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All Journal GEA, Jurnal Pendidikan Geografi Majalah Ilmiah Pengkajian Industri Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana Jurnal Sains dan Teknologi Mitigasi Bencana
Prawiradisastra, Firman
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Education Engineering Industrial & Manufacturing Engineering Transportation

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FLOOD DISASTER HAZARD ASSESSMENT USING TOPOGRAPHIC WETNESS INDEX IN SERANG DISTRICT Prawiradisastra, Firman
Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana Vol 2, No 1 (2018): Jurnal Alami
Publisher : Badan Pengkajian dan Penerapan Teknologi (BPPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.827 KB) | DOI: 10.29122/alami.v2i1.2817

Abstract

Coping flood hazard risk needs to be done early on. One way of handling floods from the beginning is to predict flood-prone areas with topographic wetness index method. Serang regency is a fairly frequent area of flooding therefore it is necessary to conduct a study to predict flood-prone areas. The total area of flood-prone areas in Serang Regency is 62,608 Ha based on TWI saga modeling results. The area is dominated by high and low class with 25,050 Ha and 29,741 Ha respectively. While for the middle class of 7,817 ha.
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.
ANALISIS PASCA BENCANA TANAH LONGSOR 1 JANUARI 2020 DAN EVALUASI PENATAAN KAWASAN DI KECAMATAN SUKAJAYA, KABUPATEN BOGOR Sri Naryanto, Heru; Prawiradisastra, Firman; Ardiyanto, Ruki; Hidayat, Wahyu
Jurnal Pendidikan Geografi Gea Vol 20, No 2 (2020)
Publisher : Indonesia University of Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/gea.v20i2.24232

Abstract

Bencana tanah longsor semakin sering terjadi akhir-akhir ini di Kabupaten Bogor termasuk di Kecamatan Sukajaya. Bencana longsor telah terjadi secara masif, menyeluruh dan waktu yang bersamaan di Kecamatan Sukajaya, pada tanggal 1 Januari 2020 . Kecamatan Sukajaya termasuk dalam zona bahaya tanah longsor tinggi. Banyak faktor yang menyebabkan terjadinya tanah longsor di Kecamatan Sukajaya, yaitu: kelerengan, kondisi geologi dan tanah, tataguna lahan, pola drainase, curah hujan, dan aktivitas manusia. Faktor dominan yang paling berpengaruh adalah: curah hujan harian yang ekstrim sebelum dan saat terjadi longsor, jenis batuan vulkanik yang membentuk tanah sangat tebal dan gembur, dan kemiringan lereng yang curam-sangat curam. Analisis berbagai faktor penyebab longsor, zonasi tanah longsor serta analisis mekanisme longsor, akan sangat membantu dalam pananganan dan antisipasi bencana ke depan. Penataan kawasan pasca bencana longsor sangat diperlukan untuk bisa mengurangi risiko bencana serta membangun kawasan yang aman berkesinambungan. Banyak hal yang bisa dilakukan dalam penataan kawasan longsor tersebut antara lain adalah: daerah bekas dan rawan longsor dijadikan kawasan konservasi, relokasi penduduk yang terancam ke tempat yang aman, pengaturan drainase, penguatan tebing dan lereng jalan, menghindari tinggal pada lereng bukit serta alur sungai, penghijauan dengan tanaman yang keras dan berakar kuat. Pembentukan kesiapsiagaan masyarakat juga sangat dibutuhkan untuk membentuk masyarakat yang tangguh terhadap bencana tanah longsor. 
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 Prihartanto, Prihartanto Riza, Hammam Ruki Ardiyanto Santoso, Eko Widi Sri Naryanto, Heru Tejakusuma, Iwan Gunawan Wahyu Hidayat