Review: Theory-guided machine learning applied to hydrogeology—state of the art, opportunities and future challenges
- 20 September 2021
- journal article
- review article
- Published by Springer Nature in Hydrogeology Journal
- Vol. 29 (8) , 2671-2683
Abstract
No abstract availableKeywords
This publication has 69 references indexed in Scilit:
- Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimizationHydrological Sciences Journal, 2014
- Groundwater level forecasting using SVM-PSOInternational Journal of Hydrology Science and Technology, 2012
- A wavelet neural network conjunction model for groundwater level forecastingJournal of Hydrology, 2011
- Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional TheoryChemistry of Materials, 2010
- Types of Machine Learning Algorithms2010
- Industrial control system simulation routines2010
- Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactionsBioinformatics, 2008
- Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference systemComputers & Geosciences, 2006
- Applicability of statistical learning algorithms in groundwater quality modelingWater Resources Research, 2005
- Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learningKnowledge Acquisition, 1991