• GANGESH KUMAR DUBEY
• PRANAV MISHRA
• NIRBHAYNARAYAN PANDEY

This research integrates an innovative strategy for gathering high-quality and diverse weather-related data from satellites, weather stations, buoys, and radars. The feature selection process encompasses historical weather data, geographical information, and external factors such as ocean currents and solar radiation. Advanced data preprocessing techniques are employed to normalize and scale the data, ensuring uniformity across features for optimal model performance. The model selection phase involves choosing appropriate machine learning algorithms, including regression, time series analysis,and neural networks, tailored specifically for weather prediction tasks. Emphasizing the significance of temporal dependencies in weather data, the study utilizes time series forecasting techniques like ARIMA and LSTM to improve prediction accuracy and reliability. By combining these methodologies, the proposed approach enhances the understanding and prediction of complex weather patterns, facilitating better decision-making in various sectors reliant on accurate weather forecasts.

"WEATHER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.c84-c89, April-2024, Available :http://www.jetir.org/papers/JETIR2404214.pdf

"WEATHER PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppc84-c89, April-2024, Available at : http://www.jetir.org/papers/JETIR2404214.pdf