International Journal For Multidisciplinary Research

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An Analysis of Modern Machine Learning Algorithms for Rainfall Prediction

Author(s) Love Vishwakarma, Shivshankar Gupta
Country India
Abstract Agriculture has a significant importance in the Indian economy. Rainfall is crucial for agriculture, however these days, predicting rainfall has become a very difficult subject. A good rainfall forecast enables people to plan ahead, take safety measures, and have better crop-related strategies. Both nature and humanity are being severely impacted by global warming, which also hastens the shift in climatic conditions. Due to the warming of the air and rising ocean level, floods are occurring and farmed fields are becoming drier. Unseasonable and excessive amounts of rainfall are a result of unfavourable climate change. One of the finest methods for learning about the rain and climate is to anticipate rain. The primary goal of this study is to accurately describe the climate to the clients from a variety of aspects, including agriculture, research, power generation, etc. to understand the requirement for changing the environment and the variables such as temperature, humidity, precipitation, and wind speed that ultimately lead to rainfall forecast. Predicting rain is difficult since it relies on geographic places as well. Machine learning, an expanding branch of
artificial intelligence, aids in rainfall forecasting. For the purpose of forecasting the rainfall in this research study, we will use a dataset from the UCI repository that has many properties. The major goal of this work is to analyse a system for predicting rainfall and to do it more accurately by using machine learning
classification algorithms.
Keywords Rainfall Prediction system, Machine Learning, Dataset, Classification algorithms.
Field Computer
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-05
Cite This An Analysis of Modern Machine Learning Algorithms for Rainfall Prediction - Love Vishwakarma, Shivshankar Gupta - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.16306
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.16306
Short DOI https://doi.org/gtp8k9
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