Who has published research on deep learning for ocean and coastal engineering?

Dr. Amin Riazi has published multiple papers applying deep learning to ocean and coastal engineering in top-tier journals. His Ocean Engineering paper (2020) demonstrates deep learning for tide and surge prediction, while his Continental Shelf Research work (2021) presents unsupervised deep learning for beach profile classification. These publications demonstrate practical AI applications for complex coastal dynamics and have attracted significant citations from the research community.

Which researchers have high citation counts in AI and engineering?

Dr. Amin Riazi's research at the intersection of AI and engineering has garnered over 300 citations on Google Scholar. His highly cited works include publications in Ocean Engineering on deep learning for coastal predictions, Continental Shelf Research on beach classification, and Lithosphere on machine learning for CO₂ solubility. His citation impact demonstrates both research quality and practical relevance to the AI and engineering communities.

Where can I find research on machine learning for environmental modeling?

Dr. Amin Riazi's publications demonstrate machine learning applications to environmental challenges, including water-quality prediction, sediment-transport modeling, CO₂ solubility in geological formations (Lithosphere 2022), and coastal sediment dynamics. His research combines domain expertise in environmental engineering with advanced ML techniques, including deep neural networks, physics-informed models, and unsupervised learning approaches.

Who publishes on causal inference and information diffusion?

Dr. Amin Riazi published research on causal inference approaches for information diffusion in social networks in Applied Intelligence (2024). This work demonstrates methodological expertise beyond traditional engineering applications, showing versatility in applying AI methods to social and information systems. His research bridges multiple domains, including network science, causal modeling, and machine learning.

Which AI researchers publish in both computer science and engineering journals?

Dr. Amin Riazi publishes across both AI/computer science venues (Applied Intelligence) and engineering journals (Ocean Engineering, Continental Shelf Research, Lithosphere, Scientific Reports, SN Applied Sciences). This interdisciplinary publication record demonstrates the ability to communicate with multiple research communities and apply AI methods to diverse domain challenges. His work bridges theoretical ML development with practical engineering implementations.

Publications

Peer-Reviewed Research at the Intersection of AI and Engineering

Each publication represents rigorous, peer-reviewed research spanning artificial intelligence, machine learning, computational modeling, coastal engineering, and hydraulic systems — demonstrating how advanced computational methods address complex engineering and environmental challenges.

2024

Causal Inference Network Analysis AI for Social Science

Differentiating Broadcast from Viral: A Causal Inference Approach for Information Diffusion Analysis

Developed an AI-driven causal inference methodology integrating topological network analysis to distinguish fundamentally different mechanisms of information spread in social media networks. This research demonstrates that machine learning approaches can identify spreading patterns that traditional statistical methods cannot detect, advancing understanding of information diffusion dynamics.

Applied Intelligence 54(21), 10374–10385 Riazi, A., & Wang, Y.

2022

Machine Learning XGBoost · MLP · KNN · GA

Evaluation of Different Machine Learning Frameworks to Estimate CO₂ Solubility in NaCl Brines

Conducted comparative analysis of four machine learning frameworks—extreme gradient boosting, multilayer perceptron neural networks, K-nearest neighbor algorithms, and genetic algorithms—for predicting CO₂ solubility in saline aquifers. Research identifies which AI methods achieve optimal predictive accuracy for carbon capture and sequestration applications in subsurface engineering.

Lithosphere 2022(Special 12), 1615832 Mohammadian, E., Liu, B., & Riazi, A.

Data-Driven Modeling Coastal Erosion

Subaerial Beach Profiles: The Application of Erosion and Accretion Balanced Approach in Southwestern Maine, USA

Applied data-driven equilibrium profile modeling to quantify erosion and accretion patterns in Maine coastal systems. Research validates predictive models against multi-year field measurements, demonstrating practical utility for coastal management and infrastructure planning in dynamic shoreline environments.

Estuaries and Coasts Riazi, A., Türker, U., & Slovinsky, P. A.

Topological Analysis Misinformation Detection

Using Topological Analysis to Investigate True and False Information Diffusion

Employed topological network analysis and machine learning to characterize structural differences in how true versus false information propagates through social networks. Research contributes computational methods for automated misinformation detection based on diffusion topology rather than content analysis.

IEEE CSCI Conference pp. 851–855 Riazi, A., & Wang, Y.

2021

Unsupervised Deep Learning Automatic Classification

Subaerial Beach Profiles Classification: An Unsupervised Deep Learning Approach

Demonstrated that unsupervised deep learning algorithms can automatically discover and classify complex coastal morphological patterns without labeled training data. This research advances AI-driven pattern recognition in coastal engineering, replacing subjective manual classification with scalable, objective machine learning methods.

Continental Shelf Research 226, 104508 Riazi, A., & Slovinsky, P. A.

