• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

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

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


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


  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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.
  9. 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
  10. 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
  11. 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.
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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


© 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)


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