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A Bayesian Optimization-Driven Transformer–BiLSTM Framework for Robust Environmental Time-Series Modelling Across Heterogeneous Ecosystems
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Using data‐driven methods to explore the predictability of surface soil moisture with FLUXNET site data
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Accuracy evaluation of evapotranspiration estimated by inverse analysis using FLUXNET2015 at forest sites
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Soil organic carbon more strongly related to soil moisture than soil temperature in temperate grasslands
inferredcites →https://doi.org/10.1007/s00034-023-02454-8openalex-snapshot
Transformers in Time-Series Analysis: A Tutorial
inferredcites →https://doi.org/10.1007/s00500-021-06009-4openalex-snapshot
GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables
inferredcites →https://doi.org/10.1007/s10980-013-9950-5openalex-snapshot
Relationships between land cover and the surface urban heat island: seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures
inferredcites →https://doi.org/10.1016/j.agrformet.2009.03.004openalex-snapshot
Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI
inferredcites →https://doi.org/10.1016/j.agrformet.2020.108314openalex-snapshot
Improving the global MODIS GPP model by optimizing parameters with FLUXNET data
inferredcites →https://doi.org/10.1016/j.agwat.2023.108414openalex-snapshot
Comparison of estimated and observed evapotranspiration from farmland using inverse analysis and FLUXNET2015
inferredcites →https://doi.org/10.1016/j.compag.2021.106568openalex-snapshot
Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network
inferredcites →https://doi.org/10.1016/j.earscirev.2010.02.004openalex-snapshot
Investigating soil moisture–climate interactions in a changing climate: A review
inferredcites →https://doi.org/10.1016/j.ecolind.2019.105939openalex-snapshot
Variation trend of global soil moisture and its cause analysis
inferredcites →https://doi.org/10.1016/j.geoderma.2018.11.044openalex-snapshot
Estimation of soil temperature from meteorological data using different machine learning models
inferredcites →https://doi.org/10.1016/j.jag.2013.12.006openalex-snapshot
Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index
inferredcites →https://doi.org/10.1016/j.jhydrol.2019.02.009openalex-snapshot
Effects of vegetation on soil temperature and water content: Field monitoring and numerical modelling
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.125840openalex-snapshot
Root zone soil moisture estimation with Random Forest
inferredcites →https://doi.org/10.1029/jc083ic04p01889openalex-snapshot
Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation
inferredcites →https://doi.org/10.1038/s41598-023-48025-4openalex-snapshot
Soil temperature forecasting using a hybrid artificial neural network in Florida subtropical grazinglands agro-ecosystems
inferredcites →https://doi.org/10.1038/s43017-022-00324-6openalex-snapshot
Soil hydrology in the Earth system
inferredcites →https://doi.org/10.1109/78.650093openalex-snapshot
Bidirectional recurrent neural networks
inferredcites →https://doi.org/10.1109/access.2023.3247448openalex-snapshot
On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice
inferredcites →https://doi.org/10.1109/jproc.2015.2494218openalex-snapshot
Taking the Human Out of the Loop: A Review of Bayesian Optimization
inferredcites →https://doi.org/10.1371/journal.pone.0231055openalex-snapshot
Advanced machine learning model for better prediction accuracy of soil temperature at different depths
inferredcites →https://doi.org/10.51526/kbes.2023.4.1.55-77openalex-snapshot
Interpretation the Influence of Hydrometeorological Variables on Soil Temperature Prediction Using the Potential of Deep Learning Model
inferredcites →https://doi.org/10.5194/bg-17-4261-2020openalex-snapshot
Linking tundra vegetation, snow, soil temperature, and permafrost
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