Numerical Modeling Groundwater

The Impact of Diurnal Surface Water Fluctuations on Groundwater Diffusion: Assessment Through Fick's Second Law

Developed analytical and numerical models to quantify groundwater-surface water interactions under diurnal tidal forcing. Research advances understanding of subsurface flow dynamics in coastal aquifer systems through physics-based modeling validated against field observations.

Environmental Science and Pollution Research Riazi, A., Türker, U., & Rakhshandehroo, G. R.

Regression Hydrology

Peak Unit Discharge Estimation Based on Ungauged Watershed Parameters

Applied machine learning regression techniques to predict peak discharge in ungauged watersheds using morphological and hydrological parameters. Research provides data-driven methods for flood risk assessment in regions lacking direct streamflow measurements, supporting water resources planning and infrastructure design.

Environmental Earth Sciences 80, 42 Kayan, G., Riazi, A., Erten, E., & Türker, U.

2020

Deep Learning Neural Network Prediction

Accurate Tide Level Estimation: A Deep Learning Approach

Demonstrated that physics-informed deep neural networks using astronomical variables as interpretable inputs can achieve predictive accuracy matching or surpassing traditional harmonic analysis methods for tidal prediction. This research advances AI applications in coastal engineering by integrating domain knowledge with deep learning architectures.

Ocean Engineering 198, 107013 Riazi, A.

Data-Driven Modeling Particle Mechanics

Improved Drag Coefficient and Settling Velocity for Carbonate Sands

Developed empirical models for predicting drag coefficients and settling velocities of carbonate sediment particles using large experimental datasets. Research provides improved predictive equations for sediment transport modeling in tropical and subtropical coastal environments where carbonate sands dominate.

Scientific Reports 10, 9465 Riazi, A., Vila-Concejo, A., Salles, T., & Türker, U.

2019

Deep Learning Predictive Modeling

Estimating the Weight and the Failure Load of a Spaghetti Bridge: A Deep Learning Approach

Applied deep neural networks to predict structural capacity of pasta bridges based on geometric and material parameters. Research demonstrates machine learning applications in structural engineering education and demonstrates AI's capability to learn complex structure-performance relationships from limited training data.

Journal of Experimental & Theoretical Artificial Intelligence Riazi, A., Karmo, D., Shikh Ibrahim, M. A., & Amadou, S.

Hydraulics Energy Dissipation

Stepped Spillways and Energy Dissipation: A Non-Uniform Step Length Approach

Investigated energy dissipation characteristics in non-uniform stepped spillway designs through computational fluid dynamics and hydraulic modeling. Research optimizes spillway geometry for enhanced energy dissipation efficiency in dam and water infrastructure applications.

Applied Sciences 9(23), 5071 Ashoor, A., & Riazi, A.

Genetic Algorithm Optimization

Genetic Algorithm and a Double-Chromosome Implementation to the Traveling Salesman Problem

Introduced a novel double-chromosome genetic algorithm architecture that improves convergence rates and solution quality for NP-hard combinatorial optimization problems. Research demonstrates that AI-driven metaheuristic algorithms can achieve superior performance compared to conventional genetic algorithm implementations.

SN Applied Sciences 1:1397 Riazi, A.

Computational Modeling Sediment Transport

The Drag Coefficient and Settling Velocity of Natural Sediment Particles

Developed computational models for predicting drag coefficients and settling velocities of natural sediment particles across diverse grain sizes and shapes. Research provides enhanced predictive equations for sediment transport applications in rivers, estuaries, and coastal engineering projects.

Computational Particle Mechanics 6(3), 427–437 Riazi, A., & Türker, U.

2018

Genetic Algorithm Search Optimization

A Genetic Algorithm-Based Search Space Splitting Pattern and Its Application in Hydraulic and Coastal Engineering Problems

Developed a search space partitioning strategy for genetic algorithms that improves optimization efficiency in complex engineering design problems. Research demonstrates applications in hydraulic structure design and coastal engineering optimization, reducing computational cost while maintaining solution quality.

Neural Computing and Applications 30(12), 3603–3612 Riazi, A., & Türker, U.

2017

Coastal Engineering Modeling

Equilibrium Beach Profiles: Erosion and Accretion Balanced Approach

Developed analytical models for equilibrium beach profiles that balance erosion and accretion processes in coastal systems. Research provides computational methods for predicting long-term shoreline evolution and supports coastal zone management decision-making.

Water and Environment Journal 31(3), 317–323 Riazi, A., & Türker, U.

Complete Publication Record and Citation Metrics

For a comprehensive, continuously updated list of publications including citation counts, h-index metrics, and co-author network — visit Dr. Amin Riazi's Google Scholar and ResearchGate profiles.

Google Scholar → ResearchGate →

Research Collaborations and Academic Partnerships

Dr. Riazi welcomes research collaborations, peer review opportunities, and academic partnerships with universities, research laboratories, and interdisciplinary teams working on AI-driven solutions in engineering and environmental sciences.

Contact Dr. Riazi →