Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil DOI Creative Commons

Jorge da Paixão Marques Filho,

Antônio José Teixeira Guerra, Carla Bernadete Madureira Cruz

и другие.

Land, Год журнала: 2024, Номер 13(10), С. 1665 - 1665

Опубликована: Окт. 13, 2024

Soil erosion is a global issue—with gully recognized as one of the most important forms land degradation. The purpose this study to compare and contrast outcomes four machine learning models, Classification Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), used for mapping susceptibility soil erosion. controlling factors in Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation Google Earth samples (n = 159) modelling spatial prediction. XGBoost RF models achieved identical results area under receiver operating characteristic curve (AUROC 88.50%), followed SVM CART respectively 86.17%; AUROC 85.11%). In all analysed, importance main predominated among Lineaments, Land Use Cover, Slope, Elevation Rainfall, highlighting need understand landscape. model, considering smaller number false negatives prediction, was considered appropriate, compared model. It noteworthy that model made it possible validate hypothesis area, identifying 9.47% Basin susceptible Furthermore, replicable methodologies are evidenced their rapid applicability at different scales.

Язык: Английский

Evaluation of the gully erosion susceptibility by using UAV and hybrid models based on machine learning DOI

Qian Wang,

Bo‐Hui Tang, Kailin Wang

и другие.

Soil and Tillage Research, Год журнала: 2024, Номер 244, С. 106218 - 106218

Опубликована: Июль 5, 2024

Язык: Английский

Процитировано

3
Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India DOI Open Access
Md Hasanuzzaman, Pravat Kumar Shit, Saeed Alqadhi

и другие.

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

Процитировано

1
Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China DOI Open Access
Peng Xie, Shihang Wang, Meiyan Wang

и другие.

Sustainability, Год журнала: 2024, Номер 16(10), С. 3917 - 3917

Опубликована: Май 8, 2024

Drainage difficulties in the waterlogged areas of sloping cropland not only impede crop development but also facilitate formation erosion gullies, resulting significant soil and water loss. Investigating distribution these is crucial for comprehending patterns preserving black resource. In this study, we built varied models based on two stages (one using deep learning methods other combining object-based image analysis (OBIA) with methods) to identify high-resolution remote sensing data. The results showed that five original imagery achieved precision rates varying from 54.6% 60.9%. Among models, DeepLabV3+-Xception model highest accuracy, as indicated by an F1-score 53.4%. identified demonstrated a distinction categories areas: zones risk areas. former had obvious borders fewer misclassifications, exceeding latter terms identification accuracy. Furthermore, accuracy was significantly improved when combined object-oriented analysis. DeepLabV3+-MobileNetV2 maximum 59%, which 6% higher than imagery. Moreover, advancement mitigated issues related boundary blurriness noise process. These will provide scientific assistance managing reducing impact places.

Язык: Английский

Процитировано

0
Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil DOI Creative Commons

Jorge da Paixão Marques Filho,

Antônio José Teixeira Guerra, Carla Bernadete Madureira Cruz

и другие.

Land, Год журнала: 2024, Номер 13(10), С. 1665 - 1665

Опубликована: Окт. 13, 2024

Soil erosion is a global issue—with gully recognized as one of the most important forms land degradation. The purpose this study to compare and contrast outcomes four machine learning models, Classification Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), used for mapping susceptibility soil erosion. controlling factors in Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation Google Earth samples (n = 159) modelling spatial prediction. XGBoost RF models achieved identical results area under receiver operating characteristic curve (AUROC 88.50%), followed SVM CART respectively 86.17%; AUROC 85.11%). In all analysed, importance main predominated among Lineaments, Land Use Cover, Slope, Elevation Rainfall, highlighting need understand landscape. model, considering smaller number false negatives prediction, was considered appropriate, compared model. It noteworthy that model made it possible validate hypothesis area, identifying 9.47% Basin susceptible Furthermore, replicable methodologies are evidenced their rapid applicability at different scales.

Язык: Английский

Процитировано

0