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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:

Detection of areas prone to flood risk using state-of-the-art machine learning models
Romulus Costache, Alireza Arabameri, Ismail Elkhrachy, et al.
Geomatics Natural Hazards and Risk (2021) Vol. 12, Iss. 1, pp. 1488-1507
Open Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions
Sepideh Tavakkoli Piralilou, Golzar Einali, Omid Ghorbanzadeh, et al.
Remote Sensing (2022) Vol. 14, Iss. 3, pp. 672-672
Open Access | Times Cited: 82
A GIS-Based Flood Risk Assessment Using the Decision-Making Trial and Evaluation Laboratory Approach at a Regional Scale
Eirini Efraimidou, Mike Spiliotis
Environmental Processes (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 20
Towards flood risk mapping based on multi-tiered decision making in a densely urbanized metropolitan city of Istanbul
Ömer Ekmekcioğlu, Kerim Koç, Mehmet Özger
Sustainable Cities and Society (2022) Vol. 80, pp. 103759-103759
Closed Access | Times Cited: 62
Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany
Omar Seleem, Georgy Ayzel, Arthur Costa Tomaz de Souza, et al.
Geomatics Natural Hazards and Risk (2022) Vol. 13, Iss. 1, pp. 1640-1662
Open Access | Times Cited: 54
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
Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards
Ömer Ekmekcioğlu, Kerim Koç
CATENA (2022) Vol. 216, pp. 106379-106379
Closed Access | Times Cited: 44
Living with Floods Using State-of-the-Art and Geospatial Techniques: Flood Mitigation Alternatives, Management Measures, and Policy Recommendations
Rabin Chakrabortty, Subodh Chandra Pal, Dipankar Ruidas, et al.
Water (2023) Vol. 15, Iss. 3, pp. 558-558
Open Access | Times Cited: 29
Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey
Ayşe Yavuz Özalp, Halil Akıncı, Mustafa Zeybek
Water (2023) Vol. 15, Iss. 14, pp. 2661-2661
Open Access | Times Cited: 29
A novel approach for flood hazard assessment using hybridized ensemble models and feature selection algorithms
A. Habibi, M. R. Delavar, Borzoo Nazari, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 122, pp. 103443-103443
Open Access | Times Cited: 24
Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS
Muhammad Ahsan Mukhtar, Donghui Shangguan, Yongjian Ding, et al.
Frontiers in Environmental Science (2024) Vol. 12
Open Access | Times Cited: 15
Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning
Romulus Costache, Subodh Chandra Pal, Chaitanya B. Pande, et al.
Applied Water Science (2024) Vol. 14, Iss. 4
Open Access | Times Cited: 13
Impacts of DEM type and resolution on deep learning-based flood inundation mapping
Mohammad Fereshtehpour, Mostafa Esmaeilzadeh, Reza Saleh Alipour, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1125-1145
Closed Access | Times Cited: 11
Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment
Aslam Ali Al-Omari, Nawras Shatnawi, Nadim I. Shbeeb, et al.
Civil Engineering Journal (2024) Vol. 10, Iss. 5, pp. 1423-1436
Open Access | Times Cited: 11
Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India
Chiranjit Singha, Kishore Chandra Swain, Modeste Meliho, et al.
Remote Sensing (2022) Vol. 14, Iss. 24, pp. 6229-6229
Open Access | Times Cited: 35
Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms
Saeid Janizadeh, Dongkyun Kim, Changhyun Jun, et al.
Journal of Environmental Management (2024) Vol. 366, pp. 121764-121764
Closed Access | Times Cited: 8
Extreme flash flood susceptibility mapping using a novel PCA-based model stacking approach
Amirreza Shojaeian, Hossein Shafizadeh‐Moghadam, Ahmad Sharafati, et al.
Advances in Space Research (2024) Vol. 74, Iss. 11, pp. 5371-5382
Closed Access | Times Cited: 7
Identification of topographic factors for gully erosion susceptibility and their spatial modelling using machine learning in the black soil region of Northeast China
Donghao Huang, Lin Su, Haoming Fan, et al.
Ecological Indicators (2022) Vol. 143, pp. 109376-109376
Open Access | Times Cited: 28
Assessment of flood susceptibility prediction based on optimized tree-based machine learning models
Seyed Ahmad Eslaminezhad, Mobin Eftekhari, Aliasghar Azma, et al.
Journal of Water and Climate Change (2022) Vol. 13, Iss. 6, pp. 2353-2385
Open Access | Times Cited: 27
The development of a road network flood risk detection model using optimised ensemble learning
Bilal Abu-Salih, Pornpit Wongthongtham, Kevin Coutinho, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106081-106081
Closed Access | Times Cited: 16
A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh
Md. Enayet Chowdhury, A. K. M. Saiful Islam, Rashed Uz Zzaman, et al.
Advances in Space Research (2024)
Closed Access | Times Cited: 5
Rainfall-driven machine learning models for accurate flood inundation mapping in Karachi, Pakistan
Umair Rasool, Xinan Yin, Zongxue Xu, et al.
Urban Climate (2023) Vol. 49, pp. 101573-101573
Closed Access | Times Cited: 13
Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency, Indonesia
Muhammad Asyroful Mujib, Bejo Apriyanto, Fahmi Arif Kurnianto, et al.
Geosfera Indonesia (2021) Vol. 6, Iss. 3, pp. 353-353
Open Access | Times Cited: 26
Advanced machine learning algorithms for flood susceptibility modeling — performance comparison: Red Sea, Egypt
Ahmed M. Youssef, Hamid Reza Pourghasemi, Bosy A. El‐Haddad
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 44, pp. 66768-66792
Closed Access | Times Cited: 19
Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping
Fatemeh Rezaie, Mahdi Panahi, Sayed M. Bateni, et al.
Natural Hazards (2022) Vol. 114, Iss. 2, pp. 1247-1283
Closed Access | Times Cited: 19
XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images
J.E. Sanderson, Naruephorn Tengtrairat, Wai Lok Woo, et al.
International Journal of Remote Sensing (2023) Vol. 44, Iss. 24, pp. 7755-7789
Closed Access | Times Cited: 11
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