Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks

M5 accuracy competition: Results, findings, and conclusions

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Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia

(2022) Stephanie R. Clark   ENVIRONMENTAL MODELLING & SOFTWARE

A comprehensive survey on conventional and modern neural networks: application to river flow forecasting

(2021) Mohammad Zounemat-Kermani et al.   Earth Science Informatics

Predicting Water Temperature Dynamics of Unmonitored Lakes With Meta‐Transfer Learning

(2021) Jared D. Willard et al.   WATER RESOURCES RESEARCH

Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE – A case study over the state of Victoria, Australia

(2021) Wenjie Yin et al.   JOURNAL OF HYDROLOGY

Principles and algorithms for forecasting groups of time series: Locality and globality

(2021) Pablo Montero-Manso et al.   INTERNATIONAL JOURNAL OF FORECASTING

Tools for enhancing the application of self-organizing maps in water resources research and engineering

(2020) Stephanie Clark et al.   ADVANCES IN WATER RESOURCES

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

(2020) Zaheed Gaffoor et al.   Water

Modern Strategies for Time Series Regression

(2020) Stephanie Clark et al.   INTERNATIONAL STATISTICAL REVIEW

What Role Does Hydrological Science Play in the Age of Machine Learning?

(2020) Grey S. Nearing et al.   WATER RESOURCES RESEARCH

Deep learning and process understanding for data-driven Earth system science

(2019) Markus Reichstein et al.   NATURE

Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

(2019) Kasun Bandara et al.   EXPERT SYSTEMS WITH APPLICATIONS

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

(2018) Pantelis R. Vlachas et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors

(2018) Sanghoon Lee et al.   HYDROGEOLOGY JOURNAL

A trans-disciplinary review of deep learning research and its relevance for water resources scientists

(2018) Chaopeng Shen   WATER RESOURCES RESEARCH

Essentials of the self-organizing map

(2012) Teuvo Kohonen   NEURAL NETWORKS