Innovation in Crop Nutrition Planning Based on Rainfall Prediction Using Singular Spectrum Analysis and Boosting to Optimize Agricultural Management
DOI:
https://doi.org/10.37385/z3mgdv08Keywords:
LightGBM, Prediction, Rainfall, Singular Spectrum Analysis, XGBoostAbstract
The high variability of rainfall in tropical climates presents a major challenge for agricultural management, as weather uncertainty often leads to inefficient fertilization practices due to nutrient loss. This study aims to develop a robust framework for rainfall prediction, which can inform a flexible and precise crop nutrient scheduling system. Utilizing an hourly rainfall dataset (n=6,624) obtained from IoT sensors, the research proposes an approach that integrates Singular Spectrum Analysis (SSA) for signal decomposition and noise reduction with Gradient Boosting algorithms (LightGBM and XGBoost). Spline interpolation was employed to handle missing data, while SSA served to disentangle deterministic trends from random noise, enabling the models to perform more effectively on the refined dataset. Empirical evaluation demonstrates that the SSA-XGBoost hybrid model achieves superior performance, with an RMSE of 0.0057 and an R² of 0.8278, significantly outperforming the SSA-LightGBM model (R² 0.2879), which struggled to capture non-linear patterns within this dataset. The high predictive accuracy of the SSA-XGBoost model facilitates the implementation of responsive nutrient management strategies, wherein fertilizer application can be deferred during forecasted periods of high rainfall to prevent runoff and environmental pollution. This research contributes to the field of hydroinformatics by demonstrating the effectiveness of combining SSA and XGBoost as a cost-efficient yet high-performance solution for mitigating climate-related risks in tropical wetland agriculture.
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Alkaff, M., Khatimi, H., Puspita, W., & Sari, Y. (2019). Modelling and predicting wetland rice production using support vector regression. Telkomnika (Telecommunication Computing Electronics and Control), 17(2), 819–825. https://doi.org/10.12928/TELKOMNIKA.V17I2.10145
Arassah, F. I., Sadik, K., Sartono, B., & Sofan, P. (2025). Optimizing Machine Learning for Daily Rainfall Prediction in Bogor: A Statistical Downscaling Approach. Eduvest - Journal of Universal Studies, 5(6), 7006–7018. https://doi.org/10.59188/EDUVEST.V5I6.51307
Baskara, A. R., Maulida, M., Lestiyanto, M. T. M., Sari, Y., Mustamin, N. F., & Wijaya, E. S. (2024). Explicit Content Classification in Indonesian Song Lyrics Using the LSTM-CNN Method. 2024 9th International Conference on Informatics and Computing, ICIC 2024, 1–6. https://doi.org/10.1109/ICIC64337.2024.10956794
Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3). https://doi.org/10.1007/s10462-020-09896-5
Bui, Q. T., Chou, T. Y., Hoang, T. Van, Fang, Y. M., Mu, C. Y., Huang, P. H., Pham, V. D., Nguyen, Q. H., Anh, D. T. N., Pham, V. M., & Meadows, M. E. (2021). Gradient boosting machine and object‐based cnn for land cover classification. Remote Sensing, 13(14). https://doi.org/10.3390/rs13142709
Cao, Q., Wu, Y., Yang, J., & Yin, J. (2023). Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM. Applied Sciences (Switzerland), 13(3). https://doi.org/10.3390/app13031610
Cao, T. T., Anh Le, H., & Eppe, G. (2025). Nutrient dynamics, environmental impacts, and feed efficiency in intensive whiteleg shrimp (Litopenaeus vannamei) farming on sandy soils in Ninh Thuan, Vietnam. Aquaculture Reports, 44, 103050. https://doi.org/10.1016/J.AQREP.2025.103050
Cui, Z., Qing, X., Chai, H., Yang, S., Zhu, Y., & Wang, F. (2021). Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis. Journal of Hydrology, 603.
