Artificial Intelligent For Rainfall Estimation In Tropical Region : A Survey

Abstract

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.