Abstract
This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.