Monthly daily-mean rainfall forecast over Indonesia using machine learning and artificial intelligence ensemble

Abstract

A daily mean rainfall in a month forecast method is presented in this paper. The method provides spatial forecast over Indonesia and employs ensemble of Machine Learning and Artificial Intelligence algorithms as its forecast models. Each spatial grid in the forecast output is processed as an individual dataset. Therefore, each location in the forecast output has different stacked ensemble models as well as their model parameter settings. Furthermore, the best ensemble model is chosen for each spatial grid. The input dataset of the model consists of eight climate data (i.e., East and West Dipole Mode Index, Outgoing Longwave Radiation, Southern Oscillation Index, and Nino 1.2, 3, 4, 3.4) and monthly rainfall reanalysis data, ranging from January 1982 until December 2019. There are four assessment procedures performed on the models: daily mean rainfall establishment as a response function of climate patterns, and one-up to three-month lead forecast. The results show that, based on their performance, these non-Physical models are considerable to complement the existing forecast models.

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

“…In 2021, Weesakul and Chaiyasarn (2021) explored ANNs for the Ping River Basin, Thailand. Similarly, Harsa et al (2021) used climate data for Japan to train an ANN and compared it to XGBoost, showing they perform comparably. For monsoon rain in India, Bajpai and Bansal (2021) compared ANNs and 1D CNNs.…”