SISTEM PERINGATAN DINI BANJIR BERBASIS MACHINE LEARNING: STUDI LITERATUR

Authors

  • Agustina Rachmawardani Sekolah Tinggi Meteorologi Klimatologi dan Geofisika
  • Sastra K. Wijaya Universitas Indonesia
  • Ardhasena Shopaheluwakan Badan Meteorologi Klimatologi dan Geofisika

DOI:

https://doi.org/10.46880/jmika.Vol6No2.pp188-198

Keywords:

Early Warning, Flood, Machine Learning, Ensemble

Abstract

Indonesia is a disaster-prone country and 76% is a hydrometeorological disaster (floods, landslides, tropical cyclones and droughts). Floods that occurred in Jakarta had a negative impact on the community so that it would have an impact on economic losses that made it difficult for communities around areas that were frequently affected by floods to develop more advanced and productively. Therefore, the inhibition of increasing welfare caused by floods that are not immediately handled can increase the number of people's poverty because they always have to spend both on house repairs, health and other things caused by floods. In addition, public facilities and various kinds of infrastructure were damaged. The environment is also negatively affected when floods occur. Clean water is difficult to obtain so it causes many diseases. Floods also cause animals to be killed, thereby disrupting the natural balance of the ecosystem. The existence of flood prediction research will reduce the risk and damage caused by flood disasters and can provide advice and considerations in policy making. Flood early warning system is one of the solutions offered in dealing with flood disasters. Providing actual and real time information, this early warning system is expected to reduce economic losses to fatalities. In an effort to create a resilient city, ESCAP (2008) puts an early warning system in place as an effort to prepare before a disaster occurs and to mitigate floods

Published

2022-10-31

Issue

Section

METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi