| dc.description.abstract | Rainfall plays a vital role in agriculture, water management, and disaster preparedness,
yet accurate prediction remains a challenge due to the complex and non-linear nature
of weather patterns. Traditional models like ARIMA and MLR often fail to address
these complexities, while machine learning models, such as Random Forest and LSTM
networks, offer higher accuracy but require extensive datasets and computational
resources. This review identifies key models and parameters for rainfall forecasting and
explores strategies to enhance prediction precision. Through a systematic review of
studies from IEEE, ScienceDirect, Springer, and MDPI, models like stacking ensemble
learning, LSTM, and ARIMA were analysed, alongside critical parameters such as
temperature, humidity, and wind patterns. Techniques like particle swarm optimization
and fuzzy rules were also reviewed for their ability to improve performance. Findings
reveal that LSTM networks achieve the highest accuracy, up to 94%, effectively capturing
long-term dependencies in weather data, while hybrid models combining traditional
and machine learning methods address individual model limitations. This study
emphasizes the need for scalable frameworks that integrate real-time data and diverse
parameters to reduce forecast errors, offering reliable solutions for practical applications
in weather-dependent sectors. | en_US |
