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2025

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Rising Stars

D-Index
50
Citations
9944
World Ranking
321
National Ranking
16

Environmental Sciences

D-Index
54
Citations
11734
World Ranking
4014
National Ranking
12

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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