{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:34:55Z","timestamp":1772609695721,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cWestern Light\u201d-Key Laboratory Cooperative Research Cross-Team Project of the Chinese Academy of Sciences","award":["xbzg-zdsys-202103"],"award-info":[{"award-number":["xbzg-zdsys-202103"]}]},{"name":"\u201cWestern Light\u201d-Key Laboratory Cooperative Research Cross-Team Project of the Chinese Academy of Sciences","award":["42130113"],"award-info":[{"award-number":["42130113"]}]},{"name":"National Natural Science Foundation of China","award":["xbzg-zdsys-202103"],"award-info":[{"award-number":["xbzg-zdsys-202103"]}]},{"name":"National Natural Science Foundation of China","award":["42130113"],"award-info":[{"award-number":["42130113"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS &gt; 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models.<\/jats:p>","DOI":"10.3390\/rs15010188","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:18:18Z","timestamp":1672370298000},"page":"188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas"],"prefix":"10.3390","volume":"15","author":[{"given":"Ting","family":"Zhou","sequence":"first","affiliation":[{"name":"Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohu","family":"Wen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5469-1738","authenticated-orcid":false,"given":"Qi","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haijiao","family":"Yu","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Xi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135539","DOI":"10.1016\/j.scitotenv.2019.135539","article-title":"Ensemble modelling framework for groundwater level prediction in urban areas of India","volume":"712","author":"Yadav","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.jhydrol.2018.12.037","article-title":"A review of the artificial intelligence methods in groundwater level modeling","volume":"572","author":"Rajaee","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126929","DOI":"10.1016\/j.jhydrol.2021.126929","article-title":"Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data","volume":"603","author":"Liu","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"L20402","DOI":"10.1029\/2010GL044571","article-title":"Global depletion of groundwater resources","volume":"37","author":"Wada","year":"2010","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105161","DOI":"10.1016\/j.ijmecsci.2019.105161","article-title":"Modeling and verifying of sawing force in ultrasonic vibration assisted diamond wire sawing (UAWS) based on impact load","volume":"164","author":"Wang","year":"2019","journal-title":"Int. J. Mech. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127630","DOI":"10.1016\/j.jhydrol.2022.127630","article-title":"Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management","volume":"608","author":"Sun","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1007\/s11269-009-9527-x","article-title":"Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India","volume":"24","author":"Mohanty","year":"2009","journal-title":"Water Resour. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104669","DOI":"10.1016\/j.envsoft.2020.104669","article-title":"Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models","volume":"126","author":"Wagena","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Othman, A., Abdelrady, A., and Mohamed, A. (2022). Monitoring Mass Variations in Iraq Using Time-Variable Gravity Data. Remote. Sens., 14.","DOI":"10.3390\/rs14143346"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124015","DOI":"10.1016\/j.jhydrol.2019.124015","article-title":"Choosing between linear and nonlinear models and avoiding overfitting for short and long term groundwater level forecasting in a linear system","volume":"578","author":"Zanotti","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.jhydrol.2015.11.033","article-title":"Gradient-based model calibration with proxy-model assistance","volume":"533","author":"Burrows","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100237","DOI":"10.1016\/j.gsd.2019.100237","article-title":"Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels","volume":"9","author":"Moghaddam","year":"2019","journal-title":"Groundw. Sustain. Dev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"154902","DOI":"10.1016\/j.scitotenv.2022.154902","article-title":"Simulation of regional groundwater levels in arid regions using interpretable machine learning models","volume":"831","author":"Liu","year":"2022","journal-title":"Sci. Total. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103595","DOI":"10.1016\/j.advwatres.2020.103595","article-title":"Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms","volume":"141","author":"Rahman","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.scitotenv.2017.04.189","article-title":"Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models","volume":"599\u2013600","author":"Barzegar","year":"2017","journal-title":"Sci. Total. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127384","DOI":"10.1016\/j.jhydrol.2021.127384","article-title":"Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: Comparative study and insight for federated learning technology","volume":"606","author":"Cui","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neucom.2022.03.014","article-title":"Groundwater level prediction using machine learning models: A comprehensive review","volume":"489","author":"Tao","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1111\/gwat.12620","article-title":"Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model","volume":"56","author":"Sattari","year":"2017","journal-title":"Groundwater"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124898","DOI":"10.1016\/j.jhydrol.2020.124898","article-title":"A probabilistic framework for water budget estimation in low runoff regions: A case study of the central Basin of Iran","volume":"586","author":"Soltani","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1016\/j.scitotenv.2018.03.292","article-title":"Drought evaluation using the GRACE terrestrial water storage deficit over the Yangtze River Basin, China","volume":"634","author":"Sun","year":"2018","journal-title":"Sci. Total. