This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.18517/ijods.4.2.84-96.2023. This is version 5 of this Preprint.
Long-term hydrometeorological time-series analysis over the central highlands of West Papua
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Authors
Sandy Hardian Susanto Herho 
Abstract
This article introduces an innovative data-driven approach to examining the long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. Through the utilization of wavelet transforms, we identified signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12).
Building upon this cause-and-effect relationship, we employed dynamic causality modeling, utilizing the Nonlinear Autoregressive with Exogenous input (NARX) model, to predict SPI-12. In this predictive framework, the Multivariate ENSO Index (MEI) was employed as an attribute variable. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series.
The implications of this study extend significantly to the advancement of data-driven precipitation models for regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).
DOI
https://doi.org/10.31223/X50K7X
Subjects
Physical Sciences and Mathematics
Keywords
ENSO, NARX, SPI, wavelet transform
Dates
Published: 2021-04-06 02:22
Last Updated: 2023-08-20 13:19
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License
CC BY Attribution 4.0 International
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Conflict of interest statement:
None
Data Availability (Reason not available):
https://github.com/sandyherho/tsHydrochWP
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