Gully erosion susceptibility mapping and prioritization of gully-dominant sub-watersheds using machine learning algorithms: Evidence from the Silabati River (tropical river, India)

Advances in Space Research, Journal Year: 2023, Volume and Issue: 73(3), P. 1653 - 1666

Published: Nov. 3, 2023

Language: Английский

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Potential Soil Erosion Mapping and Priority-Based Adaption Strategies Using RUSLE and Geospatial Techniques

Journal of Hydrologic Engineering, Journal Year: 2024, Volume and Issue: 29(3)

Published: March 13, 2024

Language: Английский

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

Sustainability, Journal Year: 2024, Volume and Issue: 16(15), P. 6569 - 6569

Published: July 31, 2024

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

Language: Английский

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Assessment and quantification of sediment retention and dam management in arid environments using remote sensing techniques

Arabian Journal of Geosciences, Journal Year: 2023, Volume and Issue: 16(10)

Published: Sept. 14, 2023

Language: Английский

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Prediction of soil erosion and sediment yield in an ungauged basin based on land use land cover changes

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 196(1)

Published: Dec. 19, 2023

Language: Английский

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