Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model Induction

[1]  Nitin Muttil,et al.  Testing the Structure of Hydrological Models using Genetic Programming , 2011 .

[2]  Keith Beven,et al.  Do we need a Community Hydrological Model? , 2015 .

[3]  M Serrano Joao,et al.  牧草変化のモニタリング:光学的OptRxR作物センサとGrassmaster IIキャパシタンスプローブ , 2016 .

[4]  Dave Campbell,et al.  Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River , 2015 .

[5]  Amy McGovern,et al.  Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning , 2019, Bulletin of the American Meteorological Society.

[6]  Dmitri Kavetski,et al.  Elements of a flexible approach for conceptual hydrological modeling: 2. Application and experimental insights , 2011 .

[7]  R. Middleton,et al.  Estimating hydrologic vulnerabilities to climate change using simulated historical data: A proof-of-concept for a rapid assessment algorithm in the Colorado River Basin , 2019 .

[8]  Martyn P. Clark,et al.  Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance , 2014 .

[9]  M. Keijzer,et al.  Genetic programming as a model induction engine , 2000 .

[10]  Ibrahim El-Baroudy,et al.  Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content , 2009 .

[11]  Alex J. Cannon,et al.  Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models , 2017 .

[12]  Martin Hanel,et al.  Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting , 2013, Computing.

[13]  Robert E. Criss,et al.  Do Nash values have value? Discussion and alternate proposals , 2008 .

[14]  Vladan Babovic,et al.  GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING 1 , 2001 .

[15]  M. Monjezi,et al.  Prediction of the strength and elasticity modulus of granite through an expert artificial neural network , 2015, Arabian Journal of Geosciences.

[16]  Conor Ryan,et al.  Adaptive logic programming , 2001 .

[17]  Elahe Fallah-Mehdipour,et al.  Prediction and simulation of monthly groundwater levels by genetic programming , 2013 .

[18]  E. Hansen,et al.  NUMERICAL SIMULATION OF THE RAINFALL-RUNOFF PROCESS ON A DAILY BASIS , 1973 .

[19]  Holger R. Maier,et al.  A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network , 2016 .

[20]  Nagiza F. Samatova,et al.  Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data , 2016, IEEE Transactions on Knowledge and Data Engineering.

[21]  Adriaan van Niekerk,et al.  Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates , 2017 .

[22]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

[23]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[24]  M. Sugawara,et al.  Automatic calibration of the tank model / L'étalonnage automatique d'un modèle à cisterne , 1979 .

[25]  Vladan Babovic,et al.  An Evolutionary Approach to Knowledge Induction: Genetic Programming in Hydraulic Engineering , 2001 .

[26]  Vladan Babovic,et al.  Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic Programming , 2015 .

[27]  Claudia Vitolo,et al.  Exploring data mining for hydrological modelling , 2015 .

[28]  William W. Hsieh Machine Learning Methods in the Environmental Sciences: Contents , 2009 .

[29]  Vladan Babovic,et al.  Artificial neural networks as routine for error correction with an application in Singapore regional model , 2012, Ocean Dynamics.

[30]  P. C. Nayak,et al.  Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach , 2006 .

[31]  Seth D. Guikema,et al.  Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds , 2016 .

[32]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 1. Modeling concept , 2015 .

[33]  Dmitri Kavetski,et al.  The influence of conceptual model structure on model performance: a comparative study for 237 French catchments , 2013 .

[34]  Andrea Castelletti,et al.  Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations , 2016 .

[35]  Karthik Duraisamy,et al.  Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils , 2016, ArXiv.

[36]  C. Perrin,et al.  Improvement of a parsimonious model for streamflow simulation , 2003 .

[37]  Alex J. Cannon,et al.  Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes , 2018, Stochastic Environmental Research and Risk Assessment.

[38]  Nagiza F. Samatova,et al.  Theory-Guided Data Science for Climate Change , 2014, Computer.

[39]  R. Woods,et al.  Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments , 2014 .

[40]  S. Brunton,et al.  Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.

