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Artificial neural network for predicting earthquake casualties and damages in Indonesia
Oktarina R.a, Bahagia S.N.b, Diawati L.b, Pribadi K.S.b
a Industrial Engineering Department, Faculty of Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia
b Industrial Engineering Department, Bandung Institute of Technology, Bandung, 40132, 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 Published under licence by IOP Publishing Ltd.The paper is intended to develop a model to predict the number of damaged buildings and casualties due to earthquake using ANN (Artificial Neural Network). This model is expected to be able to generate the type and amount of relief supplies required by those affected during the emergency phase. This research develops ANN using supervised learning paradigm, and backpropagation learning algorithm. The applied ANN network architecture is a multiple-layer system, with 1 (one) neuron used in both input and output layer, and 95 (ninety-five) neurons used in the hidden layer yielding 0.99971 as the greatest value of the correlation coefficient. The output variable in this study is the earthquake impact consisting of six variables. While the input variables (predictors) in this study consisting of eight variables. The model in this study utilizes 123 seismic datasets, divided into 100 data (80%) for the training process and 23 data (20%) for the testing process. This research adds to the existing research and demonstrates the application of ANN in predicting the numbers of damaged buildings and casualties. The model is useful in supporting and strengthening preparedness and emergency relief activities due to earthquake disaster.[/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]ANN (artificial neural network),Backpropagation learning algorithm,Correlation coefficient,Earthquake damage predictions,Earthquake disaster,Emergency relief,Input and outputs,Training process[/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,Earthquake casualties prediction,Earthquake damage 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]https://doi.org/10.1088/1755-1315/426/1/012156[/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]