Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements.

@article{Saha2021OptimizationMT,
  title={Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements.},
  author={Asish Saha and Subodh Chandra Pal and Alireza Arabameri and Indrajit Chowdhuri and Fatemeh Rezaie and Rabin Chakrabortty and Paramita Roy and Manisa Shit},
  journal={Journal of environmental management},
  year={2021},
  volume={287},
  pages={
          112284
        }
}
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The sub-tropical countries like India experience large-scale land degradation due to erosion of the surface soil. So, there is a direct impact of monsoon climate in this region; large-scale
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
TLDR
The main objective of this work was to predict the susceptible zone with the maximum possible accuracy and the artificial neural network with 50/50 random partitioning of the sample is the most optimal model in this analysis.
Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India
Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high intensity and kinetic energy
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TLDR
The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data.
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