Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Majalah Ilmiah Pengkajian Industri Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana Jurnal Sains dan Teknologi Mitigasi Bencana
Santoso, Eko Widi
Unknown Affiliation
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Education Engineering Industrial & Manufacturing Engineering Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Sains dan Teknologi Mitigasi Bencana

 1 
UTILIZATION OF ARTIFICIAL INTELLIGENCE TO IMPROVE FLOOD DISASTER MITIGATION Riza, Hammam; Santoso, Eko Widi; Tejakusuma, Iwan Gunawan; Prawiradisastra, Firman; Prihartanto, Prihartanto
Jurnal Sains dan Teknologi Mitigasi Bencana Vol 15, No 1 (2020): JURNAL SAINS DAN TEKNOLOGI MITIGASI BENCANA
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1516.032 KB) | DOI: 10.29122/jstmb.v15i1.4145

Abstract

Flood disaster is one of predominant disaster event in Indonesia. The frequency and intensity of this disaster tend to increase from year to year as well as the losses caused thereby. To reduce the risks and losses due to flood disasters, innovation in disaster mitigation is needed. Artificial intelligence and machine learning are technological innovations that have been widely applied in various fields of life and can also be used to improve flood disaster mitigation. A literature study conducted in this research shows that the use of artificial intelligence and machine learning has proven to be able, and succeed to fastly and accurately perform flood prediction, flood risk mapping, flood emergency response and, flood damage mapping. ANNs, SVM, SVR, ANFIS, WNN and DTs are popular methods used for flood mitigation in the pre-disaster phase and it is recommended to use a combination or hybrid of these methods. During the flood disaster response phase, the application of artificial intelligence and machine learning are still not much has been done and need to be developed. Examples of the application are the use of big data from social media Twitter and machine learning both supervised learning with Random Forest and unsupervised learning with CNN which have shown good results and have a good prospect to be applied. For the use of artificial intelligence in post-disaster flood phase, are still also rare, because it requires actual data from the field. However, in the future, it will become a promising program for the assessment and application of artificial intelligence in the flood disaster mitigation.
Co-Authors Fiqi, Bondan Kristijono, Agus Melati, Dian Nuraini Prawiradisastra, Firman Prihartanto, Prihartanto Riza, Hammam Tejakusuma, Iwan Gunawan Turyana, Iyan