Analogousness Enhanced Rainfall Predictor using XGBoost Backbone
Keywords:Rainfall prediction, Machine Learning, Classification, Extreme Gradient Boosting, Data Imbalance
The forecasting of intense rainfall presents a significant challenge for the meteorological department because of the strong connection between rain and the economy as well as human lives, but extreme climate shifts have made it more complicated than ever before to estimate precipitation accurately. In a country that relies heavily on agriculture, the precision of rainfall forecasts is vital. Predicting rainfall is a common application for machine learning systems. By figuring out the hidden patterns in weather data from the past, these methods can almost accurately predict when it will rain. This study proposes a novel machine learning method called Analogousness Enhanced Rainfall Predictor using XGBoost Backbone to foretell rainfall. The proposed method uses the basis of XGBoost and tunes parameters for it to get higher accuracy for the outcomes. This study uses a large dataset of weather observations collected over ten years in various places in Australia. This model successfully deals with the issue of the data-class imbalance issue.
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