Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia

Gizaw Tesfaye; Daniel Bekele; Melat Eshetu; Mohamed Rabo; Abebe Bezu; Abera Asefa

doi:doi:10.11648/j.ajese.20240803.11

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| Peer-Reviewed

Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia

Gizaw Tesfaye^*

, Daniel Bekele, Melat Eshetu, Mohamed Rabo, Abebe Bezu, Abera Asefa

Published in American Journal of Environmental Science and Engineering (Volume 8, Issue 3)

Received: 12 August 2024 Accepted: 4 September 2024 Published: 23 September 2024

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Abstract

One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.

Published in	American Journal of Environmental Science and Engineering (Volume 8, Issue 3)
DOI	10.11648/j.ajese.20240803.11
Page(s)	49-64
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Gully Erosion, Machine Learning, Random Forest, Bivariate Models, Ethiopia

References

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Tesfaye, G., Bekele, D., Eshetu, M., Rabo, M., Bezu, A., et al. (2024). Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia. American Journal of Environmental Science and Engineering, 8(3), 49-64. https://doi.org/10.11648/j.ajese.20240803.11

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Tesfaye, G.; Bekele, D.; Eshetu, M.; Rabo, M.; Bezu, A., et al. Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia. Am. J. Environ. Sci. Eng. 2024, 8(3), 49-64. doi: 10.11648/j.ajese.20240803.11

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AMA Style

Tesfaye G, Bekele D, Eshetu M, Rabo M, Bezu A, et al. Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia. Am J Environ Sci Eng. 2024;8(3):49-64. doi: 10.11648/j.ajese.20240803.11

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@article{10.11648/j.ajese.20240803.11,
  author = {Gizaw Tesfaye and Daniel Bekele and Melat Eshetu and Mohamed Rabo and Abebe Bezu and Abera Asefa},
  title = {Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia
},
  journal = {American Journal of Environmental Science and Engineering},
  volume = {8},
  number = {3},
  pages = {49-64},
  doi = {10.11648/j.ajese.20240803.11},
  url = {https://doi.org/10.11648/j.ajese.20240803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20240803.11},
  abstract = {One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.
},
 year = {2024}
}

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TY  - JOUR
T1  - Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia

AU  - Gizaw Tesfaye
AU  - Daniel Bekele
AU  - Melat Eshetu
AU  - Mohamed Rabo
AU  - Abebe Bezu
AU  - Abera Asefa
Y1  - 2024/09/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ajese.20240803.11
DO  - 10.11648/j.ajese.20240803.11
T2  - American Journal of Environmental Science and Engineering
JF  - American Journal of Environmental Science and Engineering
JO  - American Journal of Environmental Science and Engineering
SP  - 49
EP  - 64
PB  - Science Publishing Group
SN  - 2578-7993
UR  - https://doi.org/10.11648/j.ajese.20240803.11
AB  - One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.

VL  - 8
IS  - 3
ER  -

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Gizaw Tesfaye

Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agricultural Research Center, Adama, Ethiopia

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http://orcid.org/0000-0003-3104-3356
Daniel Bekele

Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agricultural Research Center, Adama, Ethiopia

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Melat Eshetu

Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agricultural Research Center, Adama, Ethiopia

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Mohamed Rabo

Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agricultural Research Center, Adama, Ethiopia

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Abebe Bezu

Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agricultural Research Center, Adama, Ethiopia

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Abera Asefa

Ethiopian Institute of Agricultural Research (EIAR), Melkassa Agricultural Research Center, Adama, Ethiopia

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Tesfaye, G., Bekele, D., Eshetu, M., Rabo, M., Bezu, A., et al. (2024). Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia. American Journal of Environmental Science and Engineering, 8(3), 49-64. https://doi.org/10.11648/j.ajese.20240803.11

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Tesfaye, G.; Bekele, D.; Eshetu, M.; Rabo, M.; Bezu, A., et al. Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia. Am. J. Environ. Sci. Eng. 2024, 8(3), 49-64. doi: 10.11648/j.ajese.20240803.11

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Tesfaye G, Bekele D, Eshetu M, Rabo M, Bezu A, et al. Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia. Am J Environ Sci Eng. 2024;8(3):49-64. doi: 10.11648/j.ajese.20240803.11

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@article{10.11648/j.ajese.20240803.11,
  author = {Gizaw Tesfaye and Daniel Bekele and Melat Eshetu and Mohamed Rabo and Abebe Bezu and Abera Asefa},
  title = {Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia
},
  journal = {American Journal of Environmental Science and Engineering},
  volume = {8},
  number = {3},
  pages = {49-64},
  doi = {10.11648/j.ajese.20240803.11},
  url = {https://doi.org/10.11648/j.ajese.20240803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20240803.11},
  abstract = {One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.
},
 year = {2024}
}

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TY  - JOUR
T1  - Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia

AU  - Gizaw Tesfaye
AU  - Daniel Bekele
AU  - Melat Eshetu
AU  - Mohamed Rabo
AU  - Abebe Bezu
AU  - Abera Asefa
Y1  - 2024/09/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ajese.20240803.11
DO  - 10.11648/j.ajese.20240803.11
T2  - American Journal of Environmental Science and Engineering
JF  - American Journal of Environmental Science and Engineering
JO  - American Journal of Environmental Science and Engineering
SP  - 49
EP  - 64
PB  - Science Publishing Group
SN  - 2578-7993
UR  - https://doi.org/10.11648/j.ajese.20240803.11
AB  - One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.

VL  - 8
IS  - 3
ER  -

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