[vc_empty_space][vc_empty_space]
Dam water level prediction system utilizing Artificial Neural Network Back Propagation: Case study: Ciliwung watershed, Katulampa Dam
Anindita A.P.a, Laksono P.a, Nugraha I.G.B.B.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, 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]© 2016 IEEE.Flooding is a natural disaster that happens annually in Jakarta because the capacity insufficiency of the canals in accommodating the overflowing river water especially under the rainy season. Hydrologists and meteorologists have tried to predict the rainfall, as it was the expected root cause of the overflow. However, there were no models available for real time water level prediction from rainfall area nearby, especially with respect to Indonesian environment characteristics. Current flood prediction system only last for 6-24 hour based on outskirts dam water level using projected time from the sluicegate to the city canals. Therefore, when there is a predicted disaster to happen, the subsequent evacuation has to be done in a short time period. Artificial Neural Network Back Propagation is one of the common methods in solving continuous data modeling and has supported multiple early warning systems in some countries. Based on the geomorphology of Ciliwung watershed, runoff drainage and land seeping variable needs to be included as one of the training attributes. Available node model has linked rainfall from nearby area to Katulampa Dam, and the model has been further developed with RMSE of 9.2142 and r (correlation coefficient) accounted for 0.8799. The result has improved the prediction capacity of the previous node model by 1%, and can be used for actual early warning system in the future. This model used batch learning for its training method but it can be upgraded to online learning where weights from the model could be readjusted automatically through continuous learning.[/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]Continuous learning,Correlation coefficient,Early Warning System,Environment characteristic,Flood prediction,Natural disasters,Prediction model,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=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Back Propagation,Drainage Runoff,Early Warning System,Multilayer Perceptron,Prediction Model[/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/ICTSS.2016.7792862[/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]