Short-Term Rainfall Prediction Using Supervised Machine Learning


  • Nusrat Jahan Prottasha Department of Computer Science, Daffodil International University, Dhaka, Bangladesh
  • Anik Tahabilder Department of Computer Science, Wayne State University, Detroit, Michigan, USA
  • Md Kowsher Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA
  • Md Shanon Mia Department of Computer Science, Daffodil International University, Dhaka, Bangladesh
  • Khadiza Tul Kobra Department of Computer Science, Daffodil International University, Dhaka, Bangladesh



rain prediction, machine learning, supervised classification, agriculture resource, crops yield


Floods and rain significantly impact the economy of many agricultural countries in the world. Early prediction of rain and floods can dramatically help prevent natural disaster damage. This paper presents a machine learning and data-driven method that can accurately predict short-term rainfall. Various machine learning classification algorithms have been implemented on an Australian weather dataset to train and develop an accurate and reliable model. To choose the best suitable prediction model, diverse machine learning algorithms have been applied for classification as well. Eventually, the performance of the models has been compared based on standard performance measurement metrics. The finding shows that the hist gradient boosting classifier has given the highest accuracy of 91%, with a good F1 value and receiver operating characteristic, the area under the curve score.


K. T. Sohn, J. H. Lee, and S. H. Lee, “Statistical Prediction of Heavy Rain in South Korea,” Advances in Atmospheric Sciences, vol. 22, no. 5, pp. 703-710, 2005.

T. Denœux and P. Rizand, “Analysis of Radar Images for Rainfall Forecasting Using Neural Networks,” Neural Computing and Applications, vol. 3, no. 1, pp. 50-61, March 1995.

B. K. Shah, S. Thapa, R. S. Diyali, S. Hk, and S. Maharjan, “Rain Prediction Using Polynomial Regression for the Field of Agriculture Prediction for Karnatakka,” International Journal of Advances in Engineering and Management, vol. 2, no. 3, pp. 62-66, March 2020.

P. Asha, A. Jesudoss, S. Prince Mary, K. V. Sai Sandeep, and K. Harsha Vardha, “An Efficient Hybrid Machine Learning Classifier for Rainfall Prediction,” Journal of Physics: Conference Series, vol. 1770, no. 1, article no. 012012, March 2021.

S. Sakthivel and G. Thailambal, “Effective Procedure to Predict Rainfall Conditions Using Hybrid Machine Learning Strategies,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 6, pp. 209-216, April 2021.

D. Naidu, B. Majhi, and S. K. Chandniha, “Development of Rainfall Prediction Models Using Machine Learning Approaches for Different Agro-Climatic Zones,” Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science, IGI Global, 2021.

T. V. Dinh, H. Nguyen, X. L. Tran, and N. D. Hoang, “Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification,” Mathematical Problems in Engineering, vol. 2021, article no. 6647829, 2021.

H. Abdel-Kader, M. Abd-El Salam, and M. Mohamed, “Hybrid Machine Learning Model for Rainfall Forecasting,” Journal of Intelligent Systems and Internet of Things, vol. 1, no. 1, pp. 5-12, 2021.

N. Samsiahsani, I. Shlash, M. Hassan, A. Hadi, and M. Aliff, “Enhancing Malaysia Rainfall Prediction Using Classification Techniques,” Journal of Applied Environmental and Biological Sciences, vol. 7, no. 2S, pp. 20-29, April 2017.

K. C. Luk, J. E. Ball, and A. Sharma, “An Application of Artificial Neural Networks for Rainfall Forecasting,” Mathematical and Computer Modeling, vol. 33, no. 6-7, pp. 683-693, March 2001.

J. Abbot and J. Marohasy, “Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia,” Advances in Atmospheric Sciences, vol. 29, no. 4, pp. 717-730, June 2012.

C. Shah, C. Hendahewa, and R. Gonzalez-Ibanez, “Rain or Shine? Forecasting Search Process Performance in Exploratory Search Tasks,” Journal of the Association for Information Science and Technology, vol. 67, no. 7, pp. 1607-1623, July 2016.

M. Sangiorgio, S. Barindelli, R. Biondi, E. Solazzo, E. Realini, G. Venuti, et al., “Improved Extreme Rainfall Events Forecasting Using Neural Networks and Water Vapor Measures,” 6th International Conference on Time Series and Forecasting, pp. 820-826, September 2019.

D. Han, T. Kwong, and S. Li, “Uncertainties in Real-Time Flood Forecasting with Neural Networks,” Hydrological Processes: An International Journal, vol. 21, no. 2, pp. 223-228, January 2007.

J. Young, “Rain in Australia,”, October 30, 2007.

M. Kowsher, A. Tahabilder, and S. A. Murad, “Impact-Learning: A Robust Machine Learning Algorithm,” Proceedings of the 8th International Conference on Computer and Communications Management, pp. 9-13, July 2020.

C. V. Z. Zelaya, “Towards Explaining the Effects of Data Preprocessing on Machine Learning,” IEEE 35th International Conference on Data Engineering, pp. 2086-2090, April 2019.




How to Cite

Nusrat Jahan Prottasha, Anik Tahabilder, Md Kowsher, Md Shanon Mia, and Khadiza Tul Kobra, “Short-Term Rainfall Prediction Using Supervised Machine Learning”, Adv. technol. innov., vol. 8, no. 2, pp. 111–120, Apr. 2023.