DOI: https://doi.org/10.18517/ijods.4.2.84-96.2023
Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua
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).
Article Details
How to Cite
[1]
S. H. S. Herho, D. E. Irawan, R. Kapid, and S. N. Kaban, “Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua”, Int. J. Data. Science., vol. 4, no. 2, pp. 84-96, Dec. 2023.
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References
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Pigram, C. J. and Symonds, P. A., 1991. A review of the timing of the major tectonic events in the New Guinea Orogen. Journal of Southeast Asian Earth Sciences, 6(3), pp.307–318.
Ramage, C. S., 1968. Role of a tropical “maritime continent” in the atmospheric circulation. Monthly Weather Review, 96(6), pp.365 – 370.
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Shen, S. S. P. and Somerville, R. C. J., 2019. Climate Mathematics: Theory and Applications. Cambridge: Cambridge University Press.
Smiley, D., 2012. PyNeurGen: Python Neural Genetic Algorithm Hybrids. Python library version 0.3.1.
Torrence, C. and Compo, G. P., 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), pp.61 – 78.
Wessel, P. et al., 2019. The Generic Mapping Tools Version 6. Geochemistry, Geophysics, Geosystems, 20(11), pp.5556–5564.
Wolter, K. and Timlin, M. S., 2011. El niño/southern oscillation behaviour since 1871 as diagnosed in an extended multivariate enso index (mei.ext). International Journal of Climatology, 31(7), pp.1074–1087.
Yamanaka, M. D., 2016. Physical climatology of Indonesian maritime continent: An outline to comprehend observational studies. Atmospheric Research, 178-179, pp.231–259.
Yang, S., Zhang, T., Li, Z. and Dong, S., 2019. Climate variability over the Maritime Continent and its role in global climate variation: A review. Journal of Meteorological Research, 33(6), pp.993–1015.
Yoden, S., Otsuka, S., Trilaksono, N. J. and Hadi, T. W., 2017. Recent Progress in Research on the Maritime Continent Monsoon. In Chapter 6, pp.63–77.
Ang, M. R. C. O., Gonzalez, R. M. and Castro, P. P. M., 2014. Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network – the development of the REIINN model. IOP Conference Series: Earth and Environmental Science, 17, p.012019.
Beguería, S. and Vicente-Serrano, S., 2017. SPEI: Calculation of the Standardised Precipitation- Evapotranspiration Index. R package version 1.7.
Boerema, J., 1938. Van Den Typen Regenval in Nederlandsch Indie (Rainfall types in Nederlands Indies). The Royal Observatory Magnetisch En Meteorologisch Batavia, Batavia.
Caswell, J. M., 2014. A nonlinear autoregressive approach to statistical prediction of disturbance storm time geomagnetic fluctuations using solar data. Journal of Signal and Information Processing, 5, pp.42–53.
Chang, C.-P., Wang, Z., McBride, J. and Liu, C.-H., 2005. Annual Cycle of Southeast Asia—Maritime Continent Rainfall and the Asymmetric Monsoon Transition. Journal of Climate, 18(2), pp.287 – 301.
Charlton, T. R., 2000. Tertiary evolution of the Eastern Indonesia Collision Complex. Journal of Asian Earth Sciences, 18(5), pp.603–631.
Chow, F. K., Schär, C., Ban, N., Lundquist, K. A., Schlemmer, L. and Shi, X., 2019. Crossing multiple gray zones in the transition from mesoscale to microscale simulation over complex terrain. Atmosphere, 10(5), p.274.
Clifton, A., Barber, S., Stökl, A., Frank, H. and Karlsson, T., 2022. Research challenges and needs for the deployment of wind energy in hilly and mountainous regions. Wind Energy Science, 7(6), pp.2231–2254.
Diaconescu, E., 2008. The use of narx neural networks to predict chaotic time series. World Scientific and Engineering Academy and Society (WSEAS), 3(3), pp.182–191.
Dow, D. B. and Sukamto, R., 1984. Western Irian Jaya: The end-product of oblique plate convergence in the late tertiary. Tectonophysics, 106(1), pp.109–139.
Gençay, R. and Qi, M., 2001. Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging. IEEE Transactions on Neural Networks, 12, pp.726 – 734.
Gers, F. A., Schmidhuber, J. and Cummins, F., 1999. Learning to forget: continual prediction with lstm. 1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), 2, pp.850–855.
Goger, B., Rotach, M. W., Gohm, A., Stiperski, I. and Fuhrer, O., 2016. Current challenges for numerical weather prediction in complex terrain: Topography representation and parameterizations. 2016 International Conference on High Performance Computing Simulation (HPCS), pp.890–894.
