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Rajarshi Das Bhowmik

affiliation not provided to SSRN

SCHOLARLY PAPERS

4

DOWNLOADS

155

TOTAL CITATIONS

0

Scholarly Papers (4)

1.

Process Guided Graph-based Transformer Learning for Streamflow Predictions in Data-Sparse River Basins

Number of pages: 46 Posted: 27 Dec 2025
Venkatesh Budmala, Sai Vikas Kona, Rajarshi Das Bhowmik and Hyunglok Kim
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 77 (891,180)

Abstract:

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Prediction in Ungauged Basins (PUB), Process-Guided Machine Learning, Graph Transformer Networks, streamflow prediction, Data-Sparse River Basins, Hydrological Model Emulation

2.

Estimating the Spatial and Model Uncertainties in Yielding Extreme Rainfall Return Levels Across India

Number of pages: 66 Posted: 23 Jul 2024
Poornima Chandra Lekha Posa and Rajarshi Das Bhowmik
affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 37 (1,236,159)

Abstract:

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Non-Stationary, Generalized Extreme Value, Teleconnections, Annual Maximum Rainfall, Uncertainty Analysis

3.

Application of Machine Learning-Based Postprocessing to Improve Crowd-Sourced Rainfall Outlooks

Number of pages: 30 Posted: 10 Jul 2024
Mohammad Ashar Hussain, Venkatesh Budamala and Rajarshi Das Bhowmik
University of Wisconsin-Madison, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 26 (1,399,036)

Abstract:

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Crowdsourcing, Machine Learning, Stochastic Rainfall Generator, Urban Rainfall, Citizen Science, Bias-Correction

4.

Unifying Hydrologic and Hydraulic Models Using a Machine Learning-Based Generalized Emulator

Number of pages: 34 Posted: 06 Jun 2026
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, Indian Institute of Technology (IIT), Roorkee and affiliation not provided to SSRN
Downloads 15

Abstract:

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Hydrologic-Hydraulic Model Integration, Machine Learning Emulator, XGBoost Surrogate Modeling, Parameter Sensitivity Analysis, Optimization