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研究生： 潘蘭娜
Parhusip, Miranda Anjelina
論文名稱： 發展以遙相關為基礎之Riau Islands月雨量預報模式
Identifying a Teleconnection-Informed Modeling Approach for Monthly Rainfall Prediction in the Riau Islands
指導教授： 陳憲宗
Chen, Shien-Tsung
學位類別： 碩士
Master
系所名稱： 工學院 - 自然災害減災及管理國際碩士學位學程
International Master Program on Natural Hazards Mitigation and Management
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 92
外文關鍵詞： Deseasonalized Anomaly, Teleconnections, Random Forest, Mutual Information, Lead Time, Riau Islands
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    • Prolonged hydro-climatic anomalies significantly threaten water resource management and socio-economic resilience in the Riau Islands Province. Because global operational climate models often struggle to resolve the non-linear rainfall dynamics of this equatorial archipelagic region, localized statistical approaches are critically needed. This study develops a machine-learning approach to predict monthly rainfall up to a three-month lead time at three key stations: Bintan, Dabo, and Tarempa. To eliminate deterministic seasonal biases, raw observations were first transformed into deseasonalized anomalies. A mutual information algorithm was then applied to isolate the most relevant global teleconnections and local predictors. These features were systematically evaluated across different predictor scenarios using a random forest regressor. The results indicate that a comprehensive data-driven architecture incorporating all significant large-scale climate drivers provided the most robust predictive performance across both coastal and open-ocean topographies. Maintaining predictive stability throughout the forecasting horizon, this localized model demonstrates higher predictive accuracy for micro-climatic variations compared to broad-scale dynamic models. Consequently, it serves as a powerful complementary tool that refines operational forecasts, bridging the spatial resolution gap over complex small-island terrains. Ultimately, this spatially adaptive modeling approach offers actionable lead times to support regional water security, agricultural planning, and climate adaptation strategies.

    ABSTRACT iii ACKNOWLEDGEMENT iv TABLE OF CONTENTS v LIST OF TABLES viii LIST OF FIGURES ix LIST OF ABBREVIATIONS x CHAPTER I INTRODUCTION 1 1.1. Background 1 1.2. Research Questions 2 1.3. Research Objectives 2 CHAPTER II LITERATURE REVIEW 4 2.1. Rainfall Characteristics in the Riau Islands 4 2.2. Global Climate Teleconnection Dynamics and Rainfall Variability 6 2.2.1. El Niño-Southern Oscillation (ENSO): Oceanic and Atmospheric Components 6 2.2.2. Indian Ocean Dipole (IOD) and Indian Ocean Basin-Wide (IOBW) Index 6 2.2.3. Madden–Julian Oscillation (MJO) 7 2.2.4. Regional Monsoon Circulation 7 2.2.5. Stratospheric Influence: Quasi-Biennial Oscillation (QBO) 8 2.2.6. Equatorial Waves (Kelvin and Rossby Waves) 8 2.2.7. Lagging Mechanisms in Teleconnections 9 2.3. Information Theory Approach: Linear Correlation and Mutual Information 9 2.4. Machine Learning Modeling Approach in Climatology 11 2.5. Prediction Strategy: Feature Scenario Approach 12 2.6. Operational Seasonal Forecasting and Dynamic Models 13 2.7. Research Gap 14 CHAPTER III DATA AND STUDY AREA 16 3.1. Study Area 16 3.2. Data and Data Sources 18 3.2.1. Surface Observation Data 18 3.2.2. Climate Teleconnection and Equatorial Waves Indices 18 