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

Hasanuzzaman, Md; Pratim Adhikary, Partha; Kumar Shit, Pravat

doi:10.1016/j.asr.2023.10.051

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…We conducted a statistical comparison of the contributions of our study and those of existing technologies, and the results are presented in Table 4. While deep learning models have been refined in numerous studies, resulting in improved identification accuracy ranging from 70% to 84%, it is worth noting that these studies typically utilize low-resolution images [35,36]. Our focus was on investigating the impact of integrating OBIA with various deep learning models and high-resolution images.…”

Section: Discussionmentioning

confidence: 99%

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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

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Section: Discussionmentioning

confidence: 99%

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Xie,

Wang,

Wang

et al. 2024

Sustainability

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

Wang,

Tang,

Wang

et al. 2024

Soil and Tillage Research

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

Hasanuzzaman,

Shit,

Alqadhi

et al. 2024

Sustainability

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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.

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

Cited by 3 publications

References 43 publications

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

Waterlogged Area Identification Models Based on Object-Oriented Image Analysis and Deep Learning Methods in Sloping Croplands of Northeast China

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

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

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