Prediction model with artificial neural network for tidal flood events in the coastal area of Bandar Lampung City

Wulandari, Eka Suci Puspita and Nurpambudi, Ramadhan and Aziz, RZ Abdul (2023) Prediction model with artificial neural network for tidal flood events in the coastal area of Bandar Lampung City. infotel, 15 (2). pp. 135-149. ISSN 24600997

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Abstract

The fastest sea level rise began in 2013 and reached its highest level in 2021. This condition is part of theongoing global warming impact, where polar ice and glaciers also continue to melt, causing sea level rise. In the BandarLampung City area, several areas are threatened by tidal flooding, namely Karang City Village and Kangkung Village, BumiWaras Village, and Sukaraja Village. Bandar Lampung itself is the city center in the coastal area where the majority of thepopulation is in the Coastal area. So that rising sea levels cause the threat of tidal flooding. This research proposes to studythe occurrence of tidal floods in the past. This research uses an Artificial Neural Network, which can study non-linear data,which is then carried out by training and testing until the best configuration model is obtained. Based on the conductedanalysis and discussion, several significant points can be inferred. These include the ratios of 80:20 and 90:10, which wereutilized. The effectiveness of these ratios is evident through the model’s high accuracy in configuration and prediction oftidal flood events, accurately representing real-world conditions. The experiment model configuration can be set to producethe best training accuracy value reaching 100 %, while the best testing accuracy is 88 %. The average correlation value oftraining with the 50:50 dataset is 0.975, the 60:40 dataset is 0.975, the 70:30 dataset is 0.951, the 80:20 dataset is 0.935, andthe 90:10 dataset is 0.929. For the average value of the correlation test with the 50:50 dataset of 0.514, the 60:40 datasetis 0.362, the 70:30 dataset is 0.488, the 80:20 dataset is 0.284, and the 90:10 dataset is 0.402. Whereas the average errorvalue for the 50:50 dataset is 0.006, the 60:40 dataset is 0.006, the 70:30 dataset is 0.010, the 80:20 dataset is 0.007, and the90:10 dataset is 0.007, the tidal flood prediction is made based on one configuration the best with a training accuracy rateof 98 % and a testing accuracy of 80 % with an error value of 0.004, namely configuration model 14, this model is the bestconfiguration model out of 3 dataset divisions out of a total of 5. The tidal flood prediction uses sea level tides of 1.5 m.The prediction results for tidal floods are very good, especially when active astronomical phenomena occur. The results ofthis excellent prediction of tidal floods illustrate that Artificial Neural Network backpropagation can study datasets well andcan be used by Meteorical, Climatological, and Geophysical Agency forecasters in making early warnings of tidal floods.

Item Type:	Article
Subjects:	Ilmu Komputer
Divisions:	Artikel Ilmiah Dosen > Fakultas Ilmu Komputer
Depositing User:	Dr. RZ Abdul Aziz
Date Deposited:	29 May 2024 06:43
Last Modified:	29 May 2024 06:43
URI:	http://repo.darmajaya.ac.id/id/eprint/16266

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