Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting
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Abstract
Rainfall forecasts are essential for human activities enabling communities to anticipate any impacts. Rainfall events correlate with other natural and hydro-meteorological phenomena, which can be used in modeling and prediction. This study used daily CHIRPS for the Gajahwong watershed in Yogyakarta, Indonesia as the precipitation data. It also used Sea Surface Temperature, Land Surface Temperature (Day and Night), Minimum and Maximum Temperatures, Solar Radiation, Wind Speed (U and V components), Cloud Pressure (Top and Base), and Cloud Height (Top and Base) as the parameters. Further, data processing was performed by means of the Google Earth Engine (GEE) platform. Machine learning methods, including Support Vector Regression, Gradient Boosting Regression, Random Forest, and Deep Neural Networks, were applied. The correlation analysis revealed that only the Wind Speed V-component showed significant correlation with rainfall, other seven parameters showed moderate and four showed weak ones. Meanwhile, accuracy assessments indicated that Support Vector Regression had the most accurate predictions accompanied by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, and Coefficient Correlation (CC) at 1.366, 0.947, 1.866, 0.948 and 0.982 respectively. This study demonstrated that utilizing openly accessible atmospheric datasets processed through the GEE could yield reliable rainfall predictions, facilitating informed decisions on a wide scale. The methodology is adaptable and can be reproduced for any comparable research or operational purposes.
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Cahyono, B. K., Ummah, M. H., Andaru, R., Andika, N., Pamungkas, A., Handayani, H. H., Atmodiwirjo, P., & Nathan, R. (2025). Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting. Communications in Science and Technology, 10(1), 135-147. https://doi.org/10.21924/cst.10.1.2025.1638
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Cahyono, B.K., Aditya, T., Istarno The Least Square Adjustment for Estimating The Tropical Peat Depth using LiDAR Data. Remote Sens. 12 (2020) 1–22.
Amini, A., Dolatshahi, M., Kerachian, R. Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models. J. Hydrol. 631 (2024) 130804.
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Salehi, S., Kavgic, M., Bonakdari, H., Begnoche, L. Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density: Exploring single and hybrid deep learning models. Energy AI 16 (2024) 100343.
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Li, H., Li, S., Ghorbani, H. Data-driven novel deep learning applications for the prediction of rainfall using meteorological data. Front. Environ. Sci. 12 (2024) 1–15.
Rincón-Avalos, P., Khouakhi, A., Mendoza-Cano, O., López-De la Cruz, J., Paredes-Bonilla, K.M. Evaluation of satellite precipitation products over Mexico using Google Earth Engine. J. Hydroinformatics 24 (2022) 711–729.
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Suprayogi, S., Purnama Sari, S., Setiacahyandari, H.K. Flood Risk Analysis in Gajah Wong River, Yogyakarta City. J. Ilmu Lingkung. 22 (2024) 1033–1040.
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Parmann, L.D., Paarmann, L.D. Design and analysis of analog filters: a signal processing perspective, Springer Science & Business Media: New York, 617 (2001).
Narejo, S., Jawaid, M.M., Talpur, S., Baloch, R., Pasero, E.G.A. Multi-step rainfall forecasting using deep learning approach. PeerJ. Comput. Sci. 7 (2021) e514.
Heddam, S., Kim, S., Danandeh Mehr, A., Zounemat-Kermani, M., Elbeltagi, A., Malik, A., Kisi, O. Chapter 11 - A long short-term memory deep learning approach for river water temperature prediction. In Intelligent Data-Centric Systems, Academic Press, (2022) 243–270.
Sui, X., He, S., Vilsen, S.B., Meng, J., Teodorescu, R., Stroe, D.I. A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery. Appl. Energy 300 (2021) 117346.
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Umoh, U.A., Eyoh, I.J., Murugesan, V.S., Nyoho, E.E. Chapter 14 - Fuzzy-machine learning models for the prediction of fire outbreaks: A comparative analysis. Academic Press, (2022) 207–233.
Wang, H., Liu, Y., Zhou, B., Li, C., Cao, G., Voropai, N., Barakhtenko, E. Taxonomy research of artificial intelligence for deterministic solar power forecasting. Energy Convers. Manag. 214 (2020) 112909.
Marimuthu, R., Shivappriya, S.N., Saroja, M.N. Chapter 14 - A study of machine learning algorithms used for detecting cognitive disorders associated with dyslexia. Academic Press, (2021) 245–262.
Breiman, L. Random forests. Mach. Learn. 45 (2001) 5–32.
Rodriguez-Galiano, V., Mendes, M.P., Garcia-Soldado, M.J., Chica-Olmo, M., Ribeiro, L. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Sci. Total Environ.476 (2014) 189–206.
Nurwatik, N., Ummah, M.H., Cahyono, A.B., Darminto, M.R., Hong, J.-H. A Comparison Study of Landslide Susceptibility Spatial Modeling Using Machine Learning. ISPRS Int. J. Geo-Information 11 (2022) 602.
Friedman, J.H. Greedy function approximation: a gradient boosting machine. Ann. Stat. (2001) 1189–1232.
Otchere, D.A., Ganat, T.O.A., Ojero, J.O., Tackie-Otoo, B.N., Taki, M.Y. Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. J. Pet. Sci. Eng. 208 (2022) 109244.
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