World's Best Scientists 2026 revealed!
Hamid Reza Pourghasemi

Hamid Reza Pourghasemi

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Environmental Sciences
Iran
2026

D-Index & Metrics

Environmental Sciences

D-Index
98
Citations
34265
World Ranking
419
National Ranking
3

Hamid Reza Pourghasemi 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 Hamid Reza Pourghasemi 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: 339 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Hamid Reza Pourghasemi 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 Hamid Reza Pourghasemi 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: 98 D-Index — 96th percentile

96% 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

  • 2026 - Research.com Environmental Sciences in Iran Leader Award
  • 2025 - Research.com Environmental Sciences in Iran Leader Award
  • 2023 - Research.com Environmental Sciences in Iran Leader Award
  • 2022 - Research.com Environmental Sciences in Iran Leader Award

Overview

Hamid Reza Pourghasemi is affiliated with Shiraz University in Iran and specializes in Environmental Science, with a focus on various subfields including Global and Planetary Change, Environmental Engineering, Management, Monitoring, Policy and Law, Ecology, and Soil Science. Their research encompasses a wide range of topics related to environmental hazards and resource management.

The main topics explored by Pourghasemi include:

  • Flood Risk Assessment and Management
  • Landslides and related hazards
  • Soil erosion and sediment transport
  • Groundwater and Watershed Analysis
  • Fire effects on ecosystems
  • Hydrology and Watershed Management Studies
  • Remote Sensing in Agriculture

Frequently publishing in scientific journals, Pourghasemi has contributed to several venues with multiple papers, notably:

  • Natural Hazards (17 publications)
  • Environmental Science and Pollution Research (12 publications)
  • Scientific Reports (10 publications)
  • Research Square (7 publications)
  • Environmental Earth Sciences (6 publications)

Some recent papers include:

  • "Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia" (2020) published in Geoscience Frontiers
  • "Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)" (2020) published in Journal of Hydrology
  • "Assessing and mapping multi-hazard risk susceptibility using a machine learning technique" (2020) published in Scientific Reports
  • "Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms" (2020) published in The Science of The Total Environment
  • "Flooding and its relationship with land cover change, population growth, and road density" (2021) published in Geoscience Frontiers

Collaborations have been established with a number of frequent coauthors, including:

  • John P. Tiefenbacher (33 joint works)
  • Narges Kariminejad (21 joint works)
  • Soheila Pouyan (19 joint works)
  • Marzieh Mokarram (18 joint works)
  • Mohsen Hosseinalizadeh (15 joint works)

Pourghasemi has authored several books published by notable publishers such as Springer Nature and GIScience and geo-environmental modelling. The titles include:

  • "Spatial Modeling in Forest Resources Management" (2020), Springer Nature
  • "Geospatial Technology for Environmental Hazards" (2021), Springer Nature
  • "Spatial Modelling of Flood Risk and Flood Hazards" (2022), GIScience and geo-environmental modelling

Best Publications

  • Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

    Hamid Reza Pourghasemi;Biswajeet Pradhan;Candan Gokceoglu

  • Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

    Unknown

  • GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

    Seyed Amir Naghibi;Hamid Reza Pourghasemi;Barnali Dixon

  • Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya

    Krishna Chandra Devkota;Amar Deep Regmi;Hamid Reza Pourghasemi;Kohki Yoshida

  • 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

  • Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran

    Hamid Reza Pourghasemi;Majid Mohammady;Biswajeet Pradhan

  • A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique

    Khabat Khosravi;Ebrahim Nohani;Edris Maroufinia;Hamid Reza Pourghasemi

  • Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

    Amar Deep Regmi;Krishna Chandra Devkota;Kohki Yoshida;Biswajeet Pradhan

  • 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

  • Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models

    Majid Mohammady;Hamid Reza Pourghasemi;Biswajeet Pradhan

  • Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms.

    Seyed Vahid Razavi Termeh;Aiding Kornejady;Hamid Reza Pourghasemi;Saskia Keesstra;Saskia Keesstra

  • Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

    H. R. Pourghasemi;H. R. Moradi;S. M. Fatemi Aghda

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

    Hamid Reza Pourghasemi;Omid Rahmati

  • Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models

    Haoyuan Hong;Hamid Reza Pourghasemi;Zohre Sadat Pourtaghi

  • GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

    A. Jaafari;A. Najafi;H. R. Pourghasemi;J. Rezaeian

  • Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

    Binh Thai Pham;Dieu Tien Bui;Hamid Reza Pourghasemi;Prakash Indra

  • Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia

    Ahmed Mohamed Youssef;Hamid Reza Pourghasemi

  • Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

    Chao Zhou;Chao Zhou;Kunlong Yin;Ying Cao;Bayes Ahmed

  • Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran

    Hamid Reza Pourghasemi;Abbas Goli Jirandeh;Biswajeet Pradhan;Chong Xu

  • Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling

    Wei Chen;Mahdi Panahi;Hamid Reza Pourghasemi

  • Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

    Hamid Reza Pourghasemi;Biswajeet Pradhan;Candan Gokceoglu;Majid Mohammadi

  • Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques

    Wei Chen;Hamid Reza Pourghasemi;Aiding Kornejady;Ning Zhang

  • Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

    Mohammad Zare;Hamid Reza Pourghasemi;Mahdi Vafakhah;Biswajeet Pradhan

  • Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran

    Seyed Amir Naghibi;Hamid Reza Pourghasemi;Zohre Sadat Pourtaghi;Ashkan Rezaei

Frequent Co-Authors

Biswajeet Pradhan
Biswajeet Pradhan University of Technology Sydney
Omid Rahmati
Omid Rahmati Agricultural Research Education And Extention Organization
Saskia Keesstra
Saskia Keesstra Wageningen University & Research
M. Santosh
M. Santosh China University of Geosciences
Christian Conoscenti
Christian Conoscenti University of Palermo
Candan Gokceoglu
Candan Gokceoglu Cappadocia University
Thomas Blaschke
Thomas Blaschke University of Salzburg
Saro Lee
Saro Lee Korea Institute of Geoscience and Mineral Resources
Dieu Tien Bui
Dieu Tien Bui University of South-Eastern Norway
Artemi Cerdà
Artemi Cerdà University of Valencia

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