Ding, X., Tao, Y., Luo, J., Xi, H., Tao, Q., Li, J., Huang, C., Du, F., & Ou, W. (2025). How do water quality requirements and spatial flows shape the supply-demand balance of water provision ecosystem services? Evidence from the Taihu Lake Basin. Applied Geography, 183, 103723. https://doi.org/10.1016/J.APGEOG.2025.103723
Diniaty, D., Fauzi, A. M., Sunarti, T. C., Raharja, S., & Helmi, F. (2024). Determination of Superior Commodities For The Development of Small and Medium Industries in Kampar Regency. Journal of Applied Engineering and Technological Science (JAETS), 5(2), 995–1010. https://doi.org/10.37385/JAETS.V5I2.4727
Diykh, M., Ali, M., Farooque, A. A., Aldhafeeri, A. A., Jamei, M., & Labban, A. (2026). A robust artificial intelligence informed over complete rational dilation wavelet transform technique coupled with deep learning for long-term rainfall prediction. Engineering Applications of Artificial Intelligence, 165, 113426. https://doi.org/10.1016/J.ENGAPPAI.2025.113426
Dong, S. (2024). Precipitation Prediction Using Long Short-Term Memory Networks: Improving Seasonal Rainfall Forecast Accuracy for Flood and Drought Prevention. Proceedings - 2024 International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024, 345–349. https://doi.org/10.1109/AEECA62331.2024.00067
Dupas, R., Faucheux, M., Senga Kiessé, T., Casanova, A., Brekenfeld, N., & Fovet, O. (2024). High-intensity rainfall following drought triggers extreme nutrient concentrations in a small agricultural catchment. Water Research, 264, 122108. https://doi.org/10.1016/J.WATRES.2024.122108
Elbasiouny, H., El-Ramady, H., Elbehiry, F., Rajput, V. D., Minkina, T., & Mandzhieva, S. (2022a). Plant Nutrition under Climate Change and Soil Carbon Sequestration. Sustainability (Switzerland), 14(2), 1–20. https://doi.org/10.3390/su14020914
Elbasiouny, H., El-Ramady, H., Elbehiry, F., Rajput, V. D., Minkina, T., & Mandzhieva, S. (2022b). Plant Nutrition under Climate Change and Soil Carbon Sequestration. Sustainability 2022, Vol. 14, 14(2). https://doi.org/10.3390/SU14020914
Getachew, B., Kewessa, G., Hailu, W., & Girma, G. (2025). Observed climate trends and farmers’ adaptation strategies in Dendi District, West Shewa Zone, Ethiopia. Climate Services, 38, 100548. https://doi.org/10.1016/J.CLISER.2025.100548
Ghosh, S., Mukhoti, S., & Sharma, P. (2025). Quantifying rainfall-induced climate risk in rainfed agriculture: A volatility-based time series study from semi-arid India. Agricultural Water Management, 319, 109775. https://doi.org/10.1016/J.AGWAT.2025.109775
Hameed, M. M., Masood, A., Hamid, A., Elbeltagi, A., Razali, S. F. M., & Salem, A. (2025). Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models. PLOS ONE, 20(5), e0321008. https://doi.org/10.1371/JOURNAL.PONE.0321008
Hu, F., Yang, Q., Yang, J., Shao, J., & Wang, G. (2025). An adaptive rainfall-runoff model for daily runoff prediction under the changing environment: Stream-LSTM. Environmental Modelling & Software, 191, 106524. https://doi.org/10.1016/J.ENVSOFT.2025.106524
Jayarani, P., Channa Basava, P., Nagaraj, P., & Kusuma Latha, P. (2024). Optimizing fertilizer usage in agriculture with AI Driven Recommendations. 21(1), 628. https://doi.org/10.0805/Jbse.2024577123
Jiang, H., Zhang, Y., Qian, C., & Wang, X. (2024). Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression. Ocean Modelling, 189, 102364. https://doi.org/10.1016/J.OCEMOD.2024.102364
