Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Advanced Rainfall Classification and Pattern Analysis using Neural Networks

Authors
Kasarapu Ramani1, *, Madupuri Rajesh2, T. Yasaswini2, Vennapusa Anju Shaharun2, Veeravalli Deep Chandu2, Yuvaraj Duraiswamy3
1Professor, Department of Data Science, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India
2UG Scholar, Department of Computer Science and Systems Engineering, Sree Vidyanikethan Engineering College, Tirupati, India
3Professor, Department of Computer Science, Chan University, Duhok, Iraq
*Corresponding author. Email: head-ds@mbu.asia
Corresponding Author
Kasarapu Ramani
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_34How to use a DOI?
Keywords
Rainfall Classification; Multilayer Perceptron; Machine Learning; Random Forest; Convolutional Neural Network; Deep Learning
Abstract

Rainfall distribution serves a variety of purposes in meteorology, hydrology, and environmental science, flood forecasting, agriculture, meteorological analysis, and more around. The gathered dataset is collected from meteorological observations which helps to depict the patterns of rainfall for the area and period of study. We applied a variety of Machine Learning and Deep Learning algorithms such as Random Forest, Convolutional Neural Networks (CNN), and Multilayer Perceptron (MLP), to predict and classify rainfall using Austin weather dataset and obtain valuable conclusions from the dataset. The algorithms selected were found suitable as they were able to represent the complex trends and relationships between the temporal and spatial dimensions. From our results, the best performing algorithms in rainfall classification were Random Forest based on RMSE (Root Mean Square Error), and CNN based on classification accuracy and these two algorithms outperformed the other existing algorithms.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_34How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kasarapu Ramani
AU  - Madupuri Rajesh
AU  - T. Yasaswini
AU  - Vennapusa Anju Shaharun
AU  - Veeravalli Deep Chandu
AU  - Yuvaraj Duraiswamy
PY  - 2024
DA  - 2024/07/30
TI  - Advanced Rainfall Classification and Pattern Analysis using Neural Networks
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 343
EP  - 353
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_34
DO  - 10.2991/978-94-6463-471-6_34
ID  - Ramani2024
ER  -