Monthly daily-mean rainfall forecast over Indonesia using machine learning and artificial intelligence ensemble
| Autor: | S Noviati, S P Rahayu, Hastuadi Harsa, T D Hutapea, Roni Kurniawan, Alfan S. Praja, Y Swarinoto, M N Habibie |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IOP Conference Series: Earth and Environmental Science. 893:012030 |
| ISSN: | 1755-1315 1755-1307 |
| DOI: | 10.1088/1755-1315/893/1/012030 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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