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
Keywords: 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.


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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., Feb. 2023.