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Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

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Abstract

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

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Availability of Data and Materials

The models and code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

The authors are grateful to the NRDMS, DST, for providing financial support (NRDMS/01/143/016) to carry out this research.

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Authors and Affiliations

  1. Department of Geography, The University of Burdwan, Bardhaman, 713104, West Bengal, India

    Rabin Chakrabortty, Subodh Chandra Pal, Paramita Roy, Indrajit Chowdhuri & Asish Saha

  2. Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111, Tehran, Iran

    Saeid Janizadeh

  3. School of Earth Sciences and Resources, China University of Geosciences Beijing, 29 Xueyuan Road, Beijing, 100083, China

    M. Santosh

  4. Department of Earth Science, University of Adelaide, Adelaide, SA, 5005, Australia

    M. Santosh

Authors
  1. Rabin Chakrabortty
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  2. Subodh Chandra Pal
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  3. Saeid Janizadeh
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  4. M. Santosh
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  5. Paramita Roy
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  6. Indrajit Chowdhuri
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  7. Asish Saha
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Contributions

Material preparation, data collection and analysis were performed by [Rabin Chakrabortty], and [Paramita Roy], The first draft of the manuscript was written by [Rabin Chakrabortty], [Subodh Chandra Pal], [Saeid Janizadeh], [M. Santosh], [Paramita Roy], Indrajit Chowdhuri] and [Asish Saha] commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Subodh Chandra Pal.

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Chakrabortty, R., Pal, S.C., Janizadeh, S. et al. Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model. Water Resour Manage 35, 4251–4274 (2021). https://doi.org/10.1007/s11269-021-02944-x

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