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The University of Massachusetts Amherst

Md Abul Ehsan Bhuiyan

NE CASC Postdoctoral Fellow
Postdoctoral Research Associate
University of Massachusetts

Research Interests

My research focuses on remote sensing, hydrology, meteorology, precipitation error modeling, extreme weather risk modeling, and data assimilation using Artificial Intelligence (AI) application. My research interest also demonstrates AI based algorithm in diverse tundra landscapes and mapping application in the pan-Arctic along with climate-change prediction, permafrost, and the modeling of hydro-meteorological processes.

Selected Publications:

Bhuiyan, M. A. E., E. I., Anagnostou, P.E. Kirstetter: A non-parametric statistical technique for modeling overland TMI (2A12) rainfall retrieval error. IEEE Geosci. Remote Sensing Letters, 14, 1898–1902, 2017.

Bhuiyan, M. A. E., Nikolopoulos, E. I., Anagnostou, E. N., Quintana-Seguí, P., and Barella-Ortiz, A.: A Nonparametric Statistical Technique for Combining Global Precipitation Datasets: Development and Hydrological Evaluation over the Iberian Peninsula, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2017-268, 2018. 

Bhuiyan, M. A. E., Nikolopoulos, E. I., Anagnostou, E. N., Albergel, C.,  Dutra, E.,  Fink, G.,  Martinez de la Torre A., Munier, S.,  Polcher, J.: Assessment of Precipitation Error Propagation in Multi-Model Global Water Resources Reanalysis, Hydrol. Earth Syst. Sci, https://doi.org/10.5194/hess-2018-434 2019.

Bhuiyan, M. A. E, Begum, F., Ilham, S. and Khan, R.S.: Advanced Wind Speed Prediction using Convective Weather Variables through Machine Learning Application. Applied Computing and Geosciences, 2019.

Bhuiyan, M. A. E., Nikolopoulos, E. I., and Anagnostou E. N.: Machine Learning-based Blending of Satellite and Reanalysis Precipitation Datasets: A Multi-regional Tropical Complex Terrain Evaluation, Journal of Hydrometeorology (JHM), 2019.

Bhuiyan, M. A. E., Feifei Yang, Nishan Kumar Biswas, Saiful Haque Rahat, Tahneen Jahan Neelam: Machine Learning-based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin, MDPI Forecasting, https://www.mdpi.com/2571-9394/2/3/14  2020.  

Bhuiyan, M. A. E., Chandi W., Anna, K. L., Benjamin, M. J., Ronald, D., Howard, E., Kelcy, K. and Claire, G.,Amber, A.: Understanding the effects of optimal combination of spectral bands on deep learning model predictions: A case study based on permafrost tundra landform mapping using high resolution multispectral satellite imagery, Journal of Imaging, MDPI, 2020.

Cerrai, D., Wanik, D. W., Bhuiyan, M. A. E., Zhang, X., Yang, J., Frediani M. E., B and Anagnostou E.N.: Predicting Storm Outages Through New Representations of Weather and Vegetation. IEEE ACCESS, 2019. 

Nikolopoulos, E. I., Destro, E., Bhuiyan, M. A. E., Borga, M., and Anagnostou, E. N.: Evaluation of predictive models for post-fire debris flows occurrence in the western United States, Nat. Hazards Earth Syst. Sci., https://doi.org/10.5194/nhess-2018-85, 2018.

Yang, F., Wanik, D. W., Cerrai, D., Bhuiyan, M. A. E., and Anagnostou, E. N.: Quantifying Uncertainty in Machine Learning–Based Power Outage Prediction Model Training,2020, MDPI, sustainability.

Mondal, A.R., Bhuiyan, M. A. E., and Yang, F.: Assessment of   the Weather Effects in Crash prediction Using Machine Learning Applications, SN Applied Sciences (SNAS), 2020.

 

Education

Ph.D. : Civil and Environmental Engineering, University of Connecticut, Storrs, 2018
M.Sc. : Civil Engineering, University of Louisiana at Lafayette, 2013
B.Sc. : Water Resources Engineering, Bangladesh University of Engineering and Technology, 2009

Affiliations

Department of Civil and Environmental Engineering

Experience

Postdoctoral Research Associate, University of Massachusetts, 2020-present
Postdoctoral Research Associate, Eversource Energy Center, University of Connecticut, Storrs, 2019-2020
Research Specialist, Eversource Energy Center, University of Connecticut, Storrs, 2019