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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:

Sediment load prediction in Johor river: deep learning versus machine learning models
Sarmad Dashti Latif, Kai Lun Chong, Ali Najah Ahmed, et al.
Applied Water Science (2023) Vol. 13, Iss. 3
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Deep learning for water quality
Wei Zhi, Alison P. Appling, Heather E. Golden, et al.
Nature Water (2024) Vol. 2, Iss. 3, pp. 228-241
Closed Access | Times Cited: 69
Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting
Türker Tuğrul, Mehmet Ali Hınıs, Sertaç Oruç
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access | Times Cited: 3
Suspended sediment load modeling using Hydro-Climate variables and Machine learning
Shahab Aldin Shojaeezadeh, Malik Al-Wardy, Mohammad Reza Nikoo
Journal of Hydrology (2024) Vol. 633, pp. 130948-130948
Closed Access | Times Cited: 10
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources
Zhong-kai Feng, J. Zhang, Wen-jing Niu
Applied Soft Computing (2024), pp. 112352-112352
Closed Access | Times Cited: 9
Enhancing sediment transport predictions through machine learning-based multi-scenario regression models
Mohammad Abdullah Almubaidin, Sarmad Dashti Latif, Kalaiarasan Balan, et al.
Results in Engineering (2023) Vol. 20, pp. 101585-101585
Open Access | Times Cited: 19
A newly developed multi-objective evolutionary paradigm for predicting suspended sediment load
Siyamak Doroudi, Ahmad Sharafati
Journal of Hydrology (2024) Vol. 634, pp. 131090-131090
Closed Access | Times Cited: 8
Optimizing sediment transport models by using the Monte Carlo simulation and deep neural network (DNN): A case study of the Riba-Roja reservoir
Danial Dehghan-Souraki, David López-Gómez, Ernest Bladé, et al.
Environmental Modelling & Software (2024) Vol. 175, pp. 105979-105979
Open Access | Times Cited: 7
Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm
Mohammed Majeed Hameed, Siti Fatin Mohd Razali, Wan Hanna Melini Wan Mohtar, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 12, pp. 4963-4989
Closed Access | Times Cited: 16
Integrating fluid–solid coupling domain knowledge with deep learning models: An automatic and interpretable diagnostic system for the silting disease of drainage pipelines
Hongyuan Fang, Zhaoyang Zhang, Danyang Di, et al.
Tunnelling and Underground Space Technology (2023) Vol. 142, pp. 105386-105386
Closed Access | Times Cited: 15
Machine learning application in modelling marine and coastal phenomena: a critical review
Ali Pourzangbar, Mahdi Jalali, Maurizio Brocchini
Frontiers in Environmental Engineering (2023) Vol. 2
Open Access | Times Cited: 15
Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence
Nawin Raj, Sarah Pasfield‐Neofitou
Remote Sensing (2024) Vol. 16, Iss. 3, pp. 551-551
Open Access | Times Cited: 6
Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations
Yalan Song, Piyaphat Chaemchuen, Farshid Rahmani, et al.
Journal of Hydrology (2024) Vol. 639, pp. 131573-131573
Closed Access | Times Cited: 6
Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment
Dong Hoon Lee, Sang-Il Lee, Joo‐Hyon Kang
The Science of The Total Environment (2024) Vol. 948, pp. 174755-174755
Closed Access | Times Cited: 5
Developing ensemble models for estimating sediment loads for different times scales
Majid Niazkar, Mohammad Zakwan
Environment Development and Sustainability (2023) Vol. 26, Iss. 6, pp. 15557-15575
Closed Access | Times Cited: 13
Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria
Mohammed Achite, Okan Mert Katipoğlu, Nehal Elshaboury, et al.
Environmental Earth Sciences (2025) Vol. 84, Iss. 2
Closed Access
Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran
Mohammad Ali Zangeneh Asadi, Leila Goli Mokhtari, Rahman Zandi, et al.
Applied Water Science (2025) Vol. 15, Iss. 3
Open Access
Examining the complexity of discharge-suspended sediment behaviour of a tropical monsoon-dominated river, India
Balai Chandra Das, Aznarul Islam, Sadik Mahammad, et al.
Journal of Hydrology Regional Studies (2025) Vol. 59, pp. 102335-102335
Closed Access
A Novel LSTM Approach for Reliable and Real-Time Flood Prediction in Complex Watersheds
Wassima Moutaouakil, Soufiane Hamida, Oussama ElGannour, et al.
Research Square (Research Square) (2025)
Closed Access
Research on interpretable graph neural network agent model for siltation diagnosis in Urban-Scale sewer systems
Junhao Wu, Ling Ma, Xi Chen, et al.
Tunnelling and Underground Space Technology (2025) Vol. 162, pp. 106666-106666
Closed Access
Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport
Soumya Kundu, Somil Swarnkar, Akshay Agarwal
Environmental Monitoring and Assessment (2025) Vol. 197, Iss. 5
Closed Access
Comparative Study of Suspended Sediment Load Prediction Models Based on Artificial Intelligence Methods
Cynthia Borkai Boye, Paul Boye, Yao Yevenyo Ziggah
Artificial Intelligence and Applications (2023) Vol. 2, Iss. 2, pp. 141-154
Open Access | Times Cited: 9
Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?
Khabat Khosravi, Aitazaz A. Farooque, Sayed M. Bateni, et al.
Engineering Applications of Computational Fluid Mechanics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 3
Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability
Ali El Bilali, Youssef Brouziyne, Oumaima Attar, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 34, pp. 47237-47257
Closed Access | Times Cited: 3
Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River
Okan Mert Katipoğlu, Veysi Kartal, Chaitanya Baliram Pande
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 10, pp. 3907-3927
Open Access | Times Cited: 3
Mode decomposition based large margin distribution machines for sediment load prediction
Barenya Bikash Hazarika, Deepak Gupta
Expert Systems with Applications (2023) Vol. 232, pp. 120844-120844
Closed Access | Times Cited: 7
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