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Driver knowability as the dominant control on groundwater-level forecastability

https://doi.org/10.2139/ssrn.7357571
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Nonlinear time‐series modeling of unconfined groundwater head
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The influence of the length of the calibration period and observation frequency on predictive uncertainty in time series modeling of groundwater dynamics
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Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.
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Time Series Analysis
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Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level
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Application of Hybrid Machine Learning for Groundwater Level Prediction: A Comprehensive Review
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Prediction of groundwater level using the hybrid model combining wavelet transform and machine learning algorithms
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Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia
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Reservoir computing approaches to recurrent neural network training
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Groundwater level prediction with machine learning for the Vidisha district, a semi-arid region of Central India
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Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
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A review of the artificial intelligence methods in groundwater level modeling
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Forecasting groundwater anomaly in the future using satellite information and machine learning
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A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data
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Analysis of nationwide groundwater monitoring networks using lumped-parameter models
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Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin
inferredcites →https://doi.org/10.1016/j.jhydrol.2024.131525openalex-snapshot
Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change
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An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India
inferredcites →https://doi.org/10.1016/j.neucom.2022.03.014openalex-snapshot
Groundwater level prediction using machine learning models: A comprehensive review
inferredcites →https://doi.org/10.1016/s0022-1694(02)00307-4openalex-snapshot
Hydrogeochemical and isotopic evidence of groundwater salinization in a coastal aquifer: a case study in Jeju volcanic island, Korea
inferredcites →https://doi.org/10.1029/2001wr001136openalex-snapshot
Transfer function‐noise modeling in continuous time using predefined impulse response functions
inferredcites →https://doi.org/10.1029/2004wr003726openalex-snapshot
Modeling irregularly spaced residual series as a continuous stochastic process
inferredcites →https://doi.org/10.1029/2007wr006239openalex-snapshot
Calibration of transient groundwater models using time series analysis and moment matching
inferredcites →https://doi.org/10.1029/2009jd012528openalex-snapshot
Observing system simulation experiments at the National Centers for Environmental Prediction
inferredcites →https://doi.org/10.1029/2011wr011420openalex-snapshot
Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment
inferredcites →https://doi.org/10.1029/wr017i001p00182openalex-snapshot
Stochastic simulation of daily precipitation, temperature, and solar radiation
inferredcites →https://doi.org/10.1038/nature11295openalex-snapshot
Water balance of global aquifers revealed by groundwater footprint
inferredcites →https://doi.org/10.1038/nclimate2425openalex-snapshot
The global groundwater crisis
inferredcites →https://doi.org/10.1038/s41467-022-28770-2openalex-snapshot
Deep learning shows declining groundwater levels in Germany until 2100 due to climate change
inferredcites →https://doi.org/10.1061/(asce)0733-9496(1985)111:2(157)openalex-snapshot
Extended Streamflow Forecasting Using NWSRFS
inferredcites →https://doi.org/10.1111/gwat.12925openalex-snapshot
Pastas: Open Source Software for the Analysis of Groundwater Time Series
inferredcites →https://doi.org/10.1111/gwat.12927openalex-snapshot
Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model
inferredcites →https://doi.org/10.1111/j.1745-6584.2007.00382.xopenalex-snapshot
Modeling Time Series of Ground Water Head Fluctuations Subjected to Multiple Stresses
inferredcites →https://doi.org/10.1126/science.1091277openalex-snapshot
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
inferredcites →https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2openalex-snapshot
Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems
inferredcites →https://doi.org/10.1175/bams-d-15-00200.1openalex-snapshot
Future Observing System Simulation Experiments
inferredcites →https://doi.org/10.3389/fenvs.2024.1291327openalex-snapshot
Predicting groundwater level using traditional and deep machine learning algorithms
inferredcites →https://doi.org/10.3390/app13042743openalex-snapshot
A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
inferredcites →https://doi.org/10.3390/fi14090259openalex-snapshot
Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis
inferredcites →https://doi.org/10.3390/w11051098openalex-snapshot
Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks
inferredcites →https://doi.org/10.3390/w12010005openalex-snapshot
Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning
inferredcites →https://doi.org/10.5194/gmd-19-2657-2026openalex-snapshot
Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features
inferredcites →https://doi.org/10.5194/hess-17-253-2013openalex-snapshot
Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling
inferredcites →https://doi.org/10.5194/hess-22-2023-2018openalex-snapshot
Benchmarking ensemble streamflow prediction skill in the UK
inferredcites →https://doi.org/10.5194/hess-22-6005-2018openalex-snapshot
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
inferredcites →https://doi.org/10.5194/hess-23-4323-2019openalex-snapshot
Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores
inferredcites →https://doi.org/10.5194/hess-25-1671-2021openalex-snapshot
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
inferredcites →https://doi.org/10.5194/hess-25-2931-2021openalex-snapshot
Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data
inferredcites →https://doi.org/10.5194/hess-28-5193-2024openalex-snapshot
Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
inferredcites →https://doi.org/10.5194/hess-29-3405-2025openalex-snapshot
Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany
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