Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling

Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions

(2021) Kai Ma et al.   WATER RESOURCES RESEARCH

Deep learned process parameterizations provide better representations of turbulent heat fluxes in hydrologic models

(2021) Andrew Bennett et al.   WATER RESOURCES RESEARCH

Deep Learning, Hydrological Processes and the Uniqueness of Place

(2020) Keith Beven   HYDROLOGICAL PROCESSES

Moving beyond the Technology: A Socio-technical Roadmap for Low-Cost Water Sensor Network Applications

(2020) Feng Mao et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales

(2020) Dapeng Feng et al.   WATER RESOURCES RESEARCH

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

(2020) Benjamin A. Toms et al.   Journal of Advances in Modeling Earth Systems

A surrogate model for the Variable Infiltration Capacity model using deep learning artificial neural network

(2020) Haiting Gu et al.   JOURNAL OF HYDROLOGY

Automatic clustering-based surrogate-assisted genetic algorithm for groundwater remediation system design

(2020) Majid Vali et al.   JOURNAL OF HYDROLOGY

Improving Precipitation Estimation Using Convolutional Neural Network

(2019) Baoxiang Pan et al.   WATER RESOURCES RESEARCH

Machine learning for data-driven discovery in solid Earth geoscience

(2019) Karianne J. Bergen et al.   SCIENCE

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

(2019) Markus Reichstein et al.   NATURE

Simulation assisted machine learning

(2019) Timo M Deist et al.   BIOINFORMATICS

Equifinality and Flux Mapping: a new approach to model evaluation and process representation under uncertainty

(2019) Sina Khatami et al.   WATER RESOURCES RESEARCH

Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose

(2019) Joseph H.A. Guillaume et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Special Issue: Machine Learning for Engineering Design

(2019) Jitesh H. Panchal et al.   JOURNAL OF MECHANICAL DESIGN

Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

(2019) Frederik Kratzert et al.   WATER RESOURCES RESEARCH

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

(2018) Tom Young et al.   IEEE Computational Intelligence Magazine

A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information

(2018) Yumeng Tao et al.   JOURNAL OF HYDROMETEOROLOGY

Has artificial intelligence become alchemy?

(2018) Matthew Hutson   SCIENCE

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

(2018) Chaopeng Shen   WATER RESOURCES RESEARCH

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

(2018) David Silver et al.   SCIENCE

Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data

(2018) Guoqiang Tang et al.   WATER RESOURCES RESEARCH

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity and uncertainty analysis

(2018) Saman Razavi et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Improved validation framework and R-package for artificial neural network models

(2017) Greer B. Humphrey et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

(2017) Fuad Yassin et al.   HYDROLOGICAL PROCESSES

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

(2017) Ronald Gelaro et al.   JOURNAL OF CLIMATE

A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network

(2016) Greer B. Humphrey et al.   JOURNAL OF HYDROLOGY

A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products

(2016) Yumeng Tao et al.   JOURNAL OF HYDROMETEOROLOGY

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

(2015) Hamed Ashouri et al.   BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY

Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models

(2015) Balew A. Mekonnen et al.   HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

A review of surrogate models and their application to groundwater modeling

(2015) M. J. Asher et al.   WATER RESOURCES RESEARCH

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

(2015) Sebastian Bach et al.   PLoS One

Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling

(2014) Wenyan Wu et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Remotely sensed evapotranspiration to calibrate a lumped conceptual model: Pitfalls and opportunities

(2014) R. Willem Vervoort et al.   JOURNAL OF HYDROLOGY

Space can substitute for time in predicting climate-change effects on biodiversity

(2013) Jessica L. Blois et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

(2012) Robert J. Abrahart et al.   PROGRESS IN PHYSICAL GEOGRAPHY

Review of surrogate modeling in water resources

(2012) Saman Razavi et al.   WATER RESOURCES RESEARCH

Towards a comprehensive assessment of model structural adequacy

(2012) Hoshin V. Gupta et al.   WATER RESOURCES RESEARCH

A New Formulation for Feedforward Neural Networks

(2011) S. Razavi et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology

(2011) V. Grimm et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES

Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

(2010) Holger R. Maier et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Improving event-based rainfall–runoff modeling using a combined artificial neural network–kinematic wave approach

(2010) Lloyd H.C. Chua et al.   JOURNAL OF HYDROLOGY

A novel method to estimate model uncertainty using machine learning techniques

(2009) Dimitri P. Solomatine et al.   WATER RESOURCES RESEARCH

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

(2008) Kourosh Behzadian et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Reconciling theory with observations: elements of a diagnostic approach to model evaluation

(2008) Hoshin V. Gupta et al.   HYDROLOGICAL PROCESSES

A tabu search algorithm for the training of neural networks

(2008) B Dengiz et al.   JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY

Stationarity Is Dead: Whither Water Management?

(2008) P. C. D. Milly et al.   SCIENCE

Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach

(2008) Saman Razavi et al.   WATER RESOURCES MANAGEMENT