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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:

Application of the GIS-Based Probabilistic Models for Mapping the Flood Susceptibility in Bansloi Sub-basin of Ganga-Bhagirathi River and Their Comparison
Gopal Chandra Paul, Sunil Saha, Tusar Kanti Hembram
Remote Sensing in Earth Systems Sciences (2019) Vol. 2, Iss. 2-3, pp. 120-146
Closed Access | Times Cited: 113

Showing 1-25 of 113 citing articles:

Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101075-101075
Open Access | Times Cited: 427
GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan
Kashif Ullah, Jiquan Zhang
PLoS ONE (2020) Vol. 15, Iss. 3, pp. e0229153-e0229153
Open Access | Times Cited: 216
Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms
Swapan Talukdar, Bonosri Ghose, Shahfahad, et al.
Stochastic Environmental Research and Risk Assessment (2020) Vol. 34, Iss. 12, pp. 2277-2300
Closed Access | Times Cited: 184
Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran
Alireza Arabameri, Sunil Saha, Jagabandhu Roy, et al.
Remote Sensing (2020) Vol. 12, Iss. 3, pp. 475-475
Open Access | Times Cited: 182
Flooding and its relationship with land cover change, population growth, and road density
Mahfuzur Rahman, Chen Ningsheng, Golam Iftekhar Mahmud, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 6, pp. 101224-101224
Open Access | Times Cited: 145
A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India
Dipankar Ruidas, Rabin Chakrabortty, Abu Reza Md. Towfiqul Islam, et al.
Environmental Earth Sciences (2022) Vol. 81, Iss. 5
Closed Access | Times Cited: 94
Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression
Soroosh Mehravar, Seyed Vahid Razavi-Termeh, Armin Moghimi, et al.
Journal of Hydrology (2023) Vol. 617, pp. 129100-129100
Open Access | Times Cited: 78
Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Myoung-Bae Seo, et al.
The Science of The Total Environment (2023) Vol. 873, pp. 162285-162285
Closed Access | Times Cited: 49
Flood susceptibility mapping contributes to disaster risk reduction: A case study in Sindh, Pakistan
Shoukat Ali Shah, Songtao Ai
International Journal of Disaster Risk Reduction (2024) Vol. 108, pp. 104503-104503
Open Access | Times Cited: 23
Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the AdaBoost-Decision Tree algorithm, in Mazandaran, Iran
Maryam Jahanbani, Mohammad H. Vahidnia, Hossein Aghamohammadi, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1433-1457
Closed Access | Times Cited: 18
Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques
Alireza Arabameri, Sunil Saha, Wei Chen, et al.
Journal of Hydrology (2020) Vol. 587, pp. 125007-125007
Closed Access | Times Cited: 139
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Narges Kariminejad, et al.
Geoscience Frontiers (2020) Vol. 11, Iss. 6, pp. 2207-2219
Open Access | Times Cited: 115
Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh
Mahfuzur Rahman, Ningsheng Chen, Ahmed Elbeltagi, et al.
Journal of Environmental Management (2021) Vol. 295, pp. 113086-113086
Closed Access | Times Cited: 88
GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran
Hadi Allafta, Christian Opp
Geomatics Natural Hazards and Risk (2021) Vol. 12, Iss. 1, pp. 2087-2116
Open Access | Times Cited: 83
Prediction of highly flood prone areas by GIS based heuristic and statistical model in a monsoon dominated region of Bengal Basin
Sadhan Malik, Subodh Chandra Pal, Indrajit Chowdhuri, et al.
Remote Sensing Applications Society and Environment (2020) Vol. 19, pp. 100343-100343
Closed Access | Times Cited: 79
Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India
Anik Saha, Sunil Saha
Remote Sensing Applications Society and Environment (2020) Vol. 19, pp. 100323-100323
Closed Access | Times Cited: 72
Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin
Supriya Ghosh, Soumik Saha, Biswajit Bera
Natural Hazards Research (2022) Vol. 2, Iss. 4, pp. 363-374
Open Access | Times Cited: 65
How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region
Tamal Kanti Saha, Swades Pal, Swapan Talukdar, et al.
Journal of Environmental Management (2021) Vol. 297, pp. 113344-113344
Open Access | Times Cited: 59
Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt
Ahmed M. Youssef, Biswajeet Pradhan, Abhirup Dikshit, et al.
Geocarto International (2022) Vol. 37, Iss. 26, pp. 11088-11115
Closed Access | Times Cited: 52
Application of GIS and Machine Learning to Predict Flood Areas in Nigeria
Eseosa Halima Ighile, Hiroaki Shirakawa, Hiroki Tanikawa
Sustainability (2022) Vol. 14, Iss. 9, pp. 5039-5039
Open Access | Times Cited: 47
Flash Flood Hazard Mapping Using Remote Sensing and GIS Techniques in Southwestern Saudi Arabia
Saad S. Alarifi, Mohamed Abdelkareem, Fathy Abdalla, et al.
Sustainability (2022) Vol. 14, Iss. 21, pp. 14145-14145
Open Access | Times Cited: 46
Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques
Mojgan Bordbar, Hossein Aghamohammadi, Hamid Reza Pourghasemi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 42
GIS ​– ​based flood susceptibility mapping using frequency ratio and information value models in upper Abay river basin, ​Ethiopia
Abinet Addis
Natural Hazards Research (2023) Vol. 3, Iss. 2, pp. 247-256
Open Access | Times Cited: 34
Mapping of Flood-Prone Areas Utilizing GIS Techniques and Remote Sensing: A Case Study of Duhok, Kurdistan Region of Iraq
Aumed Rahman M Amen, Andam Mustafa, Dalshad Ahmed Kareem, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1102-1102
Open Access | Times Cited: 34
GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan
Zahid Ur Rahman, Waheed Ullah, Shibiao Bai, et al.
Frontiers in Environmental Science (2023) Vol. 11
Open Access | Times Cited: 32
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