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A study of hold-out and k-fold cross validation for accuracy of groundwater modeling in tidal lowland reclamation using extreme learning machine
Nurhayatia,b, Soekarno I.b, Hadihardaja I.K.b, Cahyono M.b
a Civil Engineering Department, University of Tanjungpura, Pontianak, Indonesia
b Institut Teknologi Bandung, 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]© 2014 IEEE.The accuracy of prediction is required to conduct modeling the groundwater flow. This research represents the application of extreme learning machine (ELM) that can be used to model the groundwater flow in tidal lowland reclamation. The accuracy is measured using the hold-out and k-fold cross validation methods. The study will be implemented in the Delta Telang I Lowlands area, Banyuasin District, South Sumatra Province. The results of this groundwater flow modeling shows that the accuracy using the k-fold cross validation is better than the hold-out method. The values of accuracy level of the results of simulation-1 for training are: MSE = 0.000042091 and MAPE = 0.3165%. The values of accuracy level of the results of simulation-1 for testing are: MSE = 0.000093083 and MAPE = 0.4006%.[/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]Accuracy level,Cross validation,Extreme learning machine,Groundwater Flow Model,Groundwater modeling,hold-out,K fold cross validations,tidal lowland[/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]cross validation,extreme learning machine,groundwater,hold-out,tidal lowland[/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][/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/TIME-E.2014.7011623[/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]