# Flood Early Warning System Using River Water Level Prediction with Artificial Neural Network (Case Study: Jakarta City)
> Saputro D.B.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_85147093936
Jurnal / Konferensi: Proceeding IEEE 8th Information Technology International Seminar Itis 2022
Tahun terbit: 2022
DOI: https://doi.org/10.1109/ITIS57155.2022.10010313
Citations: 5

## Authors
- Saputro D.B.

## Abstract
The problem of flooding is still an important topic to be solved in Indonesia. In 2021, floods were the most common disaster. So, this research proposes one way to deal with flood disasters: by predicting the arrival of the flood by predicting the river’s water level to know the alert status in each river so that residents can anticipate the arrival of floods. In this study, the prediction of river water height will use one of the machine learning algorithms, Artificial Neural Network (ANN). The machine learning model that has been created will be presented using the web in making a machine learning model using 1096 historical data on river water levels and weather. In the experiments carried out, we also carried out comparisons using data that the Synthetic Minority Over-Sampling Technique has carried out with Gaussian Noise (SMOGN) process and data that SMOGN has not processed. The results show that using 125 nodes can produce a smaller prediction error value. For experiments using SMOGN, the results of the experiment are still inconsistent, and some observation points produce larger MAE values.

## Keywords
- Artificial neural network
- Flood myth
- Warning system
- Computer science
- Early warning system
- Water resource management
- Environmental science
- Civil engineering
- Hydrology (agriculture)
- Artificial intelligence
- Engineering
- Geography
- Telecommunications
- Geotechnical engineering
- Archaeology

---
Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.