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The prediction of groundwater level on tidal lowlands reclamation using extreme learning machine
Nurhayatia,b, Soekarno I.b, Hadihardaja I.K.b, Cahyono M.b
a Civil Engineering Department, University of Tanjungpura, Indonesia
b Civil Engineering Department, Institut Teknologi 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]Groundwater level in the tidal lowlands fluctuates according to space and time. It is influenced by local rainfall or tidal. Tidal lowlands development for agriculture particularly food crops requires proper water management strategies so that the productivity of land and the production of food crops can be optimized. Proper water management must be supported by an accurate prediction system. This research aims to apply extreme learning machine (ELM) which can be used to make a prediction system of groundwater level in tidal lowlands. ELM is a feed forward artificial neural network with a single hidden layer or commonly referred to as single hidden layer feed forward neural networks (SLFNs). ELM has the advantage in learning speed. The result of the ground water level prediction using ELM was better than that using BPANN. Based on these results, the ELM can be used to predict the ground water level in order to assist decision makers in determining water management strategies and the determination of appropriate cropping patterns in the tidal lowlands reclamation. © 2005 – 2013 JATIT & LLS. All rights reserved.[/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][/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,Back propagation,Extreme learning machine,Ground water level,Prediction[/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][/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]