Multi-Variable Agrometeorological Parameter Combination for Drought Early Warning System Using Hybrid SSA–AMBP Algorithm
DOI:
10.33395/sinkron.v10i2.15885Keywords:
drought; early warning system; singular spectrum analysis; adaptive model-based prediction; agrometeorological parametersAbstract
Drought is one of the hydrometeorological disasters that has a significant impact on the agricultural sector, water availability, and food security, thus requiring an accurate and adaptive early warning system. This study aims to develop and evaluate a drought Early Warning System (EWS) model based on a combination of multi-variable agrometeorological parameters using a hybrid approach of Singular Spectrum Analysis (SSA) and Adaptive Model-Based Prediction (AMBP). The agrometeorological data used includes rainfall, air temperature, humidity, solar radiation, wind speed, and other supporting variables processed in the form of monthly time series over a period of ten years. The SSA method is used to perform signal denoising and extract dominant components from the data, while AMBP is applied as an adaptive predictive model to generate SPI-6 drought index forecasts. Model performance is evaluated using RMSE and the coefficient of determination (R²) in the model training and evaluation phases. The results show that the hybrid SSA–AMBP model has the best performance compared to single methods, with an RMSE value of 0.149 and R² of 0.983 in the model training phase, and an RMSE of 0.176 and R² of 0.941 in the model evaluation phase. In addition, the 2026 prediction results show a seasonal pattern with indications of a moderate dry period from October to November. These findings indicate that the developed model demonstrates relatively high predictive accuracy and stability based on RMSE and R² evaluation metrics within the SPI-6 dataset used in this study, and it has the potential to serve as a conceptual basis for decision-support in drought risk mitigation, water resource management, and sustainable agricultural planning.
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Ahire, A., Zade, N., Mujawar, U., Mehta, D., & Kotecha, K. (2025). MethodsX Meteorological drought severity forecasting utilizing blended. MethodsX, 15(June), 103456. https://doi.org/10.1016/j.mex.2025.103456
Aswi, A., & Ahmar, A. S. (2025). Rainfall Forecasting Using the Singular Spectrum Analysis ( SSA ) Method. Jurnal Varian. 8(2), 233–248. https://doi.org/10.30812/varian.v8i2.4571.
Base, B., Base, B., Base, B., & Base, B. (2025). Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction iScience Article. ISCIENCE, 28(5), 112360. https://doi.org/10.1016/j.isci.2025.112360
Elisa, T., Edmond, M., & Tsubo, M. (2025). Employing a metric to quantify the effectiveness of an agricultural drought early warning system during the fourth industrial revolution. Computers and Electronics in Agriculture, 230(July 2024), 109906. https://doi.org/10.1016/j.compag.2025.109906
Gupta, B. B., Gaurav, A., Attar, R. W., & Arya, V. (2024). Advance drought prediction through rainfall forecasting with hybrid deep learning model. Scientific Reports, 1–14 https://doi.org/10.1038/s41598-024-80099-6.
Harsanto, B., Kasumaningrum, Y., Arviansyah, M. R., Ym, A., Purnomo, D., Iskandar, Y., Dwija, I., & Inda, D. (2025). Current Research in Food Science Leveraging disruptive technologies for food security : A systematic review on agricultural supply chain resilience to climate change. Current Research in Food Science, 10(March), 101079. https://doi.org/10.1016/j.crfs.2025.101079
Houmma, I. H., Gadal, S., Mansouri, L. El, Gbetkom, P. G., Badamassi, M., Barkawi, M., Hanad, I., & Hadria, R. (2023). A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems based on random forest algorithm and remote sensing. Geomatics, Natural Hazards and Risk, 14(1). https://doi.org/10.1080/19475705.2023.2223384
Lalika, C., Ul, A., Mujahid, H., James, M., & Lalika, M. C. S. (2024). Journal of Hydrology : Regional Studies Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania. Journal of Hydrology: Regional Studies, 53, 101794. https://doi.org/10.1016/j.ejrh.2024.101794
Lenczuk, A., Ndehedehe, C., Klos, A., & Bogusz, J. (2024). Remote Sensing of Environment A new Multivariate Drought Severity Index to identify short-term hydrological signals : case study of the Amazon River basin. Remote Sensing of Environment, 315(October), 114464. https://doi.org/10.1016/j.rse.2024.114464
Mariyanto, J., Muda, A. (2025). Krisis Global dan Implikasinya bagi Pertanian Indonesia: Perubahan Iklim, Konflik Geopolitik, dan Spekulasi Pasar. Jurnal Perencanaan Pembangunan Pertanian, 2(1), 22–43 https://epublikasi.pertanian.go.id/berkala/jp3/article/view/4056.
