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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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What this work says

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

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Global‐scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites
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Hydrological response to changing climate conditions: Spatial streamflow variability in the boreal region
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Spatiotemporal evolution of water storage changes in I ndia from the updated GRACE ‐derived gravity records
inferredcites →https://doi.org/10.1002/wrcr.20192openalex-snapshot
Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground‐based measurements
inferredcites →https://doi.org/10.1002/wrcr.20421openalex-snapshot
Predicting groundwater level changes using GRACE data
inferredcites →https://doi.org/10.1002/wrcr.20517openalex-snapshot
Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models
inferredcites →https://doi.org/10.1007/3-540-63046-5_20openalex-snapshot
Nonlinear versus linear models in functional neuroimaging: Learning curves and generalization crossover
inferredcites →https://doi.org/10.1007/s10040-006-0103-7openalex-snapshot
Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE
inferredcites →https://doi.org/10.1007/s11269-011-9965-0openalex-snapshot
Streamflow Response to Climate Change in the Brahmani River Basin, India
inferredcites →https://doi.org/10.1007/s11269-012-0191-1openalex-snapshot
Impacts of Climate Variables on Residential Water Consumption in the Czech Republic
inferredcites →https://doi.org/10.1007/s11269-012-0194-yopenalex-snapshot
Application of Several Data-Driven Techniques for Predicting Groundwater Level
inferredcites →https://doi.org/10.1007/s11269-012-0220-0openalex-snapshot
Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks
inferredcites →https://doi.org/10.1007/s11269-012-0239-2openalex-snapshot
A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods
inferredcites →https://doi.org/10.1007/s11269-013-0436-7openalex-snapshot
Groundwater Resources Sustainability: Past, Present, and Future
inferredcites →https://doi.org/10.1007/s11269-014-0516-3openalex-snapshot
River Discharges Forecasting In Northern Iraq Using Different ANN Techniques
inferredcites →https://doi.org/10.1007/s11269-014-0587-1openalex-snapshot
Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia
inferredcites →https://doi.org/10.1007/s11269-014-0743-7openalex-snapshot
Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil
inferredcites →https://doi.org/10.1007/s11269-014-0802-0openalex-snapshot
Comparative Study of Artificial Neural Networks and Wavelet Artificial Neural Networks for Groundwater Depth Data Forecasting with Various Curve Fractal Dimensions
inferredcites →https://doi.org/10.1007/s11269-014-0810-0openalex-snapshot
Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)
inferredcites →https://doi.org/10.1007/s11269-014-0875-9openalex-snapshot
A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods
inferredcites →https://doi.org/10.1007/s11269-015-0932-zopenalex-snapshot
Groundwater Storage and Depletion Trends in Tamil Nadu State, India
inferredcites →https://doi.org/10.1007/s11269-015-1021-zopenalex-snapshot
Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models
inferredcites →https://doi.org/10.1007/s11269-015-1132-6openalex-snapshot
Using Artificial Neural Network Approach for Simultaneous Forecasting of Weekly Groundwater Levels at Multiple Sites
inferredcites →https://doi.org/10.1016/j.jhydrol.2004.12.001openalex-snapshot
Groundwater level forecasting using artificial neural networks
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.2011.06.013openalex-snapshot
A wavelet neural network conjunction model for groundwater level forecasting
inferredcites →https://doi.org/10.1016/s0022-1694(02)00060-4openalex-snapshot
The potential for satellite-based monitoring of groundwater storage changes using GRACE: the High Plains aquifer, Central US
inferredcites →https://doi.org/10.1029/2006wr005779openalex-snapshot
Analysis of terrestrial water storage changes from GRACE and GLDAS
inferredcites →https://doi.org/10.1029/2010wr009945openalex-snapshot
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada
inferredcites →https://doi.org/10.1029/2011gl048604openalex-snapshot
Contribution of global groundwater depletion since 1900 to sea-level rise
inferredcites →https://doi.org/10.1029/2011wr010993openalex-snapshot
Monitoring groundwater storage changes in the highly seasonal humid tropics: Validation of GRACE measurements in the Bengal Basin
inferredcites →https://doi.org/10.1029/2011wr011312openalex-snapshot
Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA
inferredcites →https://doi.org/10.1038/nature08238openalex-snapshot
Satellite-based estimates of groundwater depletion in India
inferredcites →https://doi.org/10.1038/nclimate1744openalex-snapshot
Ground water and climate change
inferredcites →https://doi.org/10.1061/(asce)wr.1943-5452.0000152openalex-snapshot
Hybrid Fuzzy Regression–Artificial Neural Network for Improvement of Short-Term Water Consumption Estimation and Forecasting in Uncertain and Complex Environments: Case of a Large Metropolitan City
inferredcites →https://doi.org/10.1061/(asce)wr.1943-5452.0000454openalex-snapshot
Medium-Term Urban Water Demand Forecasting with Limited Data Using an Ensemble Wavelet–Bootstrap Machine-Learning Approach
inferredcites →https://doi.org/10.1061/(asce)wr.1943-5452.0000591openalex-snapshot
Tailoring Seasonal Time Series Models to Forecast Short-Term Water Demand
inferredcites →https://doi.org/10.1073/pnas.1222460110openalex-snapshot
Multimodel assessment of water scarcity under climate change
inferredcites →https://doi.org/10.1596/17242openalex-snapshot
India Groundwater Governance Case Study

What points at this work

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

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inferred← citeshttps://doi.org/10.70675/9c90cc08z6085z4c82z89a7z47073268db61openalex-snapshot
Use of the Artificial Intelligence methods for the detection and localization of leaks in the water distribution networks
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