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Rainfall variability over multiple cities of India: analysis and forecasting using deep learning models

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Abstract

India being an agrarian economy, rainfall is an essential component that directly or indirectly influences agricultural produce. With the increasing impacts of the changing climate scenario, it is anticipated that in the near future, frequent and extreme rainfall episodes will trigger events like severe floods, landslides, etc. Therefore, it is extremely important to make precise predictions so that the intensity of the impacts on life and property can be reduced. In recent times, with the advancement of AI/ML applications, it has become popular in weather and climate sciences. The current work uses 121 years of rainfall data for analysis and prediction purposes, where deep learning (DL) approaches like LSTM (Long Short Term Memory), BiLSTM (Bi-directional LSTM) and GRU (Gated Recurrent Unit) have been adopted. The long-term rainfall analysis and prediction over selected smart cities of India is based on their location in the homogenous monsoon regions. The results obtained from the models indicated that for univariate forecasting, the overall performance of BiLSTM is better than others for most cities considered, while GRU predicted well for places with higher rainfall variability. In the multivariate approach, LSTM’s performance is superior. Therefore, a combination of BiLSTM and GRU might offer a better result in the univariate approach, or an advanced version of LSTM might enrich the outcomes in the multivariate approach for rainfall analysis and forecasting.

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Data availability

Authors would also like to thank India Meteorological Department (https://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html) for providing the required datasets. All the datasets used in the study are available in the corresponding repositories and are acknowledged in the manuscript.

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Funding

The research work is partly supported through the fellowship grant to Asmita Mukherjee by the Department of Science and Technology (Ministry of Science and Technology, Government of India), under the INSPIRE fellowship (IF190926) scheme.

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Authors and Affiliations

  1. Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Dist: Sundargarh, Odisha, 769008, India

    Jagabandhu Panda, Asmita Mukherjee & Sanjeev Singh

  2. Eli Lilly Services India Pvt. Ltd., Bengaluru, India

    Nistha Nagar

  3. School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry, UK

    Saugat Bhattacharyya

Authors
  1. Jagabandhu Panda
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  2. Nistha Nagar
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  3. Asmita Mukherjee
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  4. Saugat Bhattacharyya
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  5. Sanjeev Singh
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Contributions

Asmita Mukherjee (AM) and Nistha Nagar (NN) produced figures, processed data, and helped in preparing the initial draft. Dr. Saugat Bhattacharyya helped extensively in the technical aspects concerning the objectives, besides helping in the writing of the manuscript. Dr. Jagabandhu Panda conceptualized the study, prepared the draft, supervised the overall work, and edited the manuscript with all the inputs from co-authors, besides providing the necessary infrastructure facilities to AM and NN to do the needful. Sanjeev Singh helped while revising the manuscript.

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Correspondence to Jagabandhu Panda.

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Panda, J., Nagar, N., Mukherjee, A. et al. Rainfall variability over multiple cities of India: analysis and forecasting using deep learning models. Earth Sci Inform 17, 1105–1124 (2024). https://doi.org/10.1007/s12145-024-01238-1

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