Enhanced Rainfall Prediction with Weighted Linear Units using Advanced Recurrent Neural Network

Authors

  • Yashwant Dongare Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology Pune, Maharashtra, India
  • Ashvini Shende Adjunct Faculty, Symbiosis school of economics, S. B. Road, Pune, Maharashtra, India
  • Amol Dhumane Associate Professor, Department of Computer Science and Engineering, Symbiosis Institute of Technology, Lavale, Pune, Maharashtra, India
  • Mubin Tamboli Associate Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India
  • Satpalsing Devising Rajput Assistant Professor, Department of Computer Engineering, Pimpri Chinchwad College of Engineering Nigdi, Pune, Maharashtra, India
  • Vinod S. Wadne Department of Computer Engineering, JSPM's ICOER wagholi, Pune, Maharashtra, India

Keywords:

Rainfall prediction, Long Short-Term Memory, deep learning, Recurrent Neural Network

Abstract

A precise rainfall forecast is essential for successful decision-making and catastrophe prevention in the field of meteorology. This article suggests a more effective technique for predicting rainfall that uses a sophisticated recurrent neural network (RNN) and weighted linear units (WLUs). The proposed model seeks to increase the precision and efficacy of rainfall forecasts as compared to existing approaches. The main architecture of the prediction model is an RNN based on Intensified Long Short-Term Memory (Intensified LSTM). The network is trained and assessed using a sizable dataset of rainfall observational data. Indicators of projected rainfall, such as intensity and duration, are generated by the trained model. The performance of the suggested model is assessed using a number of evaluation criteria, such as Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate. Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Holt-Winters, and other innovative RNN and LSTM models are compared to more established approaches. The objective is to show the superior prediction skills of the proposed model. The addition of WLUs enhances the network's capacity to identify intricate linkages and patterns in rainfall data, leading to more precise predictions. The results highlight how the proposed approach may help meteorologists make better decisions and take precautions against disasters.

Downloads

Download data is not yet available.

References

Jimoh, R.G.; Olagunju, M.; Folorunso, I.O.; Asiribo, M.A. Modeling rainfall prediction using fuzzy logic. Int. J. Innov. Res. Comput. Commun. Eng. 2013, 1, 929–936.

Wu, J.; Liu, H.; Wei, G.; Song, T.; Zhang, C.; Zhou, H. Flash flood forecasting using support vector regression model in a small mountainous catchment. Water 2019, 11, 1327.

Poornima, S.; Pushpalatha, M.; Sujit Shankar, J. Analysis of weather data using forecasting algorithms. In Computational Intelligence: Theories, Applications and Future Directions—Volume I. Advances in Intelligent Systems and Computing; Verma, N., Ghosh, A., Eds.; Springer: Singapore, 2019; Volume 798.

Manideep, K.; Sekar, K.R. Rainfall prediction using different methods of Holt winters algorithm: A big data approach. Int. J. Pure Appl. Math. 2018, 119, 379–386.

Gundalia, M.J.; Dholakia, M.B. Prediction of maximum/minimum temperatures using Holt winters method with excel spread sheet for Junagadh region. Int. J. Eng. Res. Technol. 2012, 1, 1–8.

Puah, Y.J.; Huang, Y.F.; Chua, K.C.; Lee, T.S. River catchment rainfall series analysis using additive Holt–Winters method. J. Earth Syst. Sci. 2016, 125, 269–283.

Patel, D.P.; Patel, M.M.; Patel, D.R. Implementation of ARIMA model to predict Rain Attenuation for KU-band 12 Ghz Frequency. IOSR J. Electron. Commun. Eng. (IOSR-JECE) 2014, 9, 83–87.

Graham, A.; Mishra, E.P. Time series analysis model to forecast rainfall for Allahabad region. J. Pharmacogn. Phytochem. 2017, 6, 1418–1421. 15. Salas, J.D.; Obeysekera, J.T.B. ARMA model identification of hydrologic time series. Water Resour. Res. 1982, 18, 1011–1021.

Chen, W.; Xie, X.; Wang, J.; Pradhan, B.; Hong, H.; Bui, D.T.; Duan, Z.; Ma, J. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 2017, 151, 147–160.

