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sidchat06/README.md

Siddharth Chatterjee

Flood risk engineer, completing an Erasmus Mundus Joint Master's Degree in Flood Risk Management across four European universities.

I work on flood forecasting and flood extent mapping, using satellite data, hydrological models and machine learning. Most of what I do lives between hydrology and code: Sentinel-1 SAR processing, terrain analysis, segmentation models, and the data plumbing that connects them.

Publication Khatun, A., Nisha, M.N., Chatterjee, S., & Sridhar, V. (2024). A novel insight on input variable and time lag selection in daily streamflow forecasting using deep learning models. Environmental Modelling & Software, 179, 106126. https://doi.org/10.1016/j.envsoft.2024.106126

Tools Python (rasterio, geopandas, PyTorch, scikit-learn) · QGIS · ESA SNAP · WhiteboxTools · HEC-RAS · MATLAB

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  1. Modal-Analysis-of-beams-in-MATLAB Modal-Analysis-of-beams-in-MATLAB Public

    MATLAB modal analysis of cantilever, fixed and simply supported beams: eigenvalues, natural frequencies and mode shapes, with simulated damage cases.

    MATLAB 1

  2. Analysis-of-Beams-using-Stiffness-Matrix-in-MATLAB Analysis-of-Beams-using-Stiffness-Matrix-in-MATLAB Public

    Direct stiffness method for beam analysis in MATLAB: global stiffness assembly and nodal displacement solution.

    MATLAB

  3. Flood-Forecasting-using-Deep-Learning-techniques Flood-Forecasting-using-Deep-Learning-techniques Public

    Daily inflow forecasting for the Hirakud reservoir, Mahanadi basin: DNN, 1D-CNN, LSTM and hybrid CNN-LSTM compared. Basis of AGU 2022 and Roorkee Water Conclave papers.

    Jupyter Notebook