Rainfall Prediction using Deep Learning Algorithms
1O.Shashank Reddy, 1P.Kushitha Reddy, 1P.Bharat Surya, 1P.Charan Kumar Reddy, 1P.Gyaneshwar, 2K.Gopala Krishna and 3Dr.Thayyaba Khatoon
1UG Students , 2Assistant Professor , 3Professor & HoD
Department of Artificial Intelligence and Machine Learning, School of Engineering,
Malla Reddy University, Maisammaguda, Dulapally,
Hyderabad, Telangana 500100.
2011CS020271@mallareddyuniversity.ac.in,2011CS020272@mallareddyuniveristy.ac.in, 2011CS020273@mallareddyuniversity.ac.in, 2011CS020274@mallareddyuniversity.ac.in,
2011Cs020275@mallareddyuniversity.ac.in, gopalkrishnak@mallareddyuniversity.ac.in,
thayyabakhatoon@mallareddyuniversity.ac.in .
Abstract
Accurate rainfall prediction is crucial for effective water resource management, agriculture, and disaster preparedness. Traditional methods for rainfall prediction often struggle to capture complex patterns and dependencies within meteorological data. In recent years, deep learning algorithms, particularly Artificial Neural Networks (ANNs), have demonstrated great potential for improving the accuracy of rainfall prediction.This research paper presents an in-depth analysis of rainfall prediction using deep learning approaches based on ANNs. Various architectures, including feedforward, recurrent, and hybrid models, are explored and evaluated using diverse meteorological datasets from different regions and time periods. The findings of this research contribute to the field of rainfall prediction by demonstrating the potential of deep learning approaches using ANNs. The utilization of these models allows for improved accuracy in rainfall prediction, enabling better decision-making in critical sectors like agriculture, water resource management, and disaster mitigation.
Keywords
Rainfall prediction, deep learning, artificial neural networks, feedforward neural networks, recurrent neural networks, LSTM, GRU, meteorological data, feature engineering, evaluation metrics, water resource management, agriculture, disaster preparedness.
