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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:

Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting
Soukayna Mouatadid, Jan Adamowski, Mukesh Tiwari, et al.
Agricultural Water Management (2019) Vol. 219, pp. 72-85
Closed Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

Long lead-time daily and monthly streamflow forecasting using machine learning methods
Meiling Cheng, F. Fang, Tsuyoshi Kinouchi, et al.
Journal of Hydrology (2020) Vol. 590, pp. 125376-125376
Closed Access | Times Cited: 227
Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting
Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski
Journal of Hydrology (2021) Vol. 598, pp. 126196-126196
Closed Access | Times Cited: 154
Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms
A. T. M. Sakiur Rahman, Takahiro Hosono, John Quilty, et al.
Advances in Water Resources (2020) Vol. 141, pp. 103595-103595
Closed Access | Times Cited: 153
Applications of machine learning to water resources management: A review of present status and future opportunities
Ashraf Ahmed, Sakina Sayed, Antoifi Abdoulhalik, et al.
Journal of Cleaner Production (2024) Vol. 441, pp. 140715-140715
Open Access | Times Cited: 50
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
On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction
Alireza Ghaemi, Mohammad Rezaie-Balf, Jan Adamowski, et al.
Agricultural and Forest Meteorology (2019) Vol. 278, pp. 107647-107647
Closed Access | Times Cited: 100
Smart applications and digital technologies in viticulture: A review
Javier Tardáguila, Manfred Stoll, Salvador Gutiérrez, et al.
Smart Agricultural Technology (2021) Vol. 1, pp. 100005-100005
Open Access | Times Cited: 85
High temporal resolution urban flood prediction using attention-based LSTM models
Lin Zhang, Huapeng Qin, Junqi Mao, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129499-129499
Closed Access | Times Cited: 31
Real-time rainfall and runoff prediction by integrating BC-MODWT and automatically-tuned DNNs: Comparing different deep learning models
Amirmasoud Amini, Mehri Dolatshahi, Reza Kerachian
Journal of Hydrology (2024) Vol. 631, pp. 130804-130804
Closed Access | Times Cited: 14
A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks
Ahmed Abdel Nasser, M. Z. Rashad, Sherif E. Hussein
IEEE Access (2020) Vol. 8, pp. 147647-147661
Open Access | Times Cited: 63
Long Short-Term Memory Networks to Predict One-Step Ahead Reference Evapotranspiration in a Subtropical Climatic Zone
Dilip Kumar Roy
Environmental Processes (2021) Vol. 8, Iss. 2, pp. 911-941
Closed Access | Times Cited: 50
A maximal overlap discrete wavelet packet transform integrated approach for rainfall forecasting – A case study in the Awash River Basin (Ethiopia)
John Quilty, Jan Adamowski
Environmental Modelling & Software (2021) Vol. 144, pp. 105119-105119
Closed Access | Times Cited: 44
Probabilistic urban water demand forecasting using wavelet-based machine learning models
Mostafa Rezaali, John Quilty, Abdolreza Karimi
Journal of Hydrology (2021) Vol. 600, pp. 126358-126358
Closed Access | Times Cited: 43
Development of wavelet-based Kalman Online Sequential Extreme Learning Machine optimized with Boruta-Random Forest for drought index forecasting
Mehdi Jamei, Iman Ahmadianfar, Masoud Karbasi, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 117, pp. 105545-105545
Closed Access | Times Cited: 37
A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability
Sivarama Krishna Reddy Chidepudi, Nicolas Masseï, Abderrahim Jardani, et al.
The Science of The Total Environment (2022) Vol. 865, pp. 161035-161035
Open Access | Times Cited: 33
Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data
Saeideh Samani, Meysam Vadiati, Zohre Nejatijahromi, et al.
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 9, pp. 22863-22884
Closed Access | Times Cited: 32
Exploring Temporal Dynamics of River Discharge Using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River
Md Abdullah Al Mehedi, Marzieh Khosravi, Munshi Md. Shafwat Yazdan, et al.
Hydrology (2022) Vol. 9, Iss. 11, pp. 202-202
Open Access | Times Cited: 31
Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery
Narendra Singh Chandel, Yogesh Anand Rajwade, Kumkum Dubey, et al.
Plants (2022) Vol. 11, Iss. 23, pp. 3344-3344
Open Access | Times Cited: 31
Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
Muhamad Nur Adli Zakaria, Ali Najah Ahmed, Marlinda Abdul Malek, et al.
Heliyon (2023) Vol. 9, Iss. 7, pp. e17689-e17689
Open Access | Times Cited: 20
Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin
Mahdi Nakhaei, Hossein Zanjanian, Pouria Nakhaei, et al.
Water (2024) Vol. 16, Iss. 2, pp. 208-208
Open Access | Times Cited: 6
Review of deep learning-based methods for non-destructive evaluation of agricultural products
Zhenye Li, Dongyi Wang, Tingting Zhu, et al.
Biosystems Engineering (2024) Vol. 245, pp. 56-83
Closed Access | Times Cited: 6
A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes
John Quilty, Jan Adamowski
Environmental Modelling & Software (2020) Vol. 130, pp. 104718-104718
Closed Access | Times Cited: 46
Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network
Bishwajit Roy, Maheshwari Prasad Singh, Mosbeh R. Kaloop, et al.
Applied Sciences (2021) Vol. 11, Iss. 13, pp. 6238-6238
Open Access | Times Cited: 35
On the reliability of a novel MODWT-based hybrid ARIMA-artificial intelligence approach to forecast daily Snow Depth (Case study: The western part of the Rocky Mountains in the U.S.A)
Arash Adib, Arash Zaerpour, Morteza Lotfirad
Cold Regions Science and Technology (2021) Vol. 189, pp. 103342-103342
Closed Access | Times Cited: 32
Drought forecasting using the Prophet model in a semi-arid climate region of western India
Amiya Basak, A. T. M. Sakiur Rahman, Jayanta Das, et al.
Hydrological Sciences Journal (2022) Vol. 67, Iss. 9, pp. 1397-1417
Closed Access | Times Cited: 27
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