Open Access
MATEC Web Conf.
Volume 400, 2024
5th International Conference on Sustainable Practices and Innovations in Civil Engineering (SPICE 2024)
Article Number 02012
Number of page(s) 11
Section Geotechnical and Environmental Engineering
Published online 03 July 2024
  1. Dilip Kumar Roy, Tasnia Hossain Munmun, Chitra Rani Pau, Mohamed Panjarul Haque, Nadhir Al-Ansari and Mohamed A. Mattar, “Improving Forecasting Accuracy of Multi-Scale Groundwater Level Fluctuations Using a Heterogeneous Ensemble of Machine Learning Algorithms” Water 2023, 15, 3624. [Google Scholar]
  2. Junaid Khan, Eunkyu Lee, Awatef Salem Balobaid and Kyungsup Kim, “A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting” Sci. 2023, 13, 2743. [Google Scholar]
  3. Ahmedbahaaaldin Ibrahem Ahmed Osman, Ali Najah Ahmed, Yuk Feng Huang. Pavitra Kumar, Ahmed H. Birima, Mohsen Sherif, Ahmed Sefelnasr, Abdel Azim Ebraheemand, Ahmed El-ShafiePast, “Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches” International Center for Numerical Methods in Engineering (CIMNE) 2022 [Google Scholar]
  4. Khabat Star Mohammed, Saeid Shabanlou, Ahmad Rajabi, Fariborz Yosefvand, Mohammad Ali Izadbakhsh, “Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS” Applied Water Science (2023) 13:54 [CrossRef] [Google Scholar]
  5. Cristina Di Salvo “Improving Results of Existing Groundwater Numerical Models Using Machine Learning Techniques: A Review” Water 2022, 14, 2307. [Google Scholar]
  6. Sudhakar Singha, Srinivas Pasupuleti, Soumya S. Singha, Rambabu Singh, Suresh Kumar “Prediction of groundwater quality using efficient machine learning Technique” Chemosphere 276 (2021) 130265 [CrossRef] [Google Scholar]
  7. Arman Ahmadi, Mohammadali Olyaei, Arash Ghomlaghi, Andre Daccache, Zahra Heydari Mohammad Emami, Graham E. Fogg, Amin Zeynolabedin, and Mojtaba Sadegh “Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis” Water 2022, 14, 949. [Google Scholar]
  8. Stephen Afrifa, Tao Zhang, Peter Appiahene, Vijaya kumar Varadarajan “Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis, Future Internet 2022, 14, 259. [CrossRef] [Google Scholar]
  9. Hamzeh Ghorbani, Ahmed E. Radwan, “Predicting groundwater levels using traditional and deep machine learning algorithms”, Big Data, AI, and the Environment Volume 12 2024 | [Google Scholar]
  10. Ebenezer K, Siabia, b, Yihun Taddele Dilec, Amos T. Kabo-Bahb, d, Mark Amo Boatenga, Geophery K. Anornue, Komlavi Akpotif, Christopher Vuue, Peter Donkorg, Samuel K. Mensahg, Awo B. M. Incoomh, Emmanuel K. Opokue, and Thomas Atta-Darkwa, “Machine learning based groundwater prediction in a data-scarce basin of Ghana”, APPLIED Artificial Intelligence 2022, VOL. 36, NO. 1, e2138130 (30 pages) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.