An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India

Chawngthu Zoremsanga; Jamal Hussain

doi:10.17485/IJST/v17i18.2505

Article

An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India

VIEWS 156
PDF 49

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i18.2505

Year: 2024, Volume: 17, Issue: 18, Pages: 1828-1837

Original Article

An Evaluation of Bidirectional Long Short-Term Memory Model for Estimating Monthly Rainfall in India

Chawngthu Zoremsanga^1*, Jamal Hussain²

¹PhD Scholar, Department of Mathematics and Computer Science, Mizoram University,
Aizawl, Mizoram, India
²Professor, Department of Mathematics and Computer Science, Mizoram University, Aizawl, Mizoram, India

*Corresponding Author
Email: [email protected]

Received Date:03 October 2023, Accepted Date:09 April 2024, Published Date:25 April 2024

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

Abstract

Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30) meteorological sub-divisions in India. The model was compiled using the Mean Square Error (MSE) loss function and Adam optimizer. The models' performances were evaluated using statistical metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings: This study discovered that the proposed Bidirectional LSTM model achieved an RMSE of 240.79 and outperformed an existing Recurrent Neural Network (RNN), Vanilla LSTM and Stacked LSTM by 8%, 4% and 2% respectively. The study also finds that increasing the input time step and increasing the number of cells in the hidden layer enhanced the prediction performance of the proposed model, and the Bidirectional LSTM converges at a lower epoch compared to RNN and LSTM models. Novelty: This study applied the Bidirectional LSTM for the first time in predicting all-India monthly average rainfall and provides a new benchmark for this dataset.

Keywords: Deep Learning, LSTM, Rainfall prediction, Stacked LSTM, Bidirectional LSTM

