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Prediction of Future Groundwater Levels Using Machine Learning Methods in Beijing-Tianjin-Hebei Plain, China

https://doi.org/10.2139/ssrn.4783280
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Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.
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A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida
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Forecasting of Groundwater Level using Artificial Neural Network by incorporating river recharge and river bank infiltration
inferredcites →https://doi.org/10.1061/(asce)wr.1943-5452.0000663openalex-extracted
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Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data
inferredcites →https://doi.org/10.1088/1748-9326/aac4bbopenalex-extracted
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Gridded Statistical Downscaling Based on Interpolation of Parameters and Predictor Locations for Summer Daily Precipitation in North China
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inferredcites →https://doi.org/10.3390/atmos13010022openalex-extracted
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inferredcites →https://doi.org/10.3390/atmos13081275openalex-extracted
Warming Climate and Elevated CO2 Will Enhance Future Winter Wheat Yields in North China Region
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Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis
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Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
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Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks
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