World's Best Scientists 2026 revealed!
Ataollah Shirzadi

Ataollah Shirzadi

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Environmental Sciences
Iran
2026

D-Index & Metrics

Environmental Sciences

D-Index
65
Citations
12626
World Ranking
2253
National Ranking
7

Research.com Recognitions

  • 2026 - Research.com Environmental Sciences in Iran Leader Award
  • 2025 - Research.com Environmental Sciences in Iran Leader Award

Overview

Ataollah Shirzadi is affiliated with the University of Kurdistan in Iran and has a research focus primarily within the field of Environmental Science. Their work spans multiple subfields, including Global and Planetary Change, Environmental Engineering, Management, Monitoring, Policy and Law, Water Science and Technology, and Soil Science.

The scientist's research centers on topics related to flood and landslide risk assessment, hydrological forecasting using artificial intelligence, soil erosion and sediment transport, fire effects on ecosystems, hydrology and drought analysis, and watershed management studies.

Key recent papers authored by Ataollah Shirzadi include:

  • Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning (2020, The Science of The Total Environment)
  • Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm (2020, Geoscience Frontiers)
  • Deep learning neural networks for spatially explicit prediction of flash flood probability (2020, Geoscience Frontiers)
  • Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling? (2020, Journal of Hydrology)
  • GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models (2020, Applied Sciences)

Their frequent co-authors include Himan Shahabi, John J. Clague, Binh Thai Pham, Dieu Tien Bui, and Wei Chen.

Shirzadi's work has been published predominantly in venues such as Geoscience Frontiers and Environmental Earth Sciences, with additional contributions appearing in The Science of The Total Environment, Journal of Hydrology, and Applied Sciences.

The research mainly involves applying deep learning and other advanced computational methods to improve prediction and susceptibility mapping for natural hazards like floods and landslides. This suggests a significant focus on integrating machine learning approaches with environmental science challenges.

Best Publications

  • A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

    Khabat Khosravi;Binh Thai Pham;Kamran Chapi;Ataollah Shirzadi

  • A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

    Khabat Khosravi;Himan Shahabi;Binh Thai Pham;Jan Adamowski

  • A novel hybrid artificial intelligence approach for flood susceptibility assessment

    Kamran Chapi;Vijay P. Singh;Ataollah Shirzadi;Himan Shahabi

  • Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.

    Haoyuan Hong;Haoyuan Hong;Mahdi Panahi;Ataollah Shirzadi;Tianwu Ma;Tianwu Ma

  • Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

    Himan Shahabi;Ataollah Shirzadi;Kayvan Ghaderi;Ebrahim Omidvar

  • Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.

    Hossein Shafizadeh-Moghadam;Roozbeh Valavi;Himan Shahabi;Kamran Chapi

  • Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

    Binh Thai Pham;Indra Prakash;Sushant K. Singh;Ataollah Shirzadi

  • Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning

    Jie Dou;Jie Dou;Ali P. Yunus;Abdelaziz Merghadi;Ataollah Shirzadi

  • Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA)

    M. Ahmadlou;M. Karimi;S. Alizadeh;A. Shirzadi

  • Shallow landslide susceptibility assessment using a novel hybrid intelligence approach

    Ataollah Shirzadi;Dieu Tien Bui;Binh Thai Pham;Karim Solaimani

  • Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

    Viet-Ha Nhu;Ataollah Shirzadi;Himan Shahabi;Sushant K. Singh

  • A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

    Wei Chen;Ataollah Shirzadi;Himan Shahabi;Baharin Bin Ahmad

  • Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

    Qingfeng He;Himan Shahabi;Ataollah Shirzadi;Shaojun Li

  • Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.

    Yi Wang;Haoyuan Hong;Haoyuan Hong;Wei Chen;Shaojun Li

  • New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling

    Dieu Tien Bui;Khabat Khosravi;Shaojun Li;Himan Shahabi

  • Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

    Dieu Tien Bui;Khabat Khosravi;Himan Shahabi;Prasad Daggupati

  • A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India

    Binh Thai Pham;Ataollah Shirzadi;Dieu Tien Bui;Indra Prakash

  • Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach

    Shaghayegh Miraki;Sasan Hedayati Zanganeh;Kamran Chapi;Vijay P. Singh

  • Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping.

    Ataollah Shirzadi;Karim Soliamani;Mahmood Habibnejhad;Ataollah Kavian

  • Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling

    Wei Chen;Wei Chen;Himan Shahabi;Ataollah Shirzadi;Haoyuan Hong;Haoyuan Hong

  • Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

    Binh Thai Pham;Ataollah Shirzadi;Himan Shahabi;Ebrahim Omidvar

  • Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

    Dieu Tien Bui;Mahdi Panahi;Himan Shahabi;Vijay P. Singh

  • Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in Cameron Highlands, Malaysia

    Dieu Tien Bui;Himan Shahabi;Ataollah Shirzadi;Kamran Chapi

Frequent Co-Authors

Himan Shahabi
Himan Shahabi University of Kurdistan
Baharin Bin Ahmad
Baharin Bin Ahmad University of Technology Malaysia
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Biswajeet Pradhan
Biswajeet Pradhan University of Technology Sydney
Haoyuan Hong
Haoyuan Hong Nanjing University of Information Science and Technology
Nadhir Al-Ansari
Nadhir Al-Ansari Luleå University of Technology
John J. Clague
John J. Clague Simon Fraser University
Saro Lee
Saro Lee Korea Institute of Geoscience and Mineral Resources
Marten Geertsema
Marten Geertsema University of Northern British Columbia
Indra Prakash
Indra Prakash Geological Survey of India

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