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GIS-Based Disaster Risk Analysis of Floods Using Certainty Factor (CF) and Its Ensemble with Deep Learning Neural Network (DLNN): A Case Study of Dima Hasao District of Assam, India

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Emerging Technologies for Water Supply, Conservation and Management

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Abstract

Assam, a north-eastern state of India is vulnerable to floods and other natural calamities due to its wide-ranging network of river system that have a detrimental effect on the state's overall growth. Every year, during the monsoon season, the Brahmaputra and Barak Rivers, which receive water from more than 50 tributaries, produce severe floods. Dima Hasao was severely affected district in the first wave of flood in the year 2022 as communication issues starting in mid-May 2022. This research is aimed to assess disaster risk of floods in Dima Hasao district of Assam. So, this study considered various geo-physical factors and mapped for examining flood risk using certainty factor (CF) and its ensemble with deep learning neural networks (DLNN). Initially, sixteen flood conditioning factors and a numbers of historical flood locations were taken into consideration, and then the dataset were divided into training (70%) and testing datasets (30%). The findings revealed that the northern parts of the study area is where floods are most likely to occur, while the southern part is where they are least likely to occur. Model validation results using Receiver Operating Characteristic (ROC) curve further revealed that DLNN-CF is the most performant model with Area Under Curve (AUC) value of 0.974 in compare to stand-alone CF model with AUC = 0.963. For managing and mitigating future floods in the study region, this study could be useful to the decision makers and land use planners.

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Acknowledgements

We thankfully acknowledge the reviewers and editorial team for their valuable time, productive comments and suggestions for improving the overall quality of the manuscript.

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Authors and Affiliations

  1. Department of Geography, Faculty of Science, Aligarh Muslim University, Aligarh, 202002, India

    Sk Ajim Ali, Farhana Parvin & Rukhsar Anjum

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  1. Sk Ajim Ali
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  2. Farhana Parvin
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  3. Rukhsar Anjum
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Correspondence to Sk Ajim Ali or Farhana Parvin .

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Editors and Affiliations

  1. Department of Geology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India

    Etikala Balaji

  2. Department of Geology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India

    Golla Veeraswamy

  3. Department of Geology, Central Tribal University of Andhra Pradesh, Vizianagaram, Andhra Pradesh, India

    Prasad Mannala

  4. Department of Civil Engineering, Jamia Millia Islamia, New Delhi, Delhi, India

    Sughosh Madhav

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Authors Contributions

Sk Ajim Ali: Conceptualization, model preparation, methodology development, Writing-original draft, Software, Formal analysis, Visualization. Farhana Parvin and Rukhsar Anjum: Formal analysis; Data curation, Writing, Review and editing.

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There are no conflicts of interest to declare.

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The data that support the findings of this study are available from the corresponding authors, [Sk Ajim Ali, skajimali.saa@gmail.com and Farhana Parvin, farhanaparvin93@gmail.com], upon reasonable request.

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Ali, S.A., Parvin, F., Anjum, R. (2023). GIS-Based Disaster Risk Analysis of Floods Using Certainty Factor (CF) and Its Ensemble with Deep Learning Neural Network (DLNN): A Case Study of Dima Hasao District of Assam, India. In: Balaji, E., Veeraswamy, G., Mannala, P., Madhav, S. (eds) Emerging Technologies for Water Supply, Conservation and Management. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-031-35279-9_10

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