Niazkar Majid

Institute for Climate Resilience
Risk Assessment and Adaptation Strategies Division

Edificio Porta dell’Innovazione – Piano 2, via della Libertà 12 – 30175 Venezia Marghera (VE)

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Majid Niazkar joins CMCC for a collaboration within the Risk Assessment and Adaptation Strategies Division (RAAS) working on the following Projects: CLIMAAX, ASPECT, TRANSCEND, TALANOA-WATER, and REACHOUT.

He holds a PhD in Civil Engineering – Water Resources at the Shiraz University (Iran). Previously, he worked as postdoctoral researcher at Shiraz University, University of Milan, and Free University of Bozen-Bolzano. His research interests are water resources, hydroinformatics, and climate change impact assessment.

His publications has been cited more than 1500 citations in Google scholar and selected as one of the World’s Top 2% Scientists in 2020 and 2021 by Standford University and Elsevier. One of his papers was recognized by Web of Science as a highly-cited paper in December 2024. He is an associate editor of “Computer Applications in Engineering Education”, and serves as academic editor for a few journals and a lead guest editor of several special issues. He is an editor of the book named “Water Resource Modeling and Computational Technologies”, which was published by Elsevier in October 2022.

LATEST PUBLICATIONS
Enhancing Prediction of Equilibrium Scour Depth Around Bridge Piers Using Staking Machine Learning Models
Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters
Intercomparison of machine learning methods for statistical downscaling of daily temperature under CMIP6 scenarios: a case study from Iran
Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City
Short-Term Drought Forecast across Two Different Climates Using Machine Learning Models
Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index
Editorial: Assessment of Climate Change Impact on Water Resources Using Machine Learning Algorithms
Assessing the impact of COVID-19 lockdowns on water quality in Anzali Wetland, Iran, using remote sensing data
A comparison of machine learning methods for estimation of snow density using satellite images
Hydraulic Conductivity Estimation: Comparison of Empirical Formulas Based on New Laboratory Experiments
View all articles by Niazkar Majid