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Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India

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  • Md Hasanuzzaman

    (PG Department of Geography, Raja N. L. Khan Women’s College (Autonomous), Gope Palace, Midnapore 721102, India
    Research Centre in Natural and Applied Science, Raja N. L. Khan Women’s College (Autonomous), Vidyasagar University, Midnapore 721102, India)

  • Pravat Kumar Shit

    (PG Department of Geography, Raja N. L. Khan Women’s College (Autonomous), Gope Palace, Midnapore 721102, India)

  • Saeed Alqadhi

    (Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia)

  • Hussein Almohamad

    (Department of Geography, College of Languages and Human Sciences, Qassim University, Buraydah 51452, Saudi Arabia)

  • Fahdah Falah ben Hasher

    (Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Hazem Ghassan Abdo

    (Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous P.O. Box 2147, Syria)

  • Javed Mallick

    (Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha 61411, Saudi Arabia)

Abstract

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices.

Suggested Citation

  • Md Hasanuzzaman & Pravat Kumar Shit & Saeed Alqadhi & Hussein Almohamad & Fahdah Falah ben Hasher & Hazem Ghassan Abdo & Javed Mallick, 2024. "Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India," Sustainability, MDPI, vol. 16(15), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6569-:d:1447261
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    References listed on IDEAS

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    1. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1231-1258, June.
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