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Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh

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A Correction to this article was published on 20 March 2021

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Abstract

Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management.

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All the data and materials related to the manuscript are published with the paper, and available from the corresponding author upon request.

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Acknowledgements

The authors would like to thank USGS for providing the satellite images.

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Authors and Affiliations

  1. Department of Disaster management, Begum Rokeya University, Rangpur, 5400, Bangladesh

    Abu Reza Md. Towfiqul Islam, Kutub Uddin Eibek & Shumona Akhter

  2. Research Scholars, Department of Geography, University of Gour Banga, Malda, India

    Swapan Talukdar, Susanta Mahato & Sk Ziaul

  3. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Quoc Bao Pham

  4. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Quoc Bao Pham

  5. Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden

    Babak Mohammadi

  6. Department of Geography, environment and sustainability, University of North Carolina-Greensboro, Greensboro, NC, USA

    Firoozeh Karimi

  7. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Nguyen Thi Thuy Linh

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  1. Abu Reza Md. Towfiqul Islam
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  2. Swapan Talukdar
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Contributions

Abu Reza Md. Towfiqul Islam and Swapan Talukdar conceptualized the work. Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Quoc Bao Pham, Babak Mohammadi, Firoozeh Karimi, and Nguyen Thi Thuy Linh provided overall guidance and continuous examination of the work. Susanta Mahato, Sk Ziaul, Kutub Uddin Eibek, and Shumona Akhter prepared datasets and carried out the analysis. Swapan Talukdar, Susanta Mahato, Sk Ziaul, Kutub Uddin Eibek, Shumona Akhter, Quoc Bao Pham, Babak Mohammadi, Firoozeh Karimi, and Nguyen Thi Thuy Linh initiated manuscript writing. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Nguyen Thi Thuy Linh.

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Islam, A.R.M.T., Talukdar, S., Mahato, S. et al. Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh. Environ Sci Pollut Res 28, 34450–34471 (2021). https://doi.org/10.1007/s11356-021-12806-z

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