Johny, K., Pai, M. L., & S., A. (2022). A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction. Applied Soft Computing, 123, 108941. https://doi.org/10.1016/J.ASOC.2022.108941
Komatsu, K. J., Reinhart, K., Alley, S., Porensky, L. M., Wilcox, K. R., & Koerner, S. E. (2026). Sensitivity of soil nutrient pools, but stability of microbial processes, under reduced rainfall and altered grazing management in northern mixed-grass prairie. Soil Biology and Biochemistry, 214, 110071. https://doi.org/10.1016/J.SOILBIO.2025.110071
Kumar, G. D., Pradhan, K. C., & Tyagi, S. (2024). Deep Learning Forecasting: An LSTM Neural Architecture based Approach to Rainfall and Flood Impact Predictions in Bihar. Procedia Computer Science, 235, 1455–1466. https://doi.org/10.1016/J.PROCS.2024.04.137
Kumar, V., Kedam, N., Sharma, K. V., Khedher, K. M., & Alluqmani, A. E. (2023). A Comparison of Machine Learning Models for Predicting Rainfall in Urban Metropolitan Cities. Sustainability (Switzerland), 15(18). https://doi.org/10.3390/su151813724
Li, C., Jiang, H., Zhou, R., Dou, Y., Shen, Z., Zhang, L., Qian, X., & Mao, J. (2026). Hypergraph attention and periodic fusion learning for enhanced flight delay prediction. Information Fusion, 129, 104076. https://doi.org/10.1016/J.INFFUS.2025.104076
Li, J., Zhu, X., Xu, Q., Ao, L., Yu, X., & Zhang, S. (2025). Coupling of vegetation filter strips and biochar reduced the loss of nitrogen and phosphorus nutrients in subtropical riparian areas under continuous rainfall conditions. Environmental Technology & Innovation, 40, 104473. https://doi.org/10.1016/J.ETI.2025.104473
Li, S., Wei, J., Xu, H., Lu, L., Gao, A., Jiang, J., Yu, Z., & Feng, H. (2025). Rainfall-derived dissolved organic matter and nutrients in the Chaohu Lake watershed: Urban source tracing and ecological risk insight. Journal of Environmental Chemical Engineering, 13(5), 119020. https://doi.org/10.1016/J.JECE.2025.119020
Lin, X., Yang, Z., Lin, X., Zhong, W., & Zhang, H. (2026). A non-intrusive load monitoring method for commercial buildings based on time-series periodicity adaptive fusion. Energy and Buildings, 354, 117031. https://doi.org/10.1016/J.ENBUILD.2026.117031
Liu, N., Guo, T., Zhang, H., Yang, G., Wei, B., Xu, H., Ren, H., Badgery, W., Kemp, D., Nie, Z., Lee, M. R. F., Rillig, M. C., & Zhang, Y. (2025). Optimized grazing management enhances multiple ecosystem services by maintaining plant diversity and dominance in grasslands. One Earth, 8(7), 101319. https://doi.org/10.1016/J.ONEEAR.2025.101319
Luo, J., Zhang, Z., Fu, Y., & Rao, F. (2021). Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms. Results in Physics, 27. https://doi.org/10.1016/j.rinp.2021.104462
Margenot, A. J., & Lee, J. (2023). The fate of nitrogen of ammonium phosphate fertilizers: A blind spot. Agricultural and Environmental Letters, 8(2). https://doi.org/10.1002/AEL2.20116
Mashooq, B., Singh, Y., & Manzoor, S. I. (2025). Federated Learning-based Framework for Rainfall Prediction in Smart Agriculture. Procedia Computer Science, 259, 2004–2013. https://doi.org/10.1016/J.PROCS.2025.04.156
Mehdizavareh, H., Khan, A., & Cichosz, S. L. (2025). Enhancing glucose level prediction of ICU patients through hierarchical modeling of irregular time-series. Computational and Structural Biotechnology Journal, 27, 2898–2914. https://doi.org/10.1016/J.CSBJ.2025.06.039
