Long-term hydrometeorological time-series analysis over the central highlands of West Papua

This is a Preprint and has not been peer reviewed.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.

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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 11:22

Last Updated: 2023-08-20 22:19

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
https://github.com/sandyherho/tsHydrochWP