Bahram Gharabaghi

Professor

University of Guelph

Canada

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Professor Bahram Gharabaghi is a faculty member at the University of Guelph in Canada. His research focuses on the application of advanced machine learning algorithms, remote sensing, and deep learning to address challenges in urban flood forecasting, water quality monitoring, and environmental impacts. Recent publications include studies on urban flood susceptibility mapping, real-time monitoring of ice layer thickness, and precision salt application to enhance road safety.

Articles (20)

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2025

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2023

Collaborators (2)

Amir Abbas Aliabadi

University of Guelph

CANADA

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Prasad Daggupati

Associate Professor

University of Guelph

CANADA

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Bahram Gharabaghi

Research Interests

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About

Articles (20)

Continuous Wavelet Analysis of Water Quality Time Series in a Rapidly Urbanizing Mixed-Land-Use Watershed in Ontario, Canada

Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada

Precision salt application using advanced machine learning algorithms to achieve improved road safety and reduced environmental impacts

Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models

Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review

Comparative Life Cycle Assessment (LCA) in the Aerospace Industry Regarding Aviation Seat Frame Options

Deep Learning for Atmospheric Modeling: A Proof of Concept Using a Fourier Neural Operator on WRF Data to Accelerate Transient Wind Forecasting at Multiple Altitudes

Real-Time Monitoring and Forecasting Ice Layer Thickness Growth Rate and Grip Loss on a Road Network During Winter Storm Events

Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas

Decomposing streamflow for improved river flow prediction accuracy of machine learning models

Graph-Based Deep Learning Model for Forecasting Chloride Concentration in Urban Streams to Protect Salt-Vulnerable Areas

Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database

Spatiotemporal variability of minimum runoff generating areas: a field investigation

Novel Curve Fitting Analysis of NDVI Data to Describe Turf Fertilizer Response

Intelligent flood forecasting and warning: a survey

A Prediction Model to Cost-Optimize Clean-Out of Permeable Interlocking Concrete Pavers

A New Graph-Based Deep Learning Model to Predict Flooding with Validation on a Case Study on the Humber River

Modeling stormwater management pond thermal impacts during storm events

Deep Neural Network Modeling for CFD Simulations: Benchmarking the Fourier Neural Operator on the Lid-Driven Cavity Case

Spatiotemporal Air Pollution Forecasting in Houston-TX: A Case Study for Ozone Using Deep Graph Neural Networks

Collaborators (2)

Amir Abbas Aliabadi

Prasad Daggupati

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