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Neural Network Modelling on Wave Dissipation Due to Mangrove Forest
Malvin N.a, Pudjaprasetya S.R.a, Adytia D.b
a Institut Teknologi Bandung, Faculty of Mathematics and Natural Sciences, Bandung, Indonesia
b Telkom University, School of Computing, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.Extensive research and field observations have reported the role of green vegetation in dissipating incoming wave energy, as well as in protecting coastlines and reducing abrasion. Wave dissipation due to vegetation has been studied experimentally in hydrodynamic laboratory, and also numerically by simulating wave propagation. Approach by using numerical simulation can be performed by modelling the wave damping by mangrove as a friction term with the associated Manning’s roughness coefficient that is derived from physical experiment. However, this kind of computation can be costly. In this study, we introduce a new modelling approach based on neural network modelling that is developed to estimate wave dissipation by mangrove forest. The neural network model was trained by using a large number of data sets obtained from numerical simulations of wave propagation passing through mangrove swamps. Here, we use the Nonlinear Shallow Water Equation (NSWE) model, which has been numerically solved using the momentum conserving staggered grid scheme (also called the MCS scheme). The numerical results were validated using the experimental data obtained from hydrodynamic laboratory. By using this new approach, we obtain an efficient and relatively accurate prediction model for solitary wave dissipation with various wave amplitudes, including breaking and non-breaking waves.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Accurate prediction,Manning’s roughness coefficient,Momentum conserving,Neural network model,Neural network modelling,Non-linear shallow water equations,Physical experiments,Staggered grid schemes[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]artificial neural network,mangrove,wave dissipation,wave dynamic[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors thank Dr. Semeidi Husrin and Leichtweiss Institute (LWT), TU Braunsweig for providing the experimental data.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICoDSA50139.2020.9212826[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]