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Analysis of land use/land cover change (LULCC) and debris flow risks in Adama district, Ethiopia, aided by numerical simulation and deep learning-based remote sensing

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Abstract

Detecting land use/land cover change (LULCC) and assessing the risk of slope failure and debris flow has been a worldwide concern. This study is the first in Adama District, Ethiopia, to use deep learning (DL)-based remote sensing to assess LULCC and predict the risk of slope failures and debris flows using numerical simulation methods. This study uses DL and remote sensing to analyse the spatiotemporal changes in LULC and landslide sites. The enhanced detection of debris flow susceptibility areas enabled the precise prediction of these areas’ location and sphere of influence and the precise evaluation of debris flow risk. This led to a reduction in the losses caused by such disasters. Changes in the six classes of LULC were assessed with an overall accuracy of above 87% and an overall kappa statistic of 85%. The results revealed a decreased trend in grassland, shrubland, and bareland over 30 years (1991–2021) of − 31.03 km2, − 38.15 km2, and − 114.19 km2, respectively. Also, a recent analysis of land-use maps from the past three decades reveals that the built-up area has increased significantly, from 0.95% to 5.64%. In contrast, shrubland has decreased notably, from 12.01 to 7.78% since 2021. These changes suggest that human activity significantly impacts the landscape, and that more needs to be done to protect our natural resources. The depth-integrated particle method flow simulation technique reveals high landslide risk in Adama City and Wonji sugar cane fields, aiding decision-makers in reducing damage and limiting over-cultivation in high-risk areas.

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Acknowledgements

We would like to extend our appreciation to the Ethiopian Artificial Intelligence Institute and the Ethiopian Space Science and Geospatial Institute for their invaluable contribution in providing us with elevation data, satellite information, and historical land use maps.

Funding

This research received no specific grants from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Artificial Intelligence Institute, P .O. Box 40782, Addis Ababa, Ethiopia

    Amanuel Kumsa Bojer, Muhammed Edris Ahmed, Desta Jula Bekalo, Taye Girma Debelee & Almaz Deche

  2. Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, PO Box. 16417, Addis Ababa, Ethiopia

    Taye Girma Debelee

  3. Petroleum and Mining Engineering Department, Tishk International University, Erbil 44001, Kurdistan Region, Iraq

    Ayad M. Fadhil Al-Quraishi

  4. Department of Geography and Environmental Studies, Assosa University, Assosa, Ethiopia

    Almaz Deche

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  1. Amanuel Kumsa Bojer
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  2. Muhammed Edris Ahmed
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  4. Taye Girma Debelee
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Contributions

The study's conception and design were contributed to by all of the authors. AB, AA, TD, and MA prepared the materials, collected data, and conducted the analyses. AB mainly prepared the first draft of the manuscript, and all authors provided feedback on work. The final manuscript was read and approved by all of the authors.

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Correspondence to Amanuel Kumsa Bojer or Ayad M. Fadhil Al-Quraishi.

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Bojer, A.K., Ahmed, M.E., Bekalo, D.J. et al. Analysis of land use/land cover change (LULCC) and debris flow risks in Adama district, Ethiopia, aided by numerical simulation and deep learning-based remote sensing. Stoch Environ Res Risk Assess 37, 4893–4910 (2023). https://doi.org/10.1007/s00477-023-02550-w

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