3.2.3. Operational ECMWF Forecast Data 21 CHAPTER IV METHODOLOGY 22 4.1. Data Preprocessing and Climatological Anomaly Extraction 22 4.1.1. Calculation of Climatological Normal 23 4.1.2. Extraction of Anomaly Data 23 4.2. Dataset Partitioning (Data Splitting) 23 4.3. Mutual Information 24 4.4. Thresholding and Scenario Formulation 26 4.5. Random Forest-Based Regression Modeling 28 CHAPTER V RESULTS AND DISCUSSIONS 33 5.1. Climatological Characteristics of the Riau Islands 33 5.2. Development of Model Input Scenarios 35 5.2.1. Linear Relationship Analysis (Pearson Correlation) 35 5.2.2. Non-Linear Relationship Analysis (Mutual Information) 36 5.2.3. Determination of Selection Thresholds 38 5.2.4. Feature Selection and Scenario Construction 39 5.3. Evaluation and Optimization of the Random Forest Algorithm 44 5.3.1. Effectiveness of Anomaly vs. Observational Data 44 5.3.2. Performance Comparison of Scenarios on Anomaly Data 46 5.4. Spatio-Temporal Performance Analysis and Predictability Limits 54 5.4.1. Spatial Consistency and Lead Time Degradation 54 5.4.2. Error Diagnostics and Model Biases 56 5.5. Performance Comparison between the Random Forest Model and the Operational ECMWF Model 58 CHAPTER VI CONCLUSIONS AND RECOMMENDATIONS 61 6.1. Conclusion 61 6.2. Recommendation 62 REFERENCES 63 APPENDIX 67 APPENDIX A 67 APPENDIX B 70 APPENDIX C 76 APPENDIX D 82

    Aldrian, E., & Dwi Susanto, R. (2003). Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. International Journal of Climatology, 23(12), 1435–1452. https://doi.org/10.1002/joc.950
    Ariska, M., Suhadi, Supari, Irfan, M., & Iskandar, I. (2024). Detection of Dominant Rainfall Patterns in Indonesian Regions Using Empirical Orthogonal Function (EOF) and Its Relation with ENSO and IOD Events. Science and Technology Indonesia, 9(4), 1009–1023. https://doi.org/10.26554/sti.2024.9.4.1009-1023
    As-syakur, A. R., Adnyana, I. W. S., Mahendra, M. S., Arthana, I. W., Merit, I. N., Kasa, I. W., Ekayanti, N. W., Nuarsa, I. W., & Sunarta, I. N. (2014). Observation of spatial patterns on the rainfall response to ENSO and IOD over Indonesia using TRMM Multisatellite Precipitation Analysis (TMPA). International Journal of Climatology, 34(15), 3825–3839. https://doi.org/10.1002/joc.3939
    Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55. https://doi.org/10.1038/nature14956
    BMKG. (2024). Buletin Klimatologi Kepri Januari 2024. S. M. R. H. F. Tanjungpinang.
    Box, G. E. P., M. Jenkins, G., C. Reinsel, G., & Greta M. Ljung. (2015). Time Series Analysis: Forecasting and Control. Journal of Time Series Analysis, 37(5), 709–711. https://doi.org/10.1111/jtsa.12194
    BPS Kepri. (2023). Provinsi Kepulauan Riau dalam angka 2023. B. P. K. Riau.
    Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/https://doi.org/10.1023/A:1010933404324
    Chen, C., Sahany, S., Moise, A. F., Chua, X. R., Hassim, M. E., Lim, G., & Prasanna, V. (2023). ENSO–Rainfall Teleconnection over the Maritime Continent Enhances and Shifts Eastward under Warming. Journal of Climate, 36(14), 4635–4663. https://doi.org/10.1175/jcli-d-23-0036.1
    Chen, S. T. (2015). Mining Informative Hydrologic Data by Using Support Vector Machines and Elucidating Mined Data according to Information Entropy. Entropy, 17(3), 1023–1041. https://doi.org/10.3390/e17031023
    Cover, T. M., & Thomas, J. A. (2006). Element of Information Theory. John Wiley & Sons, Inc., Hoboken, New Jersey.
