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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Development of an automated GIS tool for reproducing the HAND terrain model
Omid Rahmati, Aiding Kornejady, Mahmood Samadi, et al.
Environmental Modelling & Software (2018) Vol. 102, pp. 1-12
Closed Access | Times Cited: 65

Showing 1-25 of 65 citing articles:

Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
Esmaeel Dodangeh, Bahram Choubin, Ahmad Najafi Eigdir, et al.
The Science of The Total Environment (2019) Vol. 705, pp. 135983-135983
Closed Access | Times Cited: 223
Comparison among influencing factor, frequency ratio, and analytical hierarchy process techniques for groundwater potential zonation in Vaitarna basin, Maharashtra, India
Sumit Das
Groundwater for Sustainable Development (2019) Vol. 8, pp. 617-629
Closed Access | Times Cited: 179
Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas
Maher Ibrahim Sameen, Biswajeet Pradhan, Saro Lee
Natural Resources Research (2018) Vol. 28, Iss. 3, pp. 757-775
Closed Access | Times Cited: 113
Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors
Seyed Amir Naghibi, Hossein Hashemi, Ronny Berndtsson, et al.
Journal of Hydrology (2020) Vol. 589, pp. 125197-125197
Closed Access | Times Cited: 112
Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater
Alireza Motevalli, Seyed Amir Naghibi, Hossein Hashemi, et al.
Journal of Cleaner Production (2019) Vol. 228, pp. 1248-1263
Closed Access | Times Cited: 108
Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, et al.
Journal of Hydrology Regional Studies (2021) Vol. 36, pp. 100848-100848
Open Access | Times Cited: 97
SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors
Omid Rahmati, Mahmood Samadi, Himan Shahabi, et al.
Geoscience Frontiers (2019) Vol. 10, Iss. 6, pp. 2167-2175
Open Access | Times Cited: 89
Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models
Viet‐Ha Nhu, Omid Rahmati, Fatemeh Falah, et al.
Water (2020) Vol. 12, Iss. 4, pp. 985-985
Open Access | Times Cited: 71
A GIS-based tool for probabilistic physical modelling and prediction of landslides: GIS-FORM landslide susceptibility analysis in seismic areas
Jian Ji, Hongzhi Cui, Tong Zhang, et al.
Landslides (2022) Vol. 19, Iss. 9, pp. 2213-2231
Closed Access | Times Cited: 55
Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method
Irene Garousi‐Nejad, David G. Tarboton, Mahyar Aboutalebi, et al.
Water Resources Research (2019) Vol. 55, Iss. 10, pp. 7983-8009
Closed Access | Times Cited: 68
A web application for hydrogeomorphic flood hazard mapping
Ricardo Tavares da Costa, Salvatore Manfreda, Valerio Luzzi, et al.
Environmental Modelling & Software (2019) Vol. 118, pp. 172-186
Open Access | Times Cited: 56
Identifying sources of dust aerosol using a new framework based on remote sensing and modelling
Omid Rahmati, Farnoush Mohammadi, Seid Saeid Ghiasi, et al.
The Science of The Total Environment (2020) Vol. 737, pp. 139508-139508
Closed Access | Times Cited: 54
Efficiency of Geospatial Technology and Multi-Criteria Decision Analysis for Groundwater Potential Mapping in a Semi-Arid Region
Ahmed M. Masoud, Quoc Bao Pham, Ahmed K. Alezabawy, et al.
Water (2022) Vol. 14, Iss. 6, pp. 882-882
Open Access | Times Cited: 38
Hybrid approach for flood susceptibility assessment in a flood-prone mountainous catchment in China
Lei Fang, Jinliang Huang, Juntao Cai, et al.
Journal of Hydrology (2022) Vol. 612, pp. 128091-128091
Closed Access | Times Cited: 36
Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area
M. Shawky, Quazi K. Hassan
Remote Sensing (2023) Vol. 15, Iss. 10, pp. 2561-2561
Open Access | Times Cited: 19
Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models
Navin Tony Thalakkottukara, Jobin Thomas, Melanie Kueber Watkins, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 3, pp. 1907-1921
Open Access | Times Cited: 7
GIS preprocessing for rapid initialization of HEC-HMS hydrological basin models using web-based data services
Cyndi V. Castro, David R. Maidment
Environmental Modelling & Software (2020) Vol. 130, pp. 104732-104732
Open Access | Times Cited: 49
Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran
Vahedberdi Sheikh, Aiding Kornejady, Majid Ownegh
Natural Hazards (2019) Vol. 96, Iss. 3, pp. 1335-1365
Closed Access | Times Cited: 45
Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms
Yang Li, Wei Chen, Fatemeh Rezaie, et al.
Geocarto International (2021) Vol. 37, Iss. 17, pp. 5150-5173
Closed Access | Times Cited: 39
Flood hazard mapping using GIS-based statistical model in vulnerable riparian regions of sub-tropical environment
Anitabha Ghosh, Uday Chatterjee, Subodh Chandra Pal, et al.
Geocarto International (2023) Vol. 38, Iss. 1
Open Access | Times Cited: 15
Groundwater spring potential mapping: Assessment the contribution of hydrogeological factors
Rui Zhao, Chenchen Fan, Alireza Arabameri, et al.
Advances in Space Research (2024) Vol. 74, Iss. 1, pp. 48-64
Closed Access | Times Cited: 6
Evaluating the reliability of synthetic rating curves for continental scale flood mapping
Ankit Ghanghas, Sayan Dey, Venkatesh Merwade
Journal of Hydrology (2022) Vol. 606, pp. 127470-127470
Closed Access | Times Cited: 21
Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA
Mehdi Sadıghı, Baharak Motamedvaziri, Hassan Ahmadi, et al.
Environmental Earth Sciences (2020) Vol. 79, Iss. 24
Closed Access | Times Cited: 29
Evaluation of Soil Water Index of distributed Tank Model in a small basin with field data
Sofia Melo Vasconcellos, Masato Kobiyama, Aline de Almeida Mota
Journal of South American Earth Sciences (2025), pp. 105416-105416
Closed Access
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain
Ali Rostami, Mohammad Taghi Sattari, Halit Apaydın, et al.
Geosciences (2025) Vol. 15, Iss. 3, pp. 110-110
Open Access
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