Grinsted, A., Moore, J. C. and Jevrejeva, S., 2004. Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), pp.561–566.
Guenang, G. M. and Kamga, F. M., 2014. Computation of the standardized precipitation index (spi) and its use to assess drought occurrences in cameroon over recent decades. Journal of Applied Meteorology and Climatology, 53(10), pp.2310 – 2324.
Guttman, N. B., 1999. Accepting the standardized precipitation index: A calculation algorithm1. Journal of the American Water Resources Association, 35(2), pp.311–322.
He, X., Zhao, K. and Chu, X., 2021. Automl: A survey of the state-of-the-art. Knowledge-Based Systems, 212, p.106622.
Herho, S., Syahputra, M. R. and Suwarman, R., 2018. A preliminary study of meteorological drought influences to social events over the maritime continent during the last millennium. Extended Abstract 98th American Meteorological Society Annual Meeting, 16th History Symposium, Austin, TX. American Meteorological Society.
Hersbach, H. et al., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), pp.1999–2049.
Hochreiter, S. and Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computation, 9(8), pp.1735–1780.
Hoyer, S. and Hamman, J., 2017. xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 5(1).
Krieger, S., Freij, N., Brazhe, A., Torrence, C. and Compo, G. P., 2017. PyCWT: spectral analysis using wavelets in Python. Python library version 0.3.0a22.
Largeron, C. et al., 2020. Toward snow cover estimation in mountainous areas using modern data assimilation methods: A review. Frontiers in Earth Science, 8, p.325.
Lau, K.-M. and Weng, H., 1995. Climate signal detection using wavelet transform: How to make a time series sing. Bulletin of the American Meteorological Society, 76(12), pp.2391 – 2402.
Marshall, A. and Beehler, B. M., 2007. The Ecology of Papua. Singapore: Periplus Editions (HK), vol. 1.
McKee, T. B., Doesken, N. J. and Kleist, J., 1993. The relationship of drought frequency and duration of the time scales. Proceeding 8th Conference on Applied Climatology, Anaheim, CA. American Meteorological Society.
Neale, R. and Slingo, J., 2003. The Maritime Continent and its role in the global climate: A GCM study. Journal of Climate, 16(5), pp.834–848.
Permana, D. S., Thompson, L. G. and Setyadi, G., 2016. Tropical West Pacific moisture dynamics and climate controls on rainfall isotopic ratios in southern Papua, Indonesia. Journal of Geophysical Research: Atmospheres, 121(5), pp.2222–2245.
Peters-Lidard, C. D. et al., 2017. Scaling, similarity, and the fourth paradigm for hydrology. Hydrology and Earth System Sciences, 21(7), pp.3701–3713.
Pigram, C. J. and Symonds, P. A., 1991. A review of the timing of the major tectonic events in the New Guinea Orogen. Journal of Southeast Asian Earth Sciences, 6(3), pp.307–318.
Ramage, C. S., 1968. Role of a tropical “maritime continent” in the atmospheric circulation. Monthly Weather Review, 96(6), pp.365 – 370.
Salinas, D., Flunkert, V., Gasthaus, J. and Januschowski, T., 2020. Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), pp.1181–1191.
Shen, S. S. P. and Somerville, R. C. J., 2019. Climate Mathematics: Theory and Applications. Cambridge: Cambridge University Press.
Smiley, D., 2012. PyNeurGen: Python Neural Genetic Algorithm Hybrids. Python library version 0.3.1.
Torrence, C. and Compo, G. P., 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), pp.61 – 78.
Wessel, P. et al., 2019. The Generic Mapping Tools Version 6. Geochemistry, Geophysics, Geosystems, 20(11), pp.5556–5564.
Wolter, K. and Timlin, M. S., 2011. El niño/southern oscillation behaviour since 1871 as diagnosed in an extended multivariate enso index (mei.ext). International Journal of Climatology, 31(7), pp.1074–1087.
Yamanaka, M. D., 2016. Physical climatology of Indonesian maritime continent: An outline to comprehend observational studies. Atmospheric Research, 178-179, pp.231–259.
Yang, S., Zhang, T., Li, Z. and Dong, S., 2019. Climate variability over the Maritime Continent and its role in global climate variation: A review. Journal of Meteorological Research, 33(6), pp.993–1015.
Yoden, S., Otsuka, S., Trilaksono, N. J. and Hadi, T. W., 2017. Recent Progress in Research on the Maritime Continent Monsoon. In Chapter 6, pp.63–77.