[41]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[42]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[43]  Mehdi Vafakhah,et al.  Rainfall–Runoff Modeling Using Support Vector Machine in Snow-Affected Watershed , 2016, Arabian Journal for Science and Engineering.

[44]  Nilo Nascimento,et al.  GR3J: a daily watershed model with three free parameters , 1999 .

[45]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[46]  Vladan Babovic,et al.  Review and comparison of performance indices for automatic model induction , 2019 .

[47]  Vladan Babovic,et al.  Introducing knowledge into learning based on genetic programming. , 2009 .

[48]  Vladan Babovic,et al.  Declarative and Preferential Bias in GP-based Scientific Discovery , 2002, Genetic Programming and Evolvable Machines.

[49]  P. Krause,et al.  COMPARISON OF DIFFERENT EFFICIENCY CRITERIA FOR HYDROLOGICAL MODEL ASSESSMENT , 2005 .

[50]  Om Prakash,et al.  Application of Genetic Programming Models Incorporated in Optimization Models for Contaminated Groundwater Systems Management , 2014 .

[51]  Dmitri Kavetski,et al.  Elements of a flexible approach for conceptual hydrological modeling: 1. Motivation and theoretical development , 2011 .

[52]  Sean W. Fleming,et al.  Artificial neural network forecasting of nonlinear Markov processes , 2007 .

[53]  Dmitri Kavetski,et al.  Hydrological field data from a modeller's perspective: Part 2: process‐based evaluation of model hypotheses , 2011 .

[54]  Slobodan P. Simonovic,et al.  Estimation of missing streamflow data using principles of chaos theory , 2002 .

[55]  Zaher Mundher Yaseen,et al.  Genetic programming in water resources engineering: A state-of-the-art review , 2018, Journal of Hydrology.

[56]  Dimitri Solomatine,et al.  Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology , 2009 .

[57]  Dragan Savic,et al.  Evolutionary Computing in Hydrological Sciences , 2006 .

[58]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[59]  Hubert H. G. Savenije,et al.  Is the groundwater reservoir linear? Learning from data in hydrological modelling , 2005 .

[60]  George Kuczera,et al.  Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation , 2011 .

[61]  Sean W. Fleming,et al.  A Machine Learning Metasystem for Robust Probabilistic Nonlinear Regression-Based Forecasting of Seasonal Water Availability in the US West , 2019, IEEE Access.

[62]  Ozgur Kisi,et al.  Short-term and long-term streamflow prediction by using 'wavelet–gene expression' programming approach , 2016 .

[63]  T. M. Chui,et al.  An empirical method for approximating stream baseflow time series using groundwater table fluctuations , 2014 .

[64]  Alex J. Cannon,et al.  A graphical sensitivity analysis for statistical climate models: application to Indian monsoon rainfall prediction by artificial neural networks and multiple linear regression models , 2002 .

[65]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[66]  Anuj Karpatne,et al.  Machine Learning for the Geosciences: Challenges and Opportunities , 2017, IEEE Transactions on Knowledge and Data Engineering.

[67]  Keith Beven,et al.  Models as multiple working hypotheses: hydrological simulation of tropical alpine wetlands , 2011 .

[68]  Robert J. Abrahart,et al.  Neural network modelling of non-linear hydrological relationships , 2007 .

[69]  Dmitri Kavetski,et al.  From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions , 2016 .

[70]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[71]  Vladan Babovic,et al.  Rainfall runoff modelling based on genetic programming , 2002 .

[72]  A. W. Minns,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[73]  Fabrizio Fenicia,et al.  Comparing classical performance measures with signature indices derived from flow duration curves to assess model structures as tools for catchment classification , 2016 .

[74]  S. Liong,et al.  EC-SVM approach for real-time hydrologic forecasting , 2004 .

[75]  Steven L. Brunton,et al.  Data-driven discovery of partial differential equations , 2016, Science Advances.

[76]  Martyn P. Clark,et al.  Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models , 2008 .