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

Ataollah Shirzadi publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where Ataollah Shirzadi sits on this spectrum.

41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 283 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 25 scientists 501–510 publications: 17 scientists 511–520 publications: 18 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–686 publications: 3 scientists 687+ publications: 100 scientists
41 publications 687+

This scientist: 91 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 687 publications or more.

Ataollah Shirzadi D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where Ataollah Shirzadi sits on this spectrum.

30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 199 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 19 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125+ D-Index: 99 scientists
30 D-Index 125+

This scientist: 65 D-Index — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 125 D-Index or more.

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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