Prediction of Rainfall Using Machine Learning Technique

EasyChair Preprint 12662

Abstract

Rainfall prediction is important as heavy rainfall can lead to many disasters. The prediction helps people to take preventive measures and moreover the prediction should be accurate. There are two types of prediction short term rainfall prediction and long term rainfall. Prediction mostly short term prediction can gives us the accurate result. The main challenge is to build a model for long term rainfall prediction. Heavy precipitation prediction could be a major drawback for earth science department because it is closely associated with the economy and lifetime of human. It’s a cause for natural disasters like flood and drought that square measure encountered by individuals across the world each year. Accuracy of rainfall statement has nice importance for countries like India whose economy is basically dependent on agriculture. The dynamic nature of atmosphere, applied mathematics techniques fail to provide sensible accuracy for precipitation statement. The prediction of precipitation using machine learning techniques may use regression. Intention of this project is to offer nonexperts easy access to the techniques, approaches utilized in the sector of precipitation prediction and provide a comparative study among the various machine learning techniques.

Keyphrases: Prediction System, XGBoost, heavy rainfall, historical data, supervised learning

@booklet{EasyChair:12662,
  author    = {Akula Natesh and Godugu Naveen Kumar and Kusha Sriniketh and Chakrala Krishnasagar and Aurgyadip Kundhu},
  title     = {Prediction of Rainfall Using Machine Learning Technique},
  howpublished = {EasyChair Preprint 12662},
  year      = {EasyChair, 2024}}