Study of beach morphology has been one of the most important issues in coastal engineering research projects. Because of the existence of two important coastal areas located in the north and south parts of the Iran, i...Study of beach morphology has been one of the most important issues in coastal engineering research projects. Because of the existence of two important coastal areas located in the north and south parts of the Iran, in the present study an analysis of the coastal zone behaviour is made. Bed level elevations are measured and compared with the theoretical equilibrium profile. It is shown that the behaviour of the coastal zone in the region is consistent with the Dean （1991） equilibrium profile. In the next stage, following extensive investigations, the bed level changes due to arise in sea level at different locations in the surf zone are estimated. The mechanism of beach re-treatment due to a rise in sea level is considered based on the simplified model of Dean （1991） in which the mass balance of the sediments is taken into account. Comparison of the equilibrium profiles for different cases of sea level rise, clearly shows that because of the sediment transport induced by the fluctuation of the water level, the beach profile in the surf zone changes accordingly resulting in an erosion in the inner region of the surf zone and an accumulation of sediments towards the offshore.展开更多
A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the ch...A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the choice of initial and boundary conditions.In the present study,evolutionary algorithms(EAs)are employed for multi-objective Pareto optimum design of group method data handling(GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches,based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College(London,UK).The input parameters used for such modeling are significant wave height,wave period,wave action duration,reflection coefficient,distance from shoreline and sand size.In this way,EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks,in which the connectivity configurations in such networks are not limited to adjacent layers.Also,multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks.The most important objectives of GMDH-type neural networks that are considered in this study are training error(TE),prediction error(PE),and number of neurons(N).Different pairs of these objective functions are selected for two-objective optimization processes.Therefore,optimal Pareto fronts of such models are obtained in each case,which exhibit the trade-offs between the corresponding pair of the objectives and,thus,provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution.The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.展开更多
文摘Study of beach morphology has been one of the most important issues in coastal engineering research projects. Because of the existence of two important coastal areas located in the north and south parts of the Iran, in the present study an analysis of the coastal zone behaviour is made. Bed level elevations are measured and compared with the theoretical equilibrium profile. It is shown that the behaviour of the coastal zone in the region is consistent with the Dean （1991） equilibrium profile. In the next stage, following extensive investigations, the bed level changes due to arise in sea level at different locations in the surf zone are estimated. The mechanism of beach re-treatment due to a rise in sea level is considered based on the simplified model of Dean （1991） in which the mass balance of the sediments is taken into account. Comparison of the equilibrium profiles for different cases of sea level rise, clearly shows that because of the sediment transport induced by the fluctuation of the water level, the beach profile in the surf zone changes accordingly resulting in an erosion in the inner region of the surf zone and an accumulation of sediments towards the offshore.
文摘A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the choice of initial and boundary conditions.In the present study,evolutionary algorithms(EAs)are employed for multi-objective Pareto optimum design of group method data handling(GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches,based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College(London,UK).The input parameters used for such modeling are significant wave height,wave period,wave action duration,reflection coefficient,distance from shoreline and sand size.In this way,EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks,in which the connectivity configurations in such networks are not limited to adjacent layers.Also,multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks.The most important objectives of GMDH-type neural networks that are considered in this study are training error(TE),prediction error(PE),and number of neurons(N).Different pairs of these objective functions are selected for two-objective optimization processes.Therefore,optimal Pareto fronts of such models are obtained in each case,which exhibit the trade-offs between the corresponding pair of the objectives and,thus,provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution.The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.
