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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.

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Showing 1-25 of 38 citing articles:

A review of hybrid deep learning applications for streamflow forecasting
Kin‐Wang Ng, Yuk Feng Huang, Chai Hoon Koo, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130141-130141
Closed Access | Times Cited: 72
Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river network
Jannatul Ferdous Ruma, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan, et al.
Results in Engineering (2023) Vol. 17, pp. 100951-100951
Open Access | Times Cited: 53
Research on Parameter Identification and Fault Prediction Method of Hydraulic System in Intelligent Sensing Agriculture
Wenbo Liu, Zheng Jia-heng, Guangyu Shi, et al.
Measurement Sensors (2025), pp. 101813-101813
Open Access | Times Cited: 1
An Investigation on Hybrid Particle Swarm Optimization Algorithms for Parameter Optimization of PV Cells
Abha Singh, Abhishek Sharma, Shailendra Rajput, et al.
Electronics (2022) Vol. 11, Iss. 6, pp. 909-909
Open Access | Times Cited: 45
Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model
Sonali Swagatika, Jagadish Chandra Paul, Bibhuti Bhusan Sahoo, et al.
Journal of Water and Climate Change (2023) Vol. 15, Iss. 1, pp. 139-156
Open Access | Times Cited: 23
Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow
Kegang Wang, Shahab S. Band, Rasoul Ameri, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1833-1848
Open Access | Times Cited: 35
Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model
Fabio Di Nunno, Francesco Granata, Quoc Bao Pham, et al.
Sustainability (2022) Vol. 14, Iss. 5, pp. 2663-2663
Open Access | Times Cited: 28
An evolutionary hybrid method based on particle swarm optimization algorithm and extreme gradient boosting for short-term streamflow forecasting
Hüseyin Çağan Kılınç, Bülent Haznedar, Furkan Ozkan, et al.
Acta Geophysica (2024) Vol. 72, Iss. 5, pp. 3661-3681
Closed Access | Times Cited: 5
Multi-Variables-Driven Model Based on Random Forest and Gaussian Process Regression for Monthly Streamflow Forecasting
Na Sun, Shuai Zhang, Peng Tian, et al.
Water (2022) Vol. 14, Iss. 11, pp. 1828-1828
Open Access | Times Cited: 26
Streamflow forecasting for the Hunza river basin using ANN, RNN, and ANFIS models
Mehran Khan, Afed Ullah Khan, Jehanzeb Khan, et al.
Water Practice & Technology (2023) Vol. 18, Iss. 5, pp. 981-993
Open Access | Times Cited: 14
Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization
Hüseyin Çağan Kılınç, Iman Ahmadianfar, Vahdettin Demir, et al.
Water Resources Management (2023) Vol. 37, Iss. 9, pp. 3699-3714
Open Access | Times Cited: 14
Using Adaptive Chaotic Grey Wolf Optimization for the daily streamflow prediction
Jing Liang, Yukun Du, Yi‐Peng Xu, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121113-121113
Closed Access | Times Cited: 13
Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective
Sarah Jasim Mohammed, Salah L. Zubaidi, Sandra Ortega‐Martorell, et al.
Cogent Engineering (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 20
Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model
Xiazi Yang, Balati Maihemuti, Zibibula Simayi, et al.
Water (2022) Vol. 14, Iss. 13, pp. 2018-2018
Open Access | Times Cited: 19
Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow
Baydaa Abdul Kareem, Salah L. Zubaidi, Nadhir Al‐Ansari, et al.
Computer Modeling in Engineering & Sciences (2023) Vol. 138, Iss. 1, pp. 1-41
Open Access | Times Cited: 11
Short-term streamflow modeling using data-intelligence evolutionary machine learning models
Alfeu D. Martinho, Henrique S. Hippert, Leonardo Goliatt
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 10
An approach on the estimation and temporal interaction of runoff: the band similarity method
Volkan Yılmaz, Cihangir Köyceğiz, Meral Büyükyıldız
Journal of Water and Climate Change (2024)
Open Access | Times Cited: 3
A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series
Bülent Haznedar, Hüseyin Çağan Kılınç
Water Resources Management (2022) Vol. 36, Iss. 12, pp. 4819-4842
Closed Access | Times Cited: 15
Performance evaluation of various hydrological models with respect to hydrological responses under climate change scenario: a review
Yilak Taye Bihon, Tarun Kumar Lohani, Abebe Temesgen Ayalew, et al.
Cogent Engineering (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 2
Predicting Daily Streamflow in a Cold Climate Using a Novel Data Mining Technique: Radial M5 Model Tree
Özgür Kişi, Salim Heddam, Behrooz Keshtegar, et al.
Water (2022) Vol. 14, Iss. 9, pp. 1449-1449
Open Access | Times Cited: 12
Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective
Mehran Khan, Afed Ullah Khan, Sunaid Khan, et al.
Water Science & Technology (2023) Vol. 88, Iss. 9, pp. 2309-2331
Open Access | Times Cited: 7
Evaluation of a novel hybrid lion swarm optimization – AdaBoostRegressor model for forecasting monthly precipitation
Sumantha Esther Priestly, Kumudha Raimond, Yafit Cohen, et al.
Sustainable Computing Informatics and Systems (2023) Vol. 39, pp. 100884-100884
Closed Access | Times Cited: 6
Comparative analysis of deep learning and classical time series methods to forecast natural gas demand during COVID-19 pandemic
Zeynep Ceylan
Energy Sources Part B Economics Planning and Policy (2023) Vol. 18, Iss. 1
Closed Access | Times Cited: 5
Evaluation of precipitation forecasts for five-day streamflow forecasting in Narmada River basin
Ankit Singh, Soubhik Mondal, Nibedita Samal, et al.
Hydrological Sciences Journal (2022) Vol. 68, Iss. 1, pp. 161-179
Closed Access | Times Cited: 9
Supplementing Missing Data Using the Drainage-Area Ratio Method and Evaluating the Streamflow Drought Index with the Corrected Data Set
Evren Turhan, Serin Değerli Şimşek
Water (2023) Vol. 15, Iss. 3, pp. 425-425
Open Access | Times Cited: 4
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