Knowing this distribution provides a good chance to regulate crop planting date to be shorter than usual state to consume less water in order to be more adaptive in climate changes circumstances.
Rainfall Prediction is the application area of data science and machine learning to predict the state of the atmosphere. It is important to predict the rainfall intensity for effective use of water resources and crop production to reduce mortality due to flood and any disease caused by rain. Precipitable water is measured using information from weather balloons and water vapour imagery from weather satellites. The equation of the line is given as Y=wx+b. It provides an estimate of rainfall using various atmospheric variables like cloud cover, humidity, wind, and average temperature to predict rainfall.it is accepted that the main variables for predicting precipitation are temperature and humidity . Nevertheless, there are other four meteorological parameters—air, dewpoint temperature (or relative humidity), wind speed, and cloud cover—which are strongly correlated with rainfall.To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets.To choose the better machine learning algorithms to study the daily rainfall amount prediction, various papers can be reviewed concerning rainfall prediction. To predict the daily rainfall intensity using the real-time environmental data, three algorithms such as MLP, RF, and XGBoost gradient descent can chosen for the experiment. Hence, the three machine learning algorithms experiment with and compared to report the better algorithms to predict the daily rainfall amount.
Conference Paper Prediction of rainfall using image processing
Predicting precipitation distribution involves a combination of both theoretical and observational methods. Here are some common approaches used in practice:
It's important to note that predicting precipitation distribution is complex and not always accurate. The use of multiple methods and data sources can help improve accuracy, but it's important to also consider the limitations of each method and the uncertainty associated with any prediction.
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