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Several countries’ whole economies rest on rain. When rain is predicted early, the results on several fields can be seen. Predictions of rain that are accurate and made on time can help the building industry, transportation, agriculture, flying, and people who are worried about flooding. Every year, heavy rains cause a lot of destruction to both structures and people's lives. Plenty of investigations are being done to figure out how to predict rain based on where it is and how the weather is. Every year, rain-related disasters cause a lot of damage and loss of life, both to structures and to people. This study suggests using the Indian dataset and the Composite Learning Algorithm (CLA) to predict rainfall using global characteristics. Using data from the UCI repository, the method is used to predict how much rain there will be. The meteorological factors in the dataset are used to forecast the rain more accurately. Precision, memory, and accuracy, as well as the F1-score, are used to measure performance.

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An Assessment on the Performance of a Composite Learning System for Prediction Using Precision, Recall, Accuracy, and F1-Score for Rainfall

  • 会议论文

  • 发表日期：

    2026年

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  • G. Ravi Kumar,
  • V. Venkataiah,
  • Borra Sivaiah,
  • B. Kavitha Rani,
  • Kanthi Murali,
  • G. Swathi

摘要

Several countries’ whole economies rest on rain. When rain is predicted early, the results on several fields can be seen. Predictions of rain that are accurate and made on time can help the building industry, transportation, agriculture, flying, and people who are worried about flooding. Every year, heavy rains cause a lot of destruction to both structures and people's lives. Plenty of investigations are being done to figure out how to predict rain based on where it is and how the weather is. Every year, rain-related disasters cause a lot of damage and loss of life, both to structures and to people. This study suggests using the Indian dataset and the Composite Learning Algorithm (CLA) to predict rainfall using global characteristics. Using data from the UCI repository, the method is used to predict how much rain there will be. The meteorological factors in the dataset are used to forecast the rain more accurately. Precision, memory, and accuracy, as well as the F1-score, are used to measure performance.