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Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information

https://doi.org/10.3390/su141811598
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Groundwater Level Prediction/Forecasting and Assessment of Uncertainty Using SGS and ARIMA Models: A Case Study in the Bauru Aquifer System (Brazil)
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A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods
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Optimization of Wavelet-ANFIS and Wavelet-ANN Hybrid Models by Taguchi Method for Groundwater Level Forecasting
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Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network
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Non-linear variable selection for artificial neural networks using partial mutual information
inferredcites →https://doi.org/10.1016/j.jhydrol.2010.11.002openalex-snapshot
A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer
inferredcites →https://doi.org/10.1016/j.jhydrol.2017.08.006openalex-snapshot
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Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India : Analysis of comparative performances of SVR, ANN and LRM
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inferredcites →https://doi.org/10.1016/j.jhydrol.2018.12.037openalex-snapshot
A review of the artificial intelligence methods in groundwater level modeling
inferredcites →https://doi.org/10.1016/j.jhydrol.2019.05.055openalex-snapshot
Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background
inferredcites →https://doi.org/10.1016/j.jhydrol.2019.124226openalex-snapshot
Estimation of prediction interval in ANN-based multi-GCMs downscaling of hydro-climatologic parameters
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.125468openalex-snapshot
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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
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/j.scitotenv.2019.135539openalex-snapshot
Ensemble modelling framework for groundwater level prediction in urban areas of India
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Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 — A strategy for system predictor identification
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inferredcites →https://doi.org/10.1029/2011wr010930openalex-snapshot
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inferredcites →https://doi.org/10.1029/2019wr025326openalex-snapshot
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inferredcites →https://doi.org/10.2166/hydro.2021.108openalex-snapshot
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Determining the Groundwater Level in Hilly and Plain Areas from Multi-Source Observation Data Combined with a Rf-Pso-Grnn Hybrid Approach
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Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD , VMD and EWT decomposition techniques
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A Machine Learning Framework for Drought Prediction in Tropical Malaysia With Input Data Uncertainty
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DLSTM with Adam Waterwheel Optimization for Groundwater Level Prediction in India
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Enhancing reservoir inflow forecasting precision through Bayesian Neural Network modeling and atmospheric teleconnection pattern analysis
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GROUNDWATER LEVEL PREDICTION USING DEEP RECURRENT NEURAL NETWORKS AND UNCERTAINTY ASSESSMENT
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