Hemal Dey

Dr. Hemal Dey

Dept. of Geography and the Environment, The University of Alabama, Tuscaloosa, Alabama, USA
orcid

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 May 2020 in Environment, Development and Sustainability
Verified
Journal Article
Closed access
PDF file
MQMohammad Abdul Quader
Mohammad Abdul Quader
Hemal DeyHemal Dey
Hemal Dey
AMAbdul Malak
Abdul Malak
ASAbdul Majed Sajib
Abdul Majed Sajib

1 September 2023 in Journal of Geovisualization and Spatial Analysis
Verified
Journal Article
Closed access
PDF file
ATAnnyca Tabassum
Annyca Tabassum
RBRony Basak
Rony Basak
WSWanyun Shao
Wanyun Shao
MHMd. Munjurul Haque
Md. Munjurul Haque
TCTahmid Anam Chowdhury
Tahmid Anam Chowdhury
Hemal DeyHemal Dey
Hemal Dey

23 April 2024 in Natural Hazards
Verified
Journal Article
Closed access
PDF file
Hemal DeyHemal Dey
Hemal Dey
WSWanyun Shao
Wanyun Shao
HMHamid Moradkhani
Hamid Moradkhani
BKBarry D. Keim
Barry D. Keim
BPBrad G. Peter
Brad G. Peter

6 December 2024 in npj Natural Hazards
Verified
Journal Article
Open access
PDF file
Hemal DeyHemal Dey
Hemal Dey
MHMunjurul Haque
Munjurul Haque
WSWanyun Shao
Wanyun Shao
MVMatthew VanDyke
Matthew VanDyke
FHFeng Hao
Feng Hao
Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study’s goal is to simulate flood risk and identify dominant FRFs using historical flood damage data as target variable, with 16 FRFs as predictor variables. Five different ML models such as decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and random forest (RF) were adopted. RF classifies 2.42% of Tampa Bay as very high risk and 2.54% as high risk, while XGBoost classifies 3.85% as very high risk and 1.11% as high risk. Moreover, the communities reside at low altitudes and near the waterbodies, with dense man-made infrastructure, are at high flood risk. This study introduces a comprehensive framework for flood risk assessment and helps policymakers mitigate flood risk.

22 May 2024 in Journal of Geovisualization and Spatial Analysis
Verified
Journal Article
Closed access
PDF file
Hemal DeyHemal Dey
Hemal Dey
WSWanyun Shao
Wanyun Shao
MHMunjurul Haque
Munjurul Haque
MVMatthew VanDyke
Matthew VanDyke

14 February 2023 in Singapore Journal of Tropical Geography
Verified
Journal Article
Closed access
MQMohammad Abdul Quader
Mohammad Abdul Quader
Hemal DeyHemal Dey
Hemal Dey
AMAbdul Malak
Abdul Malak
ZRZakiur Rahman
Zakiur Rahman