Assessment of gully erosion susceptibility using four data-driven models AHP, FR, RF and XGBoosting machine learning algorithms

Natural Hazards Research, Год журнала: 2024, Номер unknown

Опубликована: Май 1, 2024

Gully erosion is a significant global threat to socioeconomic and environmental sustainability, making it widespread natural hazard. Developing spatial models for gully crucial local governance effectively implement mitigation measures promote regional development. This study applied two machine learning (ML) models, RF XGB, alongside an AHP-based multi-criteria decision method FR bivariate statistics, assess susceptibility (GES) in the Kangsabati River basin eastern India's Chotonagpur plateau fringe. A GIS database was created, incorporating recorded incidents 20 conditioning variables, which were evaluated multicollinearity. These variables served as predictive factors assessing presence area. The models' performance using metrics such RMSE, MAE, specificity, sensitivity, accuracy. XGB model outperformed others, achieving accuracy of 90.22%. found that approximately 6.56% catchment highly susceptible erosion, with 12.39% moderately 81.05% not susceptible. had highest ROC value 85.5 during testing, indicating its superiority over (ROC = 81.7), AHP 79.8), 83.8) models. findings highlight model's efficacy potential large-scale GES mapping.

Язык: Английский

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Assessing Groundwater Potential Zones and Their Implications for Landslides in the Upper Wabe‐Shebele River Basin of Southeastern Ethiopia

Journal of Engineering, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Groundwater is an essential resource, and its long‐term availability usability are closely tied to ability recharge. Therefore, the objective of this study was generate a geospatial map groundwater potential implications for landslides in Upper Wabe‐Shebele River Basin. To achieve this, integrated approach employed, combining GIS‐based MCDM under AHP. The considered rainfall patterns, land use cover, lineaments drainage density, soil texture, lithology, slope, proximity roads, static water level. These influential factors were identified through combination desk reviews, expert knowledge, experience field mapping. Then, pairwise comparison matrix formed assign weights each factor their subparameters based on relative importance potential. final generated using weighted overlay analysis tool ArcGIS 10.7. By analyzing incorporated factors, comprehensive understanding resources landslide occurrences area interrelated. AHP displayed acceptable positive principal eigenvalue (9.401) consistency ratio 0.035 < 0.1. zones occupying 1314.73 km 2 (12.81%), 4463.06 (43.5%), 3236.23 (31.54%), 283.31 (2.76%), 961.93 (9.38%) as very good, moderate, poor, poor zones, respectively. accuracy evaluated by overlaying it with existing borehole data. This revealed high degree agreement, approximately 80% data points consistent maps. In addition, correlation between occurrence observed. Based these findings, sustainable resource utilization suggested enhance management mitigate risks.

Язык: Английский

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CONTRIBUTION OF GEOPHYSICS TO THE STRATIGRAPHIC, STRUCTURAL AND NEOTECTONIC STUDY OF TAROUDANT REGION AND ADJACENT AREAS (SOUSS BASIN-SOUTHWESTERN MOROCCO)

Journal of African Earth Sciences, Год журнала: 2025, Номер unknown, С. 105591 - 105591

Опубликована: Фев. 1, 2025

Язык: Английский

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Changeability of rill morphometry during Succedent rainfall events due to soil Microorganisms’ inoculation

CATENA, Год журнала: 2025, Номер 253, С. 108883 - 108883

Опубликована: Март 9, 2025

Язык: Английский

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Transfer learning-based deep learning models for flood and erosion detection in coastal area of Algeria

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Апрель 21, 2025

Язык: Английский

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Deciphering Sediment Pathways: Novel Integrated Approaches for Sediment Source Identification and Vulnerability Prediction by Machine Learning Models in Major Dam Catchments in Chota Nagpur Plateau, India

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2025, Номер unknown, С. 103974 - 103974

Опубликована: Май 1, 2025

Язык: Английский

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Integration of FuzzyAHP and machine learning algorithms for climate-driven gully erosion susceptibility mapping: predicting future trends in the eastern lateritic region, West Bengal, India

Geosciences Journal, Год журнала: 2024, Номер 28(6), С. 981 - 1011

Опубликована: Окт. 16, 2024

Язык: 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, Год журнала: 2024, Номер 16(15), С. 6569 - 6569

Опубликована: Июль 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.

Язык: Английский

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Alluvial floodplain gully erosion in dryland rivers – An overlooked source of sediment with implications for river condition and management

Geomorphology, Год журнала: 2024, Номер unknown, С. 109430 - 109430

Опубликована: Сен. 1, 2024

Язык: Английский

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Gully erosion susceptibility mapping considering seasonal variations of NDVI using a machine learning approach in the Mollisol region of China

Soil and Tillage Research, Год журнала: 2024, Номер 245, С. 106322 - 106322

Опубликована: Окт. 15, 2024

Язык: Английский

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