You can use continuous wavelet transform (CWT) methods for mapping bank erosion potentiality. You can follow the paper entitled " Evolutionary, multi-scale analysis of river bank line retreat using continuous wavelet transforms: Jamuna River, Bangladesh".
Hi, Samrat Majumdar, It depends on how you want to formulate the problem. If it is a supervised learning-based problem and the datasets are not that big, you can use shallow neural networks, neuro-fuzzy, or fuzzy systems.
Dear Samrat,
Despite the availability of many techniques, the identification of erosion susceptible locations and prediction of riverbank erosion is difficult due to the dynamic and stochastic nature of the river channels. Numerical models such as support vector machine (SVM), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) have been used in fluvial geomorphology to model and predict the riverbank erosion probability
Have a look at these useful articles:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603737/
Article Application of numerical models to simulate the charland are...
https://link.springer.com/article/10.1007/s12518-019-00296-1
https://www.mdpi.com/2072-4292/8/3/241/htm
https://www.chijournal.org/Content/Files/R227-02.pdf
I hope it helps.
Thank you
Ankur
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