to both face parts and face configurations. Recoding activity in the FFA showed that most of the neurons in the FFA are active in res...
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Research on Ship Collision Avoidance Path Optimization Based on Particle Swarm Optimization and Genetic Algorithm
Issue:
Volume 6, Issue 4, December 2021
Pages:
81-87
Received:
6 December 2021
Accepted:
13 December 2021
Published:
31 December 2021
Abstract: With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligence, in order to avoid or reduce ship collision accidents in the case of large marine traffic flow and complex traffic forms. Based on the previous research, considering the economic and safety requirements of ship collision avoidance, and based on particle swarm optimization algorithm, genetic algorithm and nonlinear programming theory, this paper establishes the optimization model of ship collision avoidance path planning. Combined with specific cases, the simulation analysis is carried out under the three collision avoidance situations of ship head-on, crossing and overtaking. The simulation results show that the convergence speed of particle swarm genetic hybrid optimization algorithm is fast, ship collision avoidance path is smooth, and path distance and steering angle is small. The optimal path of ship collision avoidance can meet the requirements of economy and safety at the same time, and the effectiveness and operation efficiency of the algorithm have been significantly improved.
Abstract: With the increasingly busy shipping routes, ship collision accidents occur from time to time. In order to avoid ship collision, the research on ship collision avoidance decision has become a research hotspot. For a long time, many experts and scholars have been publishing research results on collision avoidance automation and artificial intelligenc...
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