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The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning

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Abstract

Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents’ basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that has been imposed by the neighboring country Myanmar which has forced the government to shelter almost a million Rohingya refugees in less than 3 years (2017–2020). The government had no other option but to acquire almost 24.1 km2 of forest areas only to construct refugee camps for the Rohingyas which has led to catastrophic environmental outcomes. This study has analyzed the land use and land surface temperature pattern change of the Rohingya camp area for the course of 1997 to 2022 with a 5-year interval rate. Future prediction of the land use and temperature of Teknaf and Ukhiya was also done in this process using a machine learning algorithm for the years 2028 and 2034. The analysis says that in the camp area, from 1997 to 2017, percentage of settlements increased from 5.28 to 11.91% but in 2022, it reached 70.09%. The same drastically changing trend has also been observed in the land surface temperature analysis. In the month of January, the average temperature increased from 18.86 to 21.31 °C between 1997 and 2017. But in 2022. it was found that the average temperature had increased up to 25.94 °C in only a blink of an eye. The future prediction of land use also does not have anything pleasing in store.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. No datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

    Faishal Ahmed, Ovi Ranjan Saha & Afeefa Rahman

  2. Water Resources Management Division, Center for Environmental and Geographic Information Services, Dhaka, 1212, Bangladesh

    Siam Alam

  3. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA

    Afeefa Rahman

Authors
  1. Faishal Ahmed
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  2. Siam Alam
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  3. Ovi Ranjan Saha
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  4. Afeefa Rahman
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Contributions

Conceptualization-S.A., O.R.S., A.R.;

Methodology-S.A., O.R.S., A.R.;

Software-F.A., S.A., O.R.S.;

Prediction-F.A., S.A.;

Validation-S.A.;

Formal Analysis-F.A., S.A., O.R.S.;

Investigation-F.A.;

Resources-S.A., O.R.S.;

Writing-F.A.; S.A.;

Supervision-A.R.;

Corresponding author

Correspondence to Siam Alam.

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The authors declare no competing interests.

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Ahmed, F., Alam, S., Saha, O.R. et al. The Rohingya refugee crisis in Bangladesh: assessing the impact on land use patterns and land surface temperature using machine learning. Environ Monit Assess 196, 555 (2024). https://doi.org/10.1007/s10661-024-12701-3

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  • DOI: https://doi.org/10.1007/s10661-024-12701-3

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