Miralles, D. G., Peng, J., Dyer, E., Talib, J., Beck, H. E., & Singer, M. B. (2025). Warming accelerates global drought severity. Nature 642(October 2024). https://doi.org/10.1038/s41586-025-09047-2
Reddy, P. S., Sivanandini, B., Chaudhary, M., & Gautam, N. (2025). Adaptive ML and DL framework for climate-reseilient agriculture. International Journal of Advanced Education and Research, 10(2), 65–71 https://www.alleducationjournal.com/assets/archives/2025/vol10issue2/10034.pdf.
Ruslana, Z. N., & Zuliarso, E. (2025). Rainfall Forecasting Using SSA-Based Hybrid Models with LSSVR and LSTM for Disaster Mitigation. Jurnal Teknik Informatika (JUTIF), 6(4), 2079–2106 https://doi.org/10.52436/1.jutif.2025.6.4.4963.
Satapathy, T., & Dietrich, J. (2024). Agricultural drought monitoring and early warning at the regional scale using a remote sensing ‑ based combined index. Environmental Monitoring and Assessment, 196(11), 1–27. https://doi.org/10.1007/s10661-024-13265-y
Sentian, J., Payus, C. M., Herman, F., Wan, V., & Kong, Y. (2022). Climate change scenarios over Southeast Asia. APN Science Bulletin, 12(1), 102–122. https://doi.org/10.30852/sb.2022.1927
Shaharudin, S. M., Setiawan, E. P., & Wutsqa, D. U. (2024). Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis. Journal of Intelligent Systems & Internet of Things, 11(01), 29–43 doi:10.54216/JISIoT.110104.
Silamat, E., Ruruh, A., Syaiful, M., & Ninasari, A. (2024). Dampak Perubahan Iklim Terhadap Peningkatan Dan Penurunan Produktivitas Tanaman Pangan, Jurnal Review Pendidikan dan Pengajaran, Volume 7 Nomor 3, 2024 | 10189. 7, 10189–10195 https://doi.org/10.31004/jrpp.v7i3.31609.
Sukoharjo, K. (2023). Identifikasi Kekeringan Lahan Pertanian Berdasarkan Metode Temperature Vegetation Dryness Index (Tvdi) pada Citra Landsat-8 Oli/Tirs di Kabupaten Madiun Jawa Timur, Universitas Muhammadiyah Surakarta, Indonesia.
Wang, J., Liu, W., & Yin, D. (2025). Impacts of integrated meteorological and agricultural drought on global maize yields. Agricultural Water Management, 318(April), 109727. https://doi.org/10.1016/j.agwat.2025.109727
Xiao, X., Ming, W., Luo, X., Yang, L., Li, M., & Yang, P. (2024). Leveraging multisource data for accurate agricultural drought monitoring : A hybrid deep learning model. Agricultural Water Management, 293(September 2023), 108692. https://doi.org/10.1016/j.agwat.2024.108692
Yuan, M., Guojing, G., Bu, J., Su, Y., Ma, H., & Liu, X. (2025). A global drought dataset for Multivariate Composite Drought Index ( MCDI ) and its constituent drought indices. Scientific Data 1–15. https://doi.org/10.5061/dryad.z612jm6bt
Zubair, M., Zafar, Z., Yao, S., Guo, Z., Ahmad, A., & Fahd, S. (2025). Agricultural drought forecasting using remote sensing : A hybrid modeling framework by integrating wavelet transformation and machine learning techniques. Agricultural Water Management, 321(October), 109922. https://doi.org/10.1016/j.agwat.2025.109922
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Copyright (c) 2026 Syahrull Rezaa, Muhammad Imam Dinata , Anggraini, Syaharuddin
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