• K. SUPRIYA
• D. LALITHA BHASKARI

Rainfall prediction is a critical task in meteorology and environmental science, as accurate forecasts can aid in disaster preparedness and resource management. In this study, historical weather data including temperature, dew point average, humidity, and sea level pressure were collected and preprocessed for training and testing the LSTM model. This research paper presents a novel approach for rainfall prediction using Long Short-Term Memory (LSTM) neural networks, for the task of rainfall prediction. LSTMs are a type of recurrent neural network (RNN) that excel at capturing temporal dependencies in sequential data, making them well-suited for time series forecasting tasks. The results obtained with model accuracy of 95% are presented.

Keywords: Rainfall prediction, LSTM neural network, meteorological variables, weather forecasting, machine learning.

"AN APPROACH FOR RAINFALL PREDICTION IN FARMING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.f138-f144, September-2023, Available :http://www.jetir.org/papers/JETIR2309516.pdf

"AN APPROACH FOR RAINFALL PREDICTION IN FARMING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppf138-f144, September-2023, Available at : http://www.jetir.org/papers/JETIR2309516.pdf