Abstract
India has witnessed a notable upsurge in floods owing to shifts in global climatic patterns underlining climate change and global warming. This inorganic change is significantly evident in places like Kerala and Tamil Nadu. Kerala receives perennial rainfall throughout the year, influenced by southeast and northwest monsoon rainfall cycles, resulting in flood-induced calamities like landslides prevailing in the Thrissur and Munnar regions of Kerala. Long-Term Short Memory (LSTM) networks outperform pre-existing deep learning forecasting models as it is optimal for time series forecasting by handling non-linear spatiotemporal dynamics, and adapting to long-term dependencies in time series data while ensuring high scalability. The objective of the present work is rainfall prediction in flood-prone regions in Kerala. We propose an LSTM network to predict the monthly rainfall in Thrissur, Pathanamthitta, Munnar and Kottayam. The proposed model is evaluated using the metrics like mean absolute error and root-mean-squared error.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hong, W.-C.: Rainfall forecasting by technological machine learning models. Appl. Math. Comput. 200(1), 41–57 (2008). ISSN 0096-3003
Monira Sumi, S., Faisal Zaman, M., Hirose, H.: A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. Int. J. Appl. Math. Comput. Sci. 22(4), 841–854 (2012)
Hernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J., Duque, N.: Rainfall prediction: a deep learning approach. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds.) HAIS 2016. LNCS, vol. 9648, pp. 151–162. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32034-2_13
Cramer, S., et al.: An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst. Appl. 85, 169–181 (2017). ISSN 0957-4174
Rasel, R.I., Sultana, N., Meesad, P.: An application of data mining and machine learning for weather forecasting. In: Meesad, P., Sodsee, S., Unger, H. (eds.) IC2IT 2017. AISC, vol. 566, pp. 169–178. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60663-7_16
Barrera-Animas, A.Y., et al.: Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting. Mach. Learn. Appl. 7, 100204 (2022). ISSN 2666-8270
Balamurugan, M.S., Manojkumar, R.: Study of short term rain forecasting using machine learning based approach. Wirel. Netw. 27(8), 5429–5434 (2021). ISSN 1572-8196
Pai, D.S., et al.: Development of a new high spatial resolution (\(0.25^\circ \times 0.25^\circ \)) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM 65(1), 1–18 (2014)
Flood - KSDMA. https://sdma.kerala.gov.in/flood/. Accessed 07 Mar 2023
Abadi, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015). Software available from http://tensorflow.org/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Akshaya, J. et al. (2024). Going Beyond Traditional Methods: Using LSTM Networks to Predict Rainfall in Kerala. In: García Márquez, F.P., Jamil, A., Ramirez, I.S., Eken, S., Hameed, A.A. (eds) Computing, Internet of Things and Data Analytics. ICCIDA 2023. Studies in Computational Intelligence, vol 1145. Springer, Cham. https://doi.org/10.1007/978-3-031-53717-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-53717-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53716-5
Online ISBN: 978-3-031-53717-2
eBook Packages: EngineeringEngineering (R0)