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

Author

Listed:
  • Rabin Chakrabortty

    (The University of Burdwan)

  • Subodh Chandra Pal

    (The University of Burdwan)

  • Saeid Janizadeh

    (Tarbiat Modares University)

  • M. Santosh

    (China University of Geosciences Beijing
    University of Adelaide)

  • Paramita Roy

    (The University of Burdwan)

  • Indrajit Chowdhuri

    (The University of Burdwan)

  • Asish Saha

    (The University of Burdwan)

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.

Suggested Citation

  • Rabin Chakrabortty & Subodh Chandra Pal & Saeid Janizadeh & M. Santosh & Paramita Roy & Indrajit Chowdhuri & Asish Saha, 2021. "Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4251-4274, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02944-x
    DOI: 10.1007/s11269-021-02944-x
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    References listed on IDEAS

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    1. Yukiko Hirabayashi & Roobavannan Mahendran & Sujan Koirala & Lisako Konoshima & Dai Yamazaki & Satoshi Watanabe & Hyungjun Kim & Shinjiro Kanae, 2013. "Global flood risk under climate change," Nature Climate Change, Nature, vol. 3(9), pages 816-821, September.
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    6. Omid Rahmati & Hamid Reza Pourghasemi, 2017. "Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1473-1487, March.
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    Cited by:

    1. Liang Chen & Mingxiang Yang & Xuan Liu & Xing Lu, 2022. "Attribution and Sensitivity Analysis of Runoff Variation in the Yellow River Basin under Climate Change," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    2. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    3. Md. Uzzal Mia & Tahmida Naher Chowdhury & Rabin Chakrabortty & Subodh Chandra Pal & Mohammad Khalid Al-Sadoon & Romulus Costache & Abu Reza Md. Towfiqul Islam, 2023. "Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer," Land, MDPI, vol. 12(4), pages 1-26, April.

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