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Environmental Sciences

D-Index
54
Citations
11734
World Ranking
4017
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12

Omid Rahmati 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 Omid Rahmati 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: 282 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: 26 scientists 501–510 publications: 17 scientists 511–520 publications: 19 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 681–689 publications: 4 scientists 690+ publications: 100 scientists
41 publications 690+

This scientist: 96 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 690 publications or more.

Omid Rahmati 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 Omid Rahmati 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: 198 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: 20 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: 9 scientists 126+ D-Index: 92 scientists
30 D-Index 126+

This scientist: 54 D-Index — 59th percentile

59% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 126 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Omid Rahmati is affiliated with the Agricultural Research Education And Extention Organization in Iran. Their research primarily focuses on environmental science, with specific emphasis on areas related to flood risk assessment and management, soil erosion, sediment transport, and hydrology.

The main fields of study for Rahmati include:

  • Environmental Science

More detailed subfields of their research encompass:

  • Global and Planetary Change
  • Soil Science
  • Water Science and Technology
  • Environmental Engineering
  • Ecology

Key topics they have worked on involve:

  • Flood Risk Assessment and Management
  • Soil erosion and sediment transport
  • Hydrology and Watershed Management Studies
  • Hydrology and Sediment Transport Processes
  • Groundwater and Watershed Analysis
  • Landslides and related hazards
  • Hydrology and Drought Analysis

Omid Rahmati has coauthored extensively with several researchers, indicating collaborative contributions in their field. Frequent coauthors include:

  • Zahra Kalantari
  • Mahdi Panahi
  • Carla Ferreira
  • Dieu Tien Bui
  • Saro Lee

Their work has been published repeatedly in certain journals reflecting their research focus. Frequent publication venues are:

  • Journal of Hydrology
  • Geocarto International
  • The Science of The Total Environment
  • Remote Sensing
  • Scientific Reports

Recent representative papers authored or coauthored by Rahmati include:

  • Development of novel hybridized models for urban flood susceptibility mapping, 2020, Scientific Reports
  • Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory, 2020, Journal of Hydrology
  • 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, 2020, Remote Sensing
  • Urban flood modeling using deep-learning approaches in Seoul, South Korea, 2021, Journal of Hydrology
  • Deep learning neural networks for spatially explicit prediction of flash flood probability, 2020, Geoscience Frontiers

Best Publications

  • Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS

    Omid Rahmati;Aliakbar Nazari Samani;Mohamad Mahdavi;Hamid Reza Pourghasemi

  • Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran

    Omid Rahmati;Hamid Reza Pourghasemi;Assefa M. Melesse

  • Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran

    Omid Rahmati;Hamid Reza Pourghasemi;Hossein Zeinivand

  • Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS

    Yousef Razandi;Hamid Reza Pourghasemi;Najmeh Samani Neisani;Omid Rahmati

  • Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

    Omid Rahmati;Hossein Zeinivand;Mosa Besharat

  • Prediction of the landslide susceptibility: Which algorithm, which precision?

    Hamid Reza Pourghasemi;Omid Rahmati

  • Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques

    Hamid Darabi;Bahram Choubin;Omid Rahmati;Ali Torabi Haghighi

  • 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

  • Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory

    Thimmaiah Gudiyangada Nachappa;Sepideh Tavakkoli Piralilou;Khalil Gholamnia;Omid Ghorbanzadeh

  • A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

    Farzaneh Sajedi-Hosseini;Arash Malekian;Bahram Choubin;Omid Rahmati

  • Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion

    Omid Rahmati;Nasser Tahmasebipour;Ali Haghizadeh;Hamid Reza Pourghasemi

  • River suspended sediment modelling using the CART model: A comparative study of machine learning techniques.

    Bahram Choubin;Hamid Darabi;Omid Rahmati;Farzaneh Sajedi-Hosseini

  • Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison

    Omid Rahmati;Ali Haghizadeh;Hamid Reza Pourghasemi;Farhad Noormohamadi

  • Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods.

    Omid Rahmati;Bahram Choubin;Abolhasan Fathabadi;Frederic Coulon

  • Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework.

    Omid Rahmati;Naser Tahmasebipour;Ali Haghizadeh;Hamid Reza Pourghasemi

  • Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

    Ali Azareh;Omid Rahmati;Elham Rafiei-Sardooi;Joel B. Sankey

  • Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models

    Samira Ghorbani Nejad;Fatemeh Falah;Mania Daneshfar;Ali Haghizadeh

  • Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing

    Naser Tahmassebipoor;Omid Rahmati;Farhad Noormohamadi;Saro Lee

  • Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia

    Omid Rahmati;Fatemeh Falah;Kavina Shaanu Dayal;Ravinesh C. Deo

  • Urban flood modeling using deep-learning approaches in Seoul, South Korea

    Xinxiang Lei;Wei Chen;Wei Chen;Mahdi Panahi;Fatemeh Falah

  • Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models

    Safura Siahkamari;Ali Haghizadeh;Hossein Zeinivand;Naser Tahmasebipour

  • Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models

    Omid Rahmati;Hamid Reza Pourghasemi

  • Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches

    Omid Rahmati;Seyed Amir Naghibi;Himan Shahabi;Dieu Tien Bui

Frequent Co-Authors

Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Biswajeet Pradhan
Biswajeet Pradhan University of Technology Sydney
Hamid Reza Pourghasemi
Hamid Reza Pourghasemi Shiraz University
Zahra Kalantari
Zahra Kalantari Royal Institute of Technology
Assefa M. Melesse
Assefa M. Melesse Florida International University
Himan Shahabi
Himan Shahabi University of Kurdistan
Ravinesh C. Deo
Ravinesh C. Deo University of Southern Queensland
Ataollah Shirzadi
Ataollah Shirzadi University of Kurdistan
Saro Lee
Saro Lee Korea Institute of Geoscience and Mineral Resources
Saskia Keesstra
Saskia Keesstra Wageningen University & Research

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