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

Abstract

The fastest sea level rise began in 2013 and reached its highest level in 2021. This is part of the ongoing global warming impact, where polar ice continues to melt, glaciers also continue to melt, causing sea level rise. In the Bandar Lampung City area, there are several areas that are threatened with tidal flooding, namely Karang City Village and Kangkung Village, Bumi Waras Village, and Sukaraja Village. Bandar Lampung itself is the city center in the coastal area. Where the majority of the population is in the Coastal area so that the threat of tidal flooding is caused by rising sea levels. To study the occurrence of tidal floods in the past, this research uses an Artificial Neural Network which has the ability to study non-linear data which is then carried out by training and testing until the best configuration model is obtained. Based on the analysis and discussion that has been carried out, several important points can be drawn, including the results of training and dataset testing that has been carried out. , 80:20, and 90;10. This is evidenced by the results of the high accuracy of the model configuration and also the results of the prediction table which is able to describe the actual conditions, setting the model configuration experimentally is able to produce the best training accuracy value reaching 100% while for the best testing accuracy is 88%. The average correlation value of training 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, and the 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 dataset is 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 error value 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 the 90:10 dataset is 0.007, the flood prediction table is made based on 1 configuration the best with a training accuracy rate of 98% and a testing accuracy of 80% with an error value of 0.004, namely configuration model 14, this model is the best configuration model out of 3 dataset divisions out of a total of 5. The prediction table uses sea level tides of 1.5 meters. The prediction table is able to provide good tidal flood percentage values, especially when there are active astronomical phenomena. The results of this good flood prediction table illustrate that the backpropagation ANN is able to study datasets well and can be used by BMKG forecasters in making tidal flood early warnings.