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All Journal Majalah Ilmiah Pengkajian Industri Jurnal Sains dan Teknologi Mitigasi Bencana
Riza, Hammam
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Engineering Industrial & Manufacturing Engineering Transportation

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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.
MACHINE LEARNING APPLICATION IN RESPONSE TO DISASTER RISK REDUCTION OF FOREST AND PEATLAND FIRE: Impact-Based Learning of DRR for Forest, Land Fire and Peat Smouldering Riza, Hammam; Santoso, Eko Widi; Kristijono, Agus; Melati, Dian Nuraini; Prawiradisastra, Firman
Majalah Ilmiah Pengkajian Industri Vol. 14 No. 3 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : Deputi TIRBR-BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/mipi.v14i3.4426

Abstract

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that has not been decomposed. Once it burns, smoldering peat fires consume huge biomass. Peat smoldering fires are challenging to extinguish. These will continuously occur for weeks to months. Experts and practitioners of peat smoldering fires are the most recommended effort to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology.' Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example, monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses, and soon such methods will be replaced by the Machine Learning method as it is developing recently. What about the potential application of Machine Learning in the forest and land fires, particularly smoldering peat fires in Indonesia? This paper tries to answer this question. This paper recommends a conceptual design: impact-based Learning for Disaster Risk Reduction (DRR) of Forest, Land Fire, and Peat Smouldering. Keywords: Artificial Intelligence; Machine Learning; Wildfire; Peat Smouldering; DRR impact-based
Co-Authors Kristijono, Agus Melati, Dian Nuraini Prawiradisastra, Firman Prihartanto, Prihartanto Santoso, Eko Widi Tejakusuma, Iwan Gunawan