    Fatkhuroyan, Wati, T., Sukmana, A., & Kurniawan, R. (2018). Validation of Satellite Daily Rainfall Estimates Over Indonesia. Sinta, 33. (Universitas Muhammadiyah Surakarta)
    Friedman, T. H. R. T. J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer New York, NY. https://doi.org/https://doi.org/10.1007/978-0-387-84858-7
    Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes. Sci Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66
    Gergis, J. L., & Fowler, A. M. (2005). Classification of synchronous oceanic and atmospheric El Nino-Southern Oscillation (ENSO) events for palaeoclimate reconstruction. International Journal of Climatology, 25(12), 1541–1565. https://doi.org/10.1002/joc.1202
    Goddard, L., Mason, S. J., Zebiak, S. E., Ropelewski, C. F., Basher, R., & Cane, M. A. (2001). Current approaches to seasonal to interannual climate predictions. International Journal of Climatology, 21(9), 1111–1152. https://doi.org/10.1002/joc.636
    Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
    Guyon, I., & Elisseeff, A. e. (2003). An Introduction to Variable and Feature Selection. Machine Learning Research 3, 3. (JMLR.org)
    Hidayat, R., & Kizu, S. (2010). Influence of the Madden-Julian Oscillation on Indonesian rainfall variability in austral summer. International Journal of Climatology, 30(12), 1816–1825. https://doi.org/10.1002/joc.2005
    Hitchman, M. H., Yoden, S., Haynes, P. H., Kumar, V., & Tegtmeier, S. (2021). An Observational History of the Direct Influence of the Stratospheric Quasi-biennial Oscillation on the Tropical and Subtropical Upper Troposphere and Lower Stratosphere. Journal of the Meteorological Society of Japan. Ser. II, 99(2), 239–267. https://doi.org/10.2151/jmsj.2021-012
    IPCC. (2022). Climate Change 2022 – Impacts, Adaptation and Vulnerability. Intergovernmental Panel on Climate Change 2022. https://doi.org/10.1017/9781009325844
    James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R Introduction. Introduction to Statistical Learning: With Applications in R, 103, 1–14. https://doi.org/10.1007/978-1-4614-7138-7_1
    Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., & Monge-Sanz, B. M. (2019). SEAS5: the new ECMWF seasonal forecast system. Geoscientific Model Development, 12(3), 1087–1117. https://doi.org/10.5194/gmd-12-1087-2019
    Juneng, L., & Tangang, F. T. (2005). Evolution of ENSO-related rainfall anomalies in Southeast Asia region and its relationship with atmosphere–ocean variations in Indo-Pacific sector. Climate Dynamics, 25(4), 337–350. https://doi.org/10.1007/s00382-005-0031-6
    Kashinath, K., Mustafa, M., Albert, A., Wu, J. L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A.,…Prabhat. (2021). Physics-informed machine learning: case studies for weather and climate modelling. Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences, 379(2194). https://doi.org/ARTN 2020009310.1098/rsta.2020.0093
    Kiefero, S. M., Lerch, S., Ludwigo, P., & Pinto, J. G. (2024). Random Forests' Postprocessing Capability of Enhancing Predictive Skill on Subseasonal Time Scales-A Flow-Dependent View on Central European Winter Weather. Artificial Intelligence for the Earth Systems, 3(4). https://doi.org/ARTN e24001410.1175/AIES-D-24-0014.1
    Kiladis, G. N., Wheeler, M. C., Haertel, P. T., Straub, K. H., & Roundy, P. E. (2009). Convectively coupled equatorial waves. Reviews of Geophysics, 47(2). https://doi.org/10.1029/2008rg000266
    Kirono, D. G. C., Butler, J. R. A., McGregor, J. L., Ripaldi, A., Katzfey, J., & Nguyen, K. (2016). Historical and future seasonal rainfall variability in Nusa Tenggara Barat Province, Indonesia: Implications for the agriculture and water sectors. Climate Risk Management, 12, 45–58. https://doi.org/10.1016/j.crm.2015.12.002
    Knoben, W. J. M., Freer, J. E., & Woods, R. A. (2019). Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrology and Earth System Sciences, 23(10), 4323–4331. https://doi.org/10.5194/hess-23-4323-2019
    Kraskov, A., Stogbauer, H., & Grassberger, P. (2004). Estimating mutual information. Phys Rev E Stat Nonlin Soft Matter Phys, 69(6 Pt 2), 066138. https://doi.org/10.1103/PhysRevE.69.066138
    Krasnopolsky, V. M. (2013). The Application of Neural Networks in the Earth System Sciences. Springer, 46.
