Hybrid Machine Learning Model for Rainfall Prediction Using Time-Series Data
Hybrid Machine Learning Model for Rainfall Prediction Using Time-Series Data |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : Abdikafi Elmi Abdishakur, Abdihamid Mohamed Tahlil, Abdisalam Abdullahi Aden | ||
| DOI : 10.14445/22315381/IJETT-V73I6P103 | ||
How to Cite?
Abdikafi Elmi Abdishakur, Abdihamid Mohamed Tahlil, Abdisalam Abdullahi Aden, "Hybrid Machine Learning Model for Rainfall Prediction Using Time-Series Data," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.17-16, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P103
Abstract
Weather forecasting is important in sectors ranging from farming to transport and disasters, to mention but a few. This paper discusses the effectiveness of three machine learning techniques: LSTM, GBM, and the combined LSTM-GBM model in rainfall prediction based on temperature and humidity data obtained from the chosen sites in Metro Manila and Rizal province. Also presented in the given dataset are data forecasted using simple linear regression, Gauss-Newton and Nernst-based non-linear, and a fourteen-gene-based genetic programming regression. This has been coupled with a high accuracy and low error rate based on the LSTM model data output. Another key model was the GBM model, which, despite its effectiveness, was also found to have a moderate accuracy with higher levels of FP and FN. This work, therefore, establishes LSTM-GBM as the model with the greatest effectiveness, perfect accuracy, precision, recall, and F1 score and the lowest error rates of all the models tested. More rigor was added by receiving operating characteristic analysis and precision-recall curve analysis, which all suggested that the hybrid model was an incredibly well-performing classifier. Thus, the effectiveness of the hybrid model again proves that the integration of various machine learning methods helps get accurate results and the reliability of predictions made by the model. The outcomes indicate that while using the presented dataset, the hybrid LSTM-GBM model performed much better than the individual LSTM and GBM models, affirming the possibility of utilizing the former structures in weather forecast-enunciated tasks. These results underscore the significance of applying various machine learning algorithms to improve weather prediction so that various entities and industries can make sound decisions.
Keywords
Rain prediction, Machine learning, LSTM, GBM, Hybrid model.
References
[1] Aman Arora et al., “Optimization of State-of-the-Art Fuzzy-Metaheuristic ANFIS-Based Machine Learning Models for Flood Susceptibility Prediction Mapping in the Middle Ganga Plain, India,” Science of The Total Environment, vol. 750, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] A.A. Masrur Ahmed et al., “Deep Learning Hybrid Model with Boruta-Random Forest Optimiser Algorithm for Streamflow Forecasting with Climate Mode Indices, Rainfall, and Periodicity,” Journal of Hydrology, vol. 599, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] C. Condemi et al., “Hydro-Power Production Capacity Prediction based on Machine Learning Regression Techniques,” Knowledge-Based Systems, vol. 222, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Diaa Salman et al., “Hybrid Deep Learning Models for Time Series Forecasting of Solar Power,” Neural Computing and Applications, vol. 36, no. 16, pp. 9095-9112, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sarmad Dashti Latif et al., “Assessing Rainfall Prediction Models : Exploring the Advantages of Machine Learning and Remote Sensing Approaches,” Alexandria Engineering Journal, vol. 82, pp. 16-25, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ehab Gomaa et al., “Assessment of Hybrid Machine Learning Algorithms Using TRMM Rainfall Data for Daily Inflow Forecasting in Três Marias Reservoir, Eastern Brazil,” Heliyon, vol. 9, no. 8, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Fatemeh Rezaei Aderyani, S. Jamshid Mousavi, and Fatemeh Jafari, “Short-Term Rainfall Forecasting Using Machine Learning-Based Approaches of PSO-SVR, LSTM and CNN,” Journal of Hydrology, vol. 614, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jian Huang et al., “A Hybrid Machine-Learning Model to Estimate Potential Debris-Flow Volumes,” Geomorphology, vol. 367, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Zeqiang Chen, Hui Lin, and Gaoyun Shen, “TreeLSTM: A Spatiotemporal Machine Learning Model for Rainfall-Runoff Estimation,” Journal of Hydrology: Regional Studies, vol. 48, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Renfei He, Limao Zhang, and Alvin Wei Ze Chew, “Modeling and Predicting Rainfall Time Series Using Seasonal-Trend Decomposition and Machine Learning,” Knowledge-Based Systems, vol. 251, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yazid Tikhamarine et al., “Rainfall-Runoff Modelling Using Improved Machine Learning Methods: Harris Hawks Optimizer vs. Particle Swarm Optimization,” Journal