Kusiak, A.; Wei, X.; Verma, A.P.; Roz, E. Modeling and prediction of rainfall using radar reflectivity data: A data-mining approach. IEEE Trans. Geosci. Remote Sens. 2013, 51, 2337–2342.

Kannan, M.; Prabhakaran, S.; Ramachandran, P. Rainfall forecasting using data mining technique. Int. J. Eng. Technol. 2010, 2, 397–401.

Mehrotra, R.; Sharma, A. A nonparametric nonhomogeneous hidden Markov model for downscaling of multisite daily rainfall occurrences. J. Geophys. Res. Atmos. 2005, 110.

Nayak, D.R.; Mahapatra, A.; Mishra, P. A survey on rainfall prediction using artificial neural network. Int. J. Comput. Appl. 2013, 72, 16.

Kashiwao, T.; Nakayama, K.; Ando, S.; Ikeda, K.; Lee, M.; Bahadori, A. A neural network-based local rainfall prediction system using meteorological data on the internet: A case study using data from the Japan meteorological agency. Appl. Soft Comput. 2017, 56, 317–330.

Mislan, H.; Hardwinarto, S.; Sumaryono, M.A. Rainfall monthly prediction based on artificial neural network: A case study in Tenggarong Station, East Kalimantan, Indonesia. Procedia Comput. Sci. 2015, 59, 142–151.

Niu, J.; Zhang, W. Comparative analysis of statistical models in rainfall prediction. In Proceedings of the 2015 IEEE International Conference on Information and Automation, Lijiang, China, 8–10 August 2015; pp. 2187–2190.

Dash, Y.; Mishra, S.K.; Panigrahi, B.K. Rainfall prediction of a maritime state (Kerala), India using SLFN and ELM techniques. In Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, India, 6–7 July 2017; pp. 1714–1718.

Poornima, S.; Pushpalatha, M.; Aglawe, U. Predictive analytics using extreme learning machine. J. Adv. Res. Dyn. Control Syst. 2018, 10, 1959–1966.

Choi, E.; Schuetz, A.; Stewart, W.F.; Sun, J. Using recurrent neural network models for early detection of heart failure onset. J. Am. Med Inform. Assoc. 2016, 24, 361–370.

Tran, N.; Nguyen, T.; Nguyen, B.M.; Nguyen, G. A multivariate fuzzy time series resource forecast model for clouds using LSTM and data correlation analysis. Procedia Comput. Sci. 2018, 126, 636–645.

Zhuang, N.; Kieu, T.D.; Qi, G.J.; Hua, K.A. Deep differential recurrent neural networks. arXiv 2018, arXiv:1804.04192. 26. Schuster, M.; Paliwal, K.K. Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 1997, 45, 2673–2681.

Graves, A.; Mohamed, A.R.; Hinton, G. Speech recognition with deep recurrent neural networks. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 26–31 May 2013; pp. 6645–6649.

Kalchbrenner, N.; Danihelka, I.; Graves, A. Grid long short-term memory. arXiv 2015, arXiv:1507.01526. 29.

Salman, A.G.; Heryadi, Y.; Abdurahman, E.; Suparta, W. Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting. Procedia Comput. Sci. 2018, 135, 89–98. 30.

Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.

Elfwing, S.; Uchibe, E.; Doya, K. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 2018, 107, 3–11.

Davuluri, R. ., Mallapragada, V. ., Mamillapalli, U. M. R. ., M, M. ., & Peeka, S. . (2023). Alzheimer’s Disease Diagnosis Using CNN Based Pre-trained Models. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 315–323. https://doi.org/10.17762/ijritcc.v11i4.6456

Martin, S., Wood, T., Hernandez, M., González, F., & Rodríguez, D. Machine Learning for Personalized Advertising and Recommendation. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/156

Dhabliya, D., Dhabliya, R. Key characteristics and components of cloud computing (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 12-18.

Downloads

Published

03.09.2023

How to Cite

Dongare, Y. ., Shende, A. ., Dhumane, A. ., Tamboli, M. ., Rajput, S. D. ., & S. Wadne, V. . (2023). Enhanced Rainfall Prediction with Weighted Linear Units using Advanced Recurrent Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 549–556. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3490

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)