References

Sahoo M, Yadav RK. Teleconnection of Atlantic Nino with summer monsoon rainfall over northeast India. Global and Planetary Change. 2021;203. Available from: https://dx.doi.org/10.1016/j.gloplacha.2021.103550
Dube A, Karunasagar S, Ashrit R, Mitra AK. Spatial verification of ensemble rainfall forecasts over India. Atmospheric Research. 2022;273. Available from: https://dx.doi.org/10.1016/j.atmosres.2022.106169
Kim HJ, Kim MK. A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid. Applied Energy. 2023;332. Available from: https://doi.org/10.1016/j.apenergy.2022.120525
Abbot J, Marohasy J. Application of artificial neural networks to rainfall forecasting in Queensland, Australia. Advances in Atmospheric Sciences. 2012;29(4):717–730. Available from: https://dx.doi.org/10.1007/s00376-012-1259-9
Abbot J, Marohasy J. Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research. 2014;138:166–178. Available from: https://dx.doi.org/10.1016/j.atmosres.2013.11.002
Abbot J, Marohasy J. Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization. Atmospheric Research. 2017;197:289–299. Available from: https://dx.doi.org/10.1016/j.atmosres.2017.07.015
Chattopadhyay S, Chattopadhyay G. Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN. Comptes Rendus. Géoscience. 2010;342(2):100–107. Available from: https://dx.doi.org/10.1016/j.crte.2009.10.016
Dash Y, Mishra SK, Panigrahi BK. Rainfall prediction for the Kerala state of India using artificial intelligence approaches. Computers & Electrical Engineering. 2018;70:66–73. Available from: https://dx.doi.org/10.1016/j.compeleceng.2018.06.004
Mekanik F, Imteaz MA, Gato-Trinidad S, Elmahdi A. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes. Journal of Hydrology. 2013;503:11–21. Available from: https://dx.doi.org/10.1016/j.jhydrol.2013.08.035
Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation. 1997;9(8):1735–1780. Available from: https://dx.doi.org/10.1162/neco.1997.9.8.1735
Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks. 2005;18(5-6):602–610. Available from: https://dx.doi.org/10.1016/j.neunet.2005.06.042
George B, Kutty G. Multivariate ensemble sensitivity analysis applied for an extreme rainfall over Indian subcontinent. Atmospheric Research. 2022;277. Available from: https://dx.doi.org/10.1016/j.atmosres.2022.106324
Saha M, Santara A, Mitra P, Chakraborty A, Nanjundiah RS. Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model. International Journal of Forecasting. 2021;37(1):58–71. Available from: https://dx.doi.org/10.1016/j.ijforecast.2020.03.001
Poornima S, Pushpalatha M. Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units. Atmosphere. 2019;10(11):1–18. Available from: https://dx.doi.org/10.3390/atmos10110668
Viswanath S, Saha M, Mitra P, Nanjundiah RS. Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India. In: International Conference on Computational Science, ICCS 2019, Lecture Notes in Computer Science. (Vol. 11537, pp. 204-218) Springer, Cham. 2019.
Manoj SO, Ananth JP. MapReduce and Optimized Deep Network for Rainfall Prediction in Agriculture. The Computer Journal. 2020;63(6):900–912. Available from: https://dx.doi.org/10.1093/comjnl/bxz164
Kumar D, Singh A, Samui P, Jha RK. Forecasting monthly precipitation using sequential modelling. Hydrological Sciences Journal. 2019;64(6):690–700. Available from: https://dx.doi.org/10.1080/02626667.2019.1595624
Zoremsanga C, Hussain J. A Comparative Study of Long Short-Term Memory for Rainfall Prediction in India. In: Proceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology, Lecture Notes in Networks and Systems. (Vol. 676, pp. 547-558) Singapore. Springer . 2023.
Kothawale DR, Rajeevan M. Monthly, Seasonal and Annual Rainfall Time Series for All-India, Homogeneous Regions and Meteorological Subdivisions: 1871-2016. 2017. Available from: https://www.tropmet.res.in/~lip/Publication/RR-pdf/RR-138.pdf
Hunasigi P, Jedhe S, Mane M, Patil-Shinde V. Multilayer perceptron neural network based models for prediction of the rainfall and reference crop evapotranspiration for sub-humid climate of Dapoli, Ratnagiri District, India. Acta Ecologica Sinica. 2023;43(1):154–201. Available from: https://dx.doi.org/10.1016/j.chnaes.2022.09.004
Feng S, Wang Y, Liu L, Wang D, Yu G. Attention based hierarchical LSTM network for context-aware microblog sentiment classification. World Wide Web. 2019;22(1):59–81. Available from: https://dx.doi.org/10.1007/s11280-018-0529-6
Ying W, Zhang L, Deng H. Sichuan dialect speech recognition with deep LSTM network. Frontiers of Computer Science. 2020;14(2):378–387. Available from: https://dx.doi.org/10.1007/s11704-018-8030-z
Song S, Huang H, Ruan T. Abstractive text summarization using LSTM-CNN based deep learning. Multimedia Tools and Applications. 2019;78(1):857–875. Available from: https://dx.doi.org/10.1007/s11042-018-5749-3
Abdel-Nasser M, Mahmoud K. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications. 2019;31(7):2727–2740. Available from: https://doi.org/10.1007/s00521-017-3225-z
Yu Y, Si X, Hu C, Zhang J. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation. 2019;31(7):1235–1270. Available from: https://dx.doi.org/10.1162/neco_a_01199
Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing. 1997;45(11):2673–2681. Available from: https://dx.doi.org/10.1109/78.650093
Ewees AA, Al-qaness MAA, Abualigah L, Elaziz MA. HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting. Energy Conversion and Management. 2022;268. Available from: https://dx.doi.org/10.1016/j.enconman.2022.116022
Mahmoodzadeh A, Nejati HR, Mohammadi M, Ibrahim HH, Rashidi S, Rashid TA. Forecasting tunnel boring machine penetration rate using LSTM deep neural network optimized by grey wolf optimization algorithm. Expert Systems with Applications. 2022;209. Available from: https://dx.doi.org/10.1016/j.eswa.2022.118303

Copyright

© 2024 Zoremsanga & Hussain. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)