    Kug, J. S., Jin, F. F., & An, S. I. (2009). Two Types of El Nino Events: Cold Tongue El Nino and Warm Pool El Nino. Journal of Climate, 22(6), 1499–1515. https://doi.org/10.1175/2008jcli2624.1
    Kumar, K. K., Rajagopalan, B., & Cane, M. A. (1999). On the weakening relationship between the Indian monsoon and ENSO. Science, 284(5423), 2156–2159. https://doi.org/DOI 10.1126/science.284.5423.2156
    Kurniadi, A., Weller, E., Min, S. K., & Seong, M. G. (2021). Independent ENSO and IOD impacts on rainfall extremes over Indonesia. International Journal of Climatology, 41(6), 3640–3656. https://doi.org/10.1002/joc.7040
    Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241. https://doi.org/Doi 10.1029/1998wr900018
    Lorenz, E. N. (1963). Deterministic Nonperiodic Flow. Journal of the Atmospheric Sciences, 20(2), 130–141. https://doi.org/Doi 10.1175/1520-0469(1963)020<0130:Dnf>2.0.Co;2
    Moron, V., Robertson, A. W., & Boer, R. (2009). Spatial Coherence and Seasonal Predictability of Monsoon Onset over Indonesia. Journal of Climate, 22(3), 840–850. https://doi.org/10.1175/2008jcli2435.1
    Mubarrok, S., & Jang, C. J. (2022). Annual Maximum Precipitation in Indonesia and Its Association to Climate Teleconnection Patterns: An Extreme Value Analysis. Sola, 18(0), 187–192. https://doi.org/10.2151/sola.2022-030
    Nash J.E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(13), 282–290. https://doi.org/https://doi.org/10.1016/0022-1694(70)90255-6
    Peatman, S. C., Hassim, M. E. E., He, Y. J., Cheong, W. K., Moise, A. F., Ferrett, S. J., Lefort, T., Nguyen, H., Peyrillé, P., Wheeler, M. C., Xavier, P., & Zhang, C. D. (2026). The Madden-Julian Oscillation and Equatorial Waves in Operational Forecasting. Bulletin of the American Meteorological Society, 107(3), E742–E752. https://doi.org/10.1175/Bams-D-26-0034.1
    Peng, H. C., Long, F. H., & Ding, C. (2005). Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. Ieee Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/Doi 10.1109/Tpami.2005.159
    Qian, J.-H. (2008). Why Precipitation Is Mostly Concentrated over Islands in the Maritime Continent. Journal of the Atmospheric Sciences, 65(4), 1428–1441. https://doi.org/10.1175/2007jas2422.1
    Ramadhan, R., Helmi Yusnaini, Marzuki Marzuki, Zahwa Vieny Adha, Mutya Vonnisa, & Muharsyah, R. (2023). Diurnal Rainfall Pattern in Riau Islands as Observed by Rain Gauge and IMERG Data. Springer Proceedings in Physics, 290, 317. https://doi.org/https://doi.org/10.1007/978-981-19-9768-6
    Ramadhan, R., Yusnaini, H., Marzuki, M., Muharsyah, R., Suryanto, W., Sholihun, S., Vonnisa, M., Harmadi, H., Ningsih, A. P., Battaglia, A., Hashiguchi, H., & Tokay, A. (2022). Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales. Remote Sensing, 14(5). https://doi.org/10.3390/rs14051172
    Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
    Robertson, A. W., Qian, J.-H., & Moron, V. (2010). Interactions among ENSO, the Monsoon, and Diurnal Cycle in Rainfall Variability over Java, Indonesia. Journal of the Atmospheric Sciences, 67(11), 3509–3524. https://doi.org/10.1175/2010jas3348.1
    Saji, N. H., Goswami, B. N., Vinayachandran, P. N., & Yamagata, T. (1999). A dipolemode in the tropical Indian Ocean. Nature, 401.
    Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/DOI 10.1002/j.1538-7305.1948.tb01338.x
    Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A strategy for system predictor identification. Journal of Hydrology, 239(1-4), 232–239. https://doi.org/Doi 10.1016/S0022-1694(00)00346-2
    Supari, Tangang, F., Salimun, E., Aldrian, E., Sopaheluwakan, A., & Juneng, L. (2017). ENSO modulation of seasonal rainfall and extremes in Indonesia. Climate Dynamics, 51(7-8), 2559–2580. https://doi.org/10.1007/s00382-017-4028-8
    Tjasyono, B. H. K. (2008). Meteorologi Indonesia Volume II: Awan dan Hujan Monsun. Badan Meteorologi, Klimatologi, dan Geofisika (BMKG).
    Trenberth, K. E. (1997). The definition of El Nino. Bulletin of the American Meteorological Society, 78(12), 2771–2777. https://doi.org/10.1175/1520-0477(1997)078<2771:Tdoeno>2.0.Co;2
    Tyralis, H., Papacharalampous, G., & Langousis, A. (2019). A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources. Water, 11(5). https://doi.org/ARTN91010.3390/w11050910
    Wheeler, M. C., & Hendon, H. H. (2017). An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Monthly Weather Review, 145(5), 1917–+.
    Wilks, D. S. (2011). Statistical Methods in the Atmospheric Sciences (J. Helé, Ed. 2 ed., Vol. 91). Elsevier.
    WMO. (2017). WMO Guidelines on the Calculation of Climate Normals. World Meteorological Organization (WMO).

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