Sayanti Mukherjee
Sayanti Mukherjee
Assistant Professor of Industrial and Systems Engineering at University at Buffalo (SUNY)
Verified email at - Homepage
Cited by
Cited by
A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S.
S Mukherjee, R Nateghi, M Hastak
Reliability Engineering & System Safety 175, 283-305, 2018
Climate sensitivity of end-use electricity consumption in the built environment: an application to the state of Florida, United States
S Mukherjee, R Nateghi
Energy 128, 688-700, 2017
A multi-paradigm framework to assess the impacts of climate change on end-use energy demand
R Nateghi, S Mukherjee
PloS one 12 (11), e0188033, 2017
Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region
P Alipour, S Mukherjee, R Nateghi
Energy 185, 1143-1153, 2019
A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand
S Mukherjee, R Nateghi
Risk Analysis, 2018
Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework
S Mukherjee, V C.R., R Nateghi
Applied Energy 235 (2019), 1561-1582, 2019
Evaluating the climate sensitivity of coupled electricity-natural gas demand using a multivariate framework
R Obringer, S Mukherjee, R Nateghi
Applied Energy 262 (114419), 2020
Estimating Climate-Demand Nexus to Support Long-term Adequacy Planning in the Energy Sector
S Mukherjee, R Nateghi
Power & Energy Society General Meeting, 2017 IEEE, 10.1109/PESGM.2017.8274648, 2018
Data on major power outage events in the continental US
S Mukherjee, R Nateghi, M Hastak
Data in brief 19, 2079-2083, 2018
Projected climate change impacts on Indiana’s energy demand and supply
L Raymond, D Gotham, W McClain, S Mukherjee, R Nateghi, PV Preckel, ...
Climatic Change, 1-15, 2019
Implications of increasing household air conditioning use across the United States under a warming climate
R Obringer, R Nateghi, D Maia‐Silva, S Mukherjee, V CR, DB McRoberts, ...
Earth's Future 10 (1), e2021EF002434, 2022
A two-stage data-driven spatiotemporal analysis to predict failure-risk of urban sewer systems leveraging machine learning algorithms
JE Fontecha, P Agarwal, MN Torres, S Mukherjee, JL Walteros, ...
Risk Analysis, 2021
Public Utility Commissions to Foster Resilience Investment in Power Grid Infrastructure
S Mukherjee, M Hastak
Procedia - Social and Behavioral Sciences 218, 5-12, 2016
Impact of geophysical and anthropogenic factors on wildfire size: A spatiotemporal data-driven risk assessment approach using statistical learning
N Masoudvaziri, P Ganguly, S Mukherjee, K Sun
Stochastic Environmental Research and Risk Assessment, 1103–1129, 2021
A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus
S Mukherjee, E Frimpong Boamah, P Ganguly, N Botchwey
Scientific Reports 11 (17548), 2021
Health-behaviors associated with the growing risk of adolescent suicide attempts: A data-driven cross-sectional study
Z Wei, S Mukherjee
American journal of health promotion 35 (5), 688-693, 2021
A novel methodological approach to estimate the impact of natural hazard-induced disasters on country/region-level economic growth
S Mukherjee, M Hastak
International Journal of Disaster Risk Science 9, 74-85, 2018
Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach
S Mukherjee, Z Wei
Plos one 16 (11), e0258824, 2021
A multifaceted risk assessment approach using statistical learning to evaluate socio-environmental factors associated with regional felony and misdemeanor rates
P Ganguly, S Mukherjee
Physica A: Statistical Mechanics and its Applications 574, 125984, 2021
Compare and Contrast Major Nuclear Power Plant Disasters: Lessons Learned from the Past
S Mukherjee, J Halligan, M Hastak
10th International Conference of the International Institute for …, 2014
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