Published Paper Details:

The hardest thing a meteorologist has to do is forecast rainfall. In this work, we presented a rainfall prediction model that can be readily calibrated through the use of LSTM and artificial intelligence algorithms. This is a sophisticated way to determine the amount of rainfall. When it comes to the correctness of this kind of technique implementation, the deep learning approach is the most beneficial. When measuring memory sequence data, a long short-term memory method is used to quickly calculate historical data and produce the best forecast. This forecast technique is vital since the majority of the population in this nation depends on agriculture. Assessing rainfall in a timely manner will boost crop yields and save agricultural expenses. We have developed our model, which will assist us in estimating the quantity of rainfall, taking all these aspects into account.To achieve this, we have gathered data from six different regions. Six factors--temperature, dew point, humidity, wind pressure, wind speed, and wind direction have been used in our prediction. Our approach yielded an accuracy rate of 76% when all of our data was analysed. For the best outcome, we also concentrate on a large dataset on long-term weather.