Mora-Motta, D., Llanos-Cabrera, M. P., Chavarro-Bermeo, J. P., Estrada-Bonilla, G. A., & Silva-Olaya, A. M. (2025). Soil quality responses of a tropical Andisol to contrasting land-use systems: Insights from multiple indexing approaches. Science of The Total Environment, 1002, 180648. https://doi.org/10.1016/J.SCITOTENV.2025.180648
Murumkar, A., Tapas, M., Martin, J., Kalcic, M., Shedekar, V., Goering, D., Thorstensen, A., Boles, C., Redder, T., & Confesor, R. (2025). Advancing SWAT modeling with rainfall risk-based fertilizer timing to improve nutrient management and crop yields. Agricultural Water Management, 316, 109555. https://doi.org/10.1016/J.AGWAT.2025.109555
Narimani, R., Jun, C., Saedi, A., Bateni, S. M., & Oh, J. (2023). A multivariate decomposition–ensemble model for estimating long-term rainfall dynamics. Climate Dynamics, 61(3–4), 1625–1641. https://doi.org/10.1007/s00382-022-06646-x
Olaleye, O. S., Faloye, O. T., Akintola, O. A., Babalola, T. E., Ogunrinde, A. T., Akinremi, J. O., Jimoh, R. A., Alonge, A. E., & Sodiq, K. A. (2026). Assessment of rainfall Spatio-temporal trends and climate variability for sustainable agriculture and environmental resilience in parts of Ekiti state, south West Nigeria. Ecological Frontiers. https://doi.org/10.1016/J.ECOFRO.2025.12.020
Paramesha, V., Kumar, P., Prabhakar, M., Gopinath, K. A., Ravisankar, N., Mohan Kumar, R., Jyoti Nath, A., Jinger, D., & Bhattacharjee, S. (2025). Assessing the impact of nutrient management on productivity, economics, soil quality, energy efficiency, and life cycle assessment in rice-based farming systems. Journal of Agriculture and Food Research, 23, 102278. https://doi.org/10.1016/J.JAFR.2025.102278
Pringandana, C. G. L., & Kusnawi, K. (2025). A Comparative Analysis of Hyperparameter-Tuned XGBoost and LightGBM for Multiclass Rainfall Classification in Jakarta. Jurnal Teknik Informatika (Jutif), 6(4), 2467–2483. https://doi.org/10.52436/1.jutif.2025.6.4.4965
Puspasari, R. L., Yoon, D., Kim, H., & Kim, K. W. (2023). Machine Learning for Flood Prediction in Indonesia: Providing Online Access for Disaster Management Control. Economic and Environmental Geology, 56(1), 65–73. https://doi.org/10.9719/EEG.2023.56.1.65
Qin, D., Tominaga, R., & Saneoka, H. (2023). Uptake and Use Efficiency of Major Plant Nutrients for Climate-Resilient Agriculture. Climate-Resilient Agriculture, 2, 35–50. https://doi.org/10.1007/978-3-031-37428-9_2
Sahin, E. K. (2020). Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest. SN Applied Sciences, 2(7), 1–17. https://doi.org/10.1007/s42452-020-3060-1
Samad, A., Bhagyanidhi, Gautam, V., Jain, P., Sangeeta, & Sarkar, K. (2020). An Approach for Rainfall Prediction Using Long Short Term Memory Neural Network. 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020, 190–195. https://doi.org/10.1109/ICCCA49541.2020.9250809
Sawah, M. S. (2026). A novel feature selection-driven framework for sustainable and efficient rainfall classification using machine learning models. Unconventional Resources, 9, 100286. https://doi.org/10.1016/J.UNCRES.2025.100286
Singh, A., Kiboi, M., Bhullar, G. S., Sisodia, B. S., Bautze, D., Patidar, I., Konde, N., Ledroit, C., & Riar, A. (2025). Long-term impacts of farming systems and preceding crops on soil organic carbon and physiochemical properties in Vertisols of subtropical India. Soil Advances, 4, 100083. https://doi.org/10.1016/J.SOILAD.2025.100083
Sutanto, S. J., Paparrizos, S., Nauta, L., Supit, I., Lefèvre, V., Kranjac-Berisavljevic, G., Gandaa, B. Z., Dogbey, R., Jamaldeen, B. M., & Ludwig, F. (2025). DROP app: A hydroclimate information service to deliver scientific rainfall, local rainfall, and soil moisture forecasts for agricultural decision-making. Heliyon, 11(4), e42740. https://doi.org/10.1016/J.HELIYON.2025.E42740
