Influences of soil erosion susceptibility toward overloading vulnerability of the gully head bundhs in Mayurakshi River basin of eastern Chottanagpur Plateau

Pal, Swades; Debanshi, Sandipta

doi:10.1007/s10668-017-9963-3

Cited by 26 publications

(2 citation statements)

References 52 publications

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“…Higher drainage frequency and drainage density in its upper part coupling with relatively steeper slope and fragile laterite soil strongly control the gully erosion. Pal and Debanshi (2018) also condemned the fragility of laterite soil as a dominant factor for the high rate of gully erosion in this area.…”

Section: Discussionmentioning

confidence: 99%

“…Geographically, the upper parts of the study region are high to very highly susceptible to gully erosion. Highly erodible coarse-textured laterite soil, relatively sloppy Chottanagpur plateau fringe, highly dense 1 st and 2 nd orders streams are mainly responsible for such greater gully erosion susceptibility (Pal, 2016;Pal and Debanshi, 2018;Debanshi and Pal, 2020). Areal patches of very highly erosion susceptibility are looking quite continuous in case of point-based modelling and this geolocation is deliberately different from pixel based ML model output.…”

Section: Gully Erosion Susceptible Models Pbm and Spbmmentioning

confidence: 98%

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Point and pixel inclusive machine learning models for exploring gully erosion susceptibility

Pal

Debanshi

Paul³

2022

Geocarto International

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Sample point based spatial model derived from Machine Learning (ML) algorithms often generalizes the spatial pattern of event. The present study has tried to highlight how far it is acceptable and can it replace pixel based modelling? The present study has presented a comparative view of pixel and sample point based modelling of gully erosion susceptibility of the upper Mayurakshi basin to assess the predictabilities. Random forest (RF), Support Vector Machine (SVM) and (ADB) were applied for developing pixel and point based models in WEKA and Python software environments respectively. From the models, it is found that 14-25% area located mainly in the upper parts of the study unit is very highly susceptible to gully erosion. Based on the accuracy and performance level using Area under curve (AUC) of Receiver operating curve, sensitivity, precision, F1 score, MCC pixel based ensemble models are superior to point based modelling. Often, the point based models have a very poor agreement with training and testing data. So, pixel based models could not be replaced with point based models. RF is found as the best representative model. The study, therefore, recommends using pixel based modelling for this or a similar purpose. Since the models have figured out the gully erosion susceptible areas, it would be a useful tool for related planning processes.

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Section: Discussionmentioning

confidence: 99%

Section: Gully Erosion Susceptible Models Pbm and Spbmmentioning

confidence: 98%