Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network
Fabio Di Nunno, Francesco Granata
Environmental Research (2020) Vol. 190, pp. 110062-110062
Closed Access | Times Cited: 123

Showing 1-25 of 123 citing articles:

Groundwater level prediction using machine learning models: A comprehensive review
Tao Hai, Mohammed Majeed Hameed, Haydar Abdulameer Marhoon, et al.
Neurocomputing (2022) Vol. 489, pp. 271-308
Open Access | Times Cited: 255
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Andreas Wünsch, Tanja Liesch, Stefan Broda
Hydrology and earth system sciences (2021) Vol. 25, Iss. 3, pp. 1671-1687
Open Access | Times Cited: 212
Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks
Francesco Granata, Fabio Di Nunno
Agricultural Water Management (2021) Vol. 255, pp. 107040-107040
Closed Access | Times Cited: 131
Groundwater level prediction using machine learning algorithms in a drought-prone area
Quoc Bao Pham, Manish Kumar, Fabio Di Nunno, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 13, pp. 10751-10773
Closed Access | Times Cited: 125
A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
Junaid Khan, Eunkyu Lee, Awatef Salem Balobaid, et al.
Applied Sciences (2023) Vol. 13, Iss. 4, pp. 2743-2743
Open Access | Times Cited: 51
Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms
Fabio Di Nunno, Francesco Granata
Agricultural Water Management (2023) Vol. 280, pp. 108232-108232
Open Access | Times Cited: 44
Predicting groundwater level using traditional and deep machine learning algorithms
Fan Feng, Hamzeh Ghorbani, Ahmed E. Radwan
Frontiers in Environmental Science (2024) Vol. 12
Open Access | Times Cited: 27
Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India
Kusum Pandey, Shiv Kumar, Anurag Malik, et al.
Sustainability (2020) Vol. 12, Iss. 21, pp. 8932-8932
Open Access | Times Cited: 81
Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling
Yongkui Yang, Kyong-Ryong Kim, Rongrong Kou, et al.
Process Safety and Environmental Protection (2021) Vol. 158, pp. 515-524
Closed Access | Times Cited: 72
Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
Hejiang Cai, Haiyun Shi, Suning Liu, et al.
Journal of Hydrology Regional Studies (2021) Vol. 37, pp. 100930-100930
Open Access | Times Cited: 62
Artificial Intelligence models for prediction of the tide level in Venice
Francesco Granata, Fabio Di Nunno
Stochastic Environmental Research and Risk Assessment (2021) Vol. 35, Iss. 12, pp. 2537-2548
Closed Access | Times Cited: 61
Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis
Li‐Chiu Chang, Jia-Yi Liou, Fi‐John Chang
Journal of Hydrology (2022) Vol. 612, pp. 128086-128086
Closed Access | Times Cited: 59
Graph neural network for groundwater level forecasting
Tao Bai, Pejman Tahmasebi
Journal of Hydrology (2022) Vol. 616, pp. 128792-128792
Closed Access | Times Cited: 58
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches
Ahmedbahaaaldin Ibrahem Ahmed Osman, Ali Najah Ahmed, Yuk Feng Huang, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 6, pp. 3843-3859
Closed Access | Times Cited: 53
Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions
Ahmed Elbeltagi, Nand Lal Kushwaha, Jitendra Rajput, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 36, Iss. 10, pp. 3311-3334
Closed Access | Times Cited: 53
River flow rate prediction in the Des Moines watershed (Iowa, USA): a machine learning approach
Ahmed Elbeltagi, Fabio Di Nunno, Nand Lal Kushwaha, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 36, Iss. 11, pp. 3835-3855
Closed Access | Times Cited: 39
Short-term forecasts of streamflow in the UK based on a novel hybrid artificial intelligence algorithm
Fabio Di Nunno, Giovanni de Marinis, Francesco Granata
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 39
Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms
Fabio Di Nunno, Francesco Granata
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2349-2375
Closed Access | Times Cited: 25
An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers
Senlin Zhu, Fabio Di Nunno, Jiang Sun, et al.
The Science of The Total Environment (2024) Vol. 926, pp. 171954-171954
Closed Access | Times Cited: 10
Prediction of daily river water temperatures using an optimized model based on NARX networks
Jiang Sun, Fabio Di Nunno, Mariusz Sojka, et al.
Ecological Indicators (2024) Vol. 161, pp. 111978-111978
Open Access | Times Cited: 10
Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production
Emerson Rodolfo Abraham, João Gilberto Mendes dos Reis, Oduvaldo Vendrametto, et al.
Agriculture (2020) Vol. 10, Iss. 10, pp. 475-475
Open Access | Times Cited: 66
Predicting groundwater level fluctuations under climate change scenarios for Tasuj plain, Iran
Babak Ghazi, Esmaeil Jeihouni, Zahra Kalantari
Arabian Journal of Geosciences (2021) Vol. 14, Iss. 2
Closed Access | Times Cited: 49
The prediction of aquifer groundwater level based on spatial clustering approach using machine learning
Hamid Kardan Moghaddam, Sami Ghordoyee Milan, Zahra Kayhomayoon, et al.
Environmental Monitoring and Assessment (2021) Vol. 193, Iss. 4
Closed Access | Times Cited: 46
Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models
Fabio Di Nunno, Francesco Granata, Rudy Gargano, et al.
Environmental Monitoring and Assessment (2021) Vol. 193, Iss. 6
Closed Access | Times Cited: 46
Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models
Fabio Di Nunno, Francesco Granata, Rudy Gargano, et al.
Atmosphere (2021) Vol. 12, Iss. 4, pp. 512-512
Open Access | Times Cited: 42
Page 1 - Next Page

Scroll to top