of Hydrology, vol. 589, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Vahid Nourani, and Nima Farboudfam, “Rainfall Time Series Disaggregation in Mountainous Regions Using Hybrid Wavelet-Artificial Intelligence Methods,” Environmental Research, vol. 168, pp. 306-318, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Dina Pirone et al., “Short-Term Rainfall Forecasting Using Cumulative Precipitation Fields from Station Data: A Probabilistic Machine Learning Approach,” Journal of Hydrology, vol. 617, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Xiujie Qiao et al., “Metaheuristic Evolutionary Deep Learning Model Based on Temporal Convolutional Network, Improved Aquila Optimizer and Random Forest for Rainfall-Runoff Simulation and Multi-Step Runoff Prediction,” Expert Systems with Applications, vol. 229, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Ari Yair Barrera-Animas et al., “Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting,” Machine Learning with Applications, vol. 7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Dieu Tien Bui et al., “A Novel Hybrid Approach Based on a Swarm Intelligence Optimized Extreme Learning Machine for Flash Flood Susceptibility Mapping,” Catena, vol. 179, pp. 184-196, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Wanie M. Ridwan et al., “Rainfall Forecasting Model Using Machine Learning Methods: Case Study Terengganu, Malaysia,” Ain Shams Engineering Journal, vol. 12, no. 2, pp. 1651-1663, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Elham Fijani et al., “Design and Implementation of a Hybrid Model Based on Two-Layer Decomposition Method Coupled with Extreme Learning Machines to Support Real-Time Environmental Monitoring of Water Quality Parameters,” Science of The Total Environment, vol. 648, pp. 839-853, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Archana Nigam, and Sanjay Srivastava, “Hybrid Deep Learning Models for Traffic Stream Variables Prediction During Rainfall,” Multimodal Transportation, vol. 2, no. 1, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohammad Hossein Shahani et al., “Assessing Climate Change Impact on River Flow Extreme Events in Different Climates of Iran Using Hybrid Application of LARS-WG6 and Rainfall-Runoff Modeling of Deep Learning,” Ecohydrology & Hydrobiology, vol. 23, no. 2, pp. 224-239, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mohd Usman Saeed Khan et al., “Comparative Analysis of Different Rainfall Prediction Models: A Case Study of Aligarh City, India,” Results in Engineering, vol. 22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Geethu Thottungal Harilal, Aniket Dixit, and Giovanni Quattrone, “Establishing Hybrid Deep Learning Models For Regional Daily Rainfall Time Series Forecasting in the United Kingdom,” Engineering Applications of Artificial Intelligence, vol. 133, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Faisal Baig et al., “How Accurate are the Machine Learning Models in Improving Monthly Rainfall Prediction in Hyper Arid Environment?,” Journal of Hydrology, vol. 633, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Binh Thai Pham et al., “Hybrid Artificial Intelligence Models Based on Adaptive Neuro Fuzzy Inference System and Metaheuristic Optimization Algorithms for Prediction of Daily Rainfall,” Physics and Chemistry of the Earth, Parts A/B/C, vol. 134, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[25] T. Anuradha, P.S.G. Aruna Sri Formal, and J. RamaDevi, “Hybrid Model for Rainfall Prediction with Statistical and Technical Indicator Feature Set,” Expert Systems with Applications, vol. 249, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Amirmasoud Amini, Mehri Dolatshahi, and Reza Kerachian, “Real-Time Rainfall and Runoff Prediction by Integrating BC-MODWT and Automatically-Tuned DNNs: Comparing Different Deep Learning Models,” Journal of Hydrology, vol. 631, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Ali Azizpour et al., “Estimation of Water Level Fluctuations in Groundwater through a Hybrid Learning Machine,” Groundwater for Sustainable Development, vol. 15, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Rana Muhammad Adnan et al., “Improving Streamflow Prediction Using a New Hybrid ELM Model Combined with Hybrid Particle Swarm Optimization and Grey Wolf Optimization,” Knowledge-Based Systems, vol. 230, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Hai Tao et al., “River Water Level Prediction in Coastal Catchment Using Hybridized Relevance Vector Machine Model with Improved Grasshopper Optimization,” Journal of Hydrology, vol. 598, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Diaa Salman et al., “Integration of Electric Vehicle Charging Stations Into the Unit Commitment Modeling,” 2022 11th International Conference on Power Science and Engineering, Eskisehir, Turkey, pp. 79-83, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Joyce Cai et al., “Data for Rain Prediction,” Mendeley Data, 2022.
[CrossRef] [Publisher Link]