Prediction of flood events in the city of Bandar Lampung using the artificial neural network

Nurpambudi, Ramadhan and Wulandari, Eka Suci Puspita and Aziz, RZ Abdul (2023) Prediction of flood events in the city of Bandar Lampung using the artificial neural network. JURNAL INFOTEL, 15 (1). ISSN 2460-0997

Text Prediction of Flood Events in the City of Bandar Lampung Using the Artificial Neural Network _ JURNAL INFOTEL.pdf Download (1MB)

Abstract

The city of Bandar Lampung, Indonesia, is recently experiencing seasonal flooding, which occurs almost every year and results in significant losses. In the last 10 years, floods event recorded by the National Agency for Disaster Countermeasure (BNPB) in the Bandar Lampung area is 16 incidents of flooding. More than 14,000 people suffered, more than 500 people had to be evacuated, more than 900 houses were damaged, and four public facilities were damaged. To study the pattern of flood events in the past, the Artificial Neural Network Backpropagation learning method will be used which will utilize its non-linear variable learning abilities. The configuration settings for the Artificial Neural Network were carried 0ut experimentally without any basis for assigning values, especially for the parameters of the number of hidden layers, number of neurons, and epochs used in training and variable testing. The results obtained from this study are the results of training and testing of datasets that have been carried out by ANN backpropagation and can properly study patterns of flood events and also non-flood events in the dataset, this is evidenced by the results of high model configuration accuracy and also the results of predictive tables that able to describe actual conditions, setting the configuration model experimentally can produce an accuracy value of 90 %-100 %, an average training correlation value of 0.96 and an average test correlation value of 0.89, and an average error value of 0.0089 out of 20 model configuration, and the flood prediction table are made based on the 1 best configuration with a training and testing accuracy rate of 100 % with an error value of 0.00134, namely configuration model 20, the prediction table uses an average air temperature of 27◦C with 80 % humidity. The prediction table can produce excellent flood potential results which can represent flood events as well as non-flood events based on the results of the dataset learning.

Item Type:	Article
Subjects:	600 Teknologi - Ilmu terapan > 620 Ilmu Teknik - Engineering
Divisions:	Artikel Ilmiah Dosen > Fakultas Ilmu Komputer
Depositing User:	Dr. RZ Abdul Aziz
Date Deposited:	20 Jul 2023 09:11
Last Modified:	20 Jul 2023 09:11
URI:	http://repo.darmajaya.ac.id/id/eprint/12310

Actions (login required)

View Item