Tian, Y., Fu, W., Xiang, Y., Xiong, Q., Cui, R., Ao, Z., & Lei, X. (2025). Urban real-time rainfall-runoff prediction using adaptive SSA-decomposition with dual attention. Journal of Hydrology, 653, 132701. https://doi.org/10.1016/J.JHYDROL.2025.132701
Valladares-Castellanos, M., de Jesús Crespo, R., Xu, Y. J., & Douthat, T. H. (2024). A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico. Science of The Total Environment, 949, 175111. https://doi.org/10.1016/J.SCITOTENV.2024.175111
Wang, C., Zhang, Q., Tao, M., Hu, H., Xue, C., Xue, F., & Dong, Z. (2026). Estimation of seasonal ecological water demand in arid zone of Northwest China: An approach using the LSTM-random forest regression model. Journal of Environmental Management, 397, 128240. https://doi.org/10.1016/J.JENVMAN.2025.128240
Wang, R., Chen, Y., Wu, H., Liu, J., Wang, M., & Duan, J. (2026). A flood susceptibility prediction method for climate change scenarios driven by coupled land simulation and spatiotemporal dual convolution synergy. Journal of Hydrology, 664, 134366. https://doi.org/10.1016/J.JHYDROL.2025.134366
Wang, W., Wen, X., Zhang, M., Wang, Y., Zheng, Y., Gong, M., & Liu, D. (2025). A gate-aware GRU model with trend-residual decomposition and quantile regression for remaining useful life prediction of IGBT. Microelectronics Journal, 165, 106852. https://doi.org/10.1016/J.MEJO.2025.106852
Wang, Y., Su, Y., Zheng, Z., Zhou, Z., & Wang, X. (2026). Bayesian-optimized CNN-LSTM neural network for predicting road construction dust concentrations. Developments in the Built Environment, 25, 100843. https://doi.org/10.1016/J.DIBE.2026.100843
Wibawa, T. S., Ningrum, N. K., & Syahreza, A. (2025). Comparison of CatBoost and LightGBM Models for Air Humidity Prediction. Journal of Applied Informatics and Computing, 9(3), 803-809.
Wu, S., Kong, L., Wang, A., & Feng, X. (2025). Thermal error prediction of CNC Swiss-type lathe under variable operating conditions based on symbolic regression and time series mixup enhancement. CIRP Journal of Manufacturing Science and Technology, 63, 362–374. https://doi.org/10.1016/J.CIRPJ.2025.09.018
Yamada, H., Yamasaki, T., Kanuma, N., & Nishimura, T. (2025). Application of GeoWEPP to a cabbage monocropping region: developing agricultural strategies to mitigate water erosion under short-duration intense rainfall. CATENA, 258, 109242. https://doi.org/10.1016/J.CATENA.2025.109242
Yang, X., Wong, S. C., Ge, L. Y., Chan, W. P., & Lisak, G. (2025). Enhancing vegetable yield and quality in urban farming under soil and soil-less conditions: the synergistic role of biochar in food security and waste management. Journal of Cleaner Production, 513, 145724. https://doi.org/10.1016/J.JCLEPRO.2025.145724
Yang, Y., & Ma, Q. (2025). From deficit to balance: Identifying blue-green infrastructure networks based on trade-offs and synergies between water and terrestrial ecosystem services in a water sensitive region. Ecohydrology & Hydrobiology, 25(4), 100656. https://doi.org/10.1016/J.ECOHYD.2025.100656
Yao, X., Fu, X., & Zong, C. (2022). Short-Term Load Forecasting Method Based on Feature Preference Strategy and LightGBM-XGboost. IEEE Access, 10(June), 75257–75268. https://doi.org/10.1109/ACCESS.2022.3192011
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