TY - JOUR
T1 - Artificial Intelligent For Rainfall Estimation In Tropical Region
T2 - 2022 International Conference on Sustainability and Technology in Climate Change: Adaptation Action, IC-STCC 2022
AU - Mardyansyah, R. Y.
AU - Kurniawan, B.
AU - Soekirno, S.
AU - Nuryanto, D. E.
AU - Satria, H.
N1 - Funding Information:
This study is supported by Degree byResearch Program from Lembaga Ilmu Pengetahuan Indonesia (LIPI). Author wishes to acknowledge Meteorological, Climatological, and Geophysics Agency of Indonesia for providing research’s facility.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2022
Y1 - 2022
N2 - Rainfall monitoring in real-time is a mandatory in tropical areas such as Indonesia. As a country with various topographical conditions ranging from low-lying urban areas, highlands, to mountainous valleys, Indonesia is prone to hydrometeorological disasters in the form of flash floods and landslides. The strategic geographical position at the equator, between the Pacific and Indian oceans, and surrounded by vast oceans, combined with various natural phenomena related to the dynamics of the atmosphere and the ocean, makes high-density rainfall observations indispensable for both disaster mitigation and climate monitoring. As a vast tropical and archipelagic country, Indonesia currently has around 1000 automatic rainfall sensors and still requires more sensors to increase the spatial resolution of the observation network. Increasing the density of the observation network using both rain gauges and weather radar poses a problem of high operational costs. Therefore, several alternative rainfall observation systems are required. In the last decade, there have been several studies related to rainfall measurements using artificial intelligence from various meteorological variables, including the exploitation of microwave signals from radio telecommunications links, both terrestrial and satellite using high frequency bands. In this survey paper, we review and discuss research articles related to rainfall estimation using state-of-the-art methods in artificial intelligence using meteorological observation data, remote sensing, terrestrial and satellite microwave communication links. In conclusion, we present several future research challenges that can be applied to increase the density of rainfall observation networks.
AB - Rainfall monitoring in real-time is a mandatory in tropical areas such as Indonesia. As a country with various topographical conditions ranging from low-lying urban areas, highlands, to mountainous valleys, Indonesia is prone to hydrometeorological disasters in the form of flash floods and landslides. The strategic geographical position at the equator, between the Pacific and Indian oceans, and surrounded by vast oceans, combined with various natural phenomena related to the dynamics of the atmosphere and the ocean, makes high-density rainfall observations indispensable for both disaster mitigation and climate monitoring. As a vast tropical and archipelagic country, Indonesia currently has around 1000 automatic rainfall sensors and still requires more sensors to increase the spatial resolution of the observation network. Increasing the density of the observation network using both rain gauges and weather radar poses a problem of high operational costs. Therefore, several alternative rainfall observation systems are required. In the last decade, there have been several studies related to rainfall measurements using artificial intelligence from various meteorological variables, including the exploitation of microwave signals from radio telecommunications links, both terrestrial and satellite using high frequency bands. In this survey paper, we review and discuss research articles related to rainfall estimation using state-of-the-art methods in artificial intelligence using meteorological observation data, remote sensing, terrestrial and satellite microwave communication links. In conclusion, we present several future research challenges that can be applied to increase the density of rainfall observation networks.
UR - http://www.scopus.com/inward/record.url?scp=85145288382&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/1105/1/012024
DO - 10.1088/1755-1315/1105/1/012024
M3 - Conference article
AN - SCOPUS:85145288382
SN - 1755-1307
VL - 1105
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012024
Y2 - 23 April 2022 through 24 April 2022
ER -