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101092","DOI":"10.1016\/j.ejrh.2022.101092","article-title":"The accuracy of multisource evapotranspiration products and their applicability in streamflow simulation over a large catchment of Southern China","volume":"41","author":"Ding","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"124258","DOI":"10.1016\/j.jhydrol.2019.124258","article-title":"Variations in terrestrial water storage in the Lancang-Mekong river basin from GRACE solutions and land surface model","volume":"580","author":"Jing","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127369","DOI":"10.1016\/j.jhydrol.2021.127369","article-title":"Evaluation of GRACE derived groundwater storage changes in different agro-ecological zones of the Indus Basin","volume":"605","author":"Akhtar","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124100","DOI":"10.1016\/j.jhydrol.2019.124100","article-title":"Separation and prioritization of uncertainty sources in a raster based flood inundation model using hierarchical Bayesian model averaging","volume":"578","author":"Liu","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_25","first-page":"9","article-title":"A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting","volume":"57","author":"Rajagopalan","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"144715","DOI":"10.1016\/j.scitotenv.2020.144715","article-title":"Bayesian machine learning ensemble approach to quantify model uncertainty in predicting groundwater storage change","volume":"769","author":"Yin","year":"2021","journal-title":"Sci. Total. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1111\/j.2517-6161.1995.tb02015.x","article-title":"Assessment and Propagation of Model Uncertainty","volume":"57","author":"Draper","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B (Statistical Methodol.)"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1214\/ss\/1009212519","article-title":"Bayesian model averaging: A tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors","volume":"14","author":"Hoeting","year":"1999","journal-title":"Stat. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1111\/j.1745-6584.2009.00642.x","article-title":"Model Averaging Techniques for Quantifying Conceptual Model Uncertainty","volume":"48","author":"Singh","year":"2010","journal-title":"Groundwater"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1016\/j.ijforecast.2021.11.007","article-title":"Simple averaging of direct and recursive forecasts via partial pooling using machine learning","volume":"38","author":"In","year":"2021","journal-title":"Int. J. Forecast."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6585","DOI":"10.1029\/2017WR021857","article-title":"Estimation and Impact Assessment of Input and Parameter Uncertainty in Predicting Groundwater Flow with a Fully Distributed Model","volume":"54","author":"Mustafa","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1016\/j.advwatres.2006.11.014","article-title":"Multi-model ensemble hydrologic prediction using Bayesian model averaging","volume":"30","author":"Duan","year":"2007","journal-title":"Adv. Water Resour."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1007\/s11269-019-02305-9","article-title":"Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging","volume":"33","author":"Huang","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1016\/j.jhydrol.2018.06.062","article-title":"The response of crop water productivity to climatic variation in the upper-middle reaches of the Heihe River basin, Northwest China","volume":"563","author":"Niu","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, M., Feng, Q., Wen, X., Yin, Z., Yang, L., and Sheng, D. (2021). Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level. Water, 13.","DOI":"10.3390\/w13020139"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s12665-016-5458-z","article-title":"Groundwater simulation for efficient water resources management in Zhangye Oasis, Northwest China","volume":"75","author":"Chen","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"125042","DOI":"10.1016\/j.jhydrol.2020.125042","article-title":"Long-term assessment of groundwater resources carrying capacity using GRACE data and Budyko model","volume":"588","author":"Gao","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_38","unstructured":"Xi, L. (2014). Groundwater Numerical Simulation of the Middle Reaches of Heihe River Basin. [Master\u2019s Thesis, Tsinghua University]."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2679","DOI":"10.1002\/2013WR014633","article-title":"Estimating the human contribution to groundwater depletion in the Middle East, from GRACE data, land surface models, and well observations","volume":"50","author":"Joodaki","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100734","DOI":"10.1016\/j.ejrh.2020.100734","article-title":"Evaluation of GRACE data for water resource management in Iberia: A case study of groundwater storage monitoring in the Algarve region","volume":"32","author":"Neves","year":"2020","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_41","first-page":"345","article-title":"Land Data Assimilation Systems","volume":"26","author":"Houser","year":"2003","journal-title":"Springer Neth."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ali, S., Liu, D., Fu, Q., Cheema, M.J.M., Pham, Q.B., Rahaman, M., Dang, T.D., and Anh, D.T. (2021). Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment. Remote. Sens., 13.","DOI":"10.3390\/rs13173513"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, L., He, Q., Liu, K., Li, J., and Jing, C. (2019). Downscaling of GRACE-Derived Groundwater Storage Based on the Random Forest Model. Remote. Sens., 11.","DOI":"10.3390\/rs11242979"},{"key":"ref_44","first-page":"148","article-title":"Groundwater decline and tree change in floodplain landscapes: Identifying non-linear threshold responses in canopy condition","volume":"2","author":"Kath","year":"2014","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101118","DOI":"10.1016\/j.ejrh.2022.101118","article-title":"Analyses of groundwater storage change using GRACE satellite data in the Usutu-Mhlatuze drainage region, north-eastern South Africa","volume":"42","author":"Ramjeawon","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"967","DOI":"10.5194\/hess-15-967-2011","article-title":"Magnitude and variability of land evaporation and its components at the global scale","volume":"15","author":"Miralles","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.agrformet.2018.01.022","article-title":"Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach","volume":"252","author":"Khan","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"143532","DOI":"10.1016\/j.scitotenv.2020.143532","article-title":"The role of climate change and vegetation greening on the variation of terrestrial evapotranspiration in northwest China\u2019s Qilian Mountains","volume":"759","author":"Yang","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.jhydrol.2015.03.059","article-title":"Optimal selection of groundwater-level monitoring sites in the Zhangye Basin, Northwest China","volume":"525","author":"Ran","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Samani, S., Vadiati, M., Nejatijahromi, Z., Etebari, B., and Kisi, O. (Environ. Sci. Pollut. Res., 2022). Groundwater level response identification by hybrid wavelet\u2013machine learning conjunction models using meteorological data, Environ. Sci. Pollut. Res., online ahead of print.","DOI":"10.1007\/s11356-022-23686-2"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.jhydrol.2015.04.073","article-title":"Extreme Learning Machines: A new approach for prediction of reference evapotranspiration","volume":"527","author":"Abdullah","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/TNN.1997.641482","article-title":"The Nature of Statistical Learning Theory","volume":"8","author":"Cherkassky","year":"1997","journal-title":"IEEE Trans. Neural Networks"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.atmosenv.2013.09.020","article-title":"Ensemble statistical post-processing of the National Air Quality Forecast Capability: Enhancing ozone forecasts in Baltimore, Maryland","volume":"81","author":"Garner","year":"2013","journal-title":"Atmospheric Environ."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fletcher, D. (2018). Bayesian model averaging. Model Averaging, Springer.","DOI":"10.1007\/978-3-662-58541-2"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"885","DOI":"10.13031\/2013.23153","article-title":"Model evaluation guidelines for systematic quantification of accuracy in watershed simulations","volume":"50","author":"Moriasi","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_60","unstructured":"Hammersley, J. (2013). Monte Carlo Methods, Springer Science & Business Media."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.atmosenv.2018.03.027","article-title":"Artificial neural network model for ozone concentration estimation and Monte Carlo analysis","volume":"184","author":"Gao","year":"2018","journal-title":"Atmospheric Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"105653","DOI":"10.1016\/j.compag.2020.105653","article-title":"Uncertainty analysis of artificial intelligence modeling daily reference evapotranspiration in the northwest end of China","volume":"176","author":"Yu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.advengsoft.2011.12.014","article-title":"Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes","volume":"47","author":"Nourani","year":"2012","journal-title":"Adv. Eng. Softw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1623\/hysj.54.5.852","article-title":"Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation \/ Indices pour \u00e9valuer les bornes de pr\u00e9vision de mod\u00e8les hydrologiques et mise en \u0153uvre pour une estimation d\u2019incertitude par vraisemblance g\u00e9n\u00e9ralis\u00e9e","volume":"54","author":"Xiong","year":"2009","journal-title":"Hydrol. Sci. J."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"126735","DOI":"10.1016\/j.jhydrol.2021.126735","article-title":"Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE\u2014A case study over the state of Victoria, Australia","volume":"602","author":"Yin","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jhydrol.2017.12.035","article-title":"Trends and variability in streamflow and snowmelt runoff timing in the southern Tianshan Mountains","volume":"557","author":"Shen","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.proeng.2016.07.546","article-title":"Comparison of Different Configurations of Quantile Regression in Estimating Predictive Hydrological Uncertainty","volume":"154","author":"Muthusamy","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.2166\/nh.2016.396","article-title":"Wavelet analysis\u2013artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China","volume":"48","author":"Wen","year":"2016","journal-title":"Hydrol. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1038\/s41467-022-28770-2","article-title":"Deep learning shows declining groundwater levels in Germany until 2100 due to climate change","volume":"13","author":"Wunsch","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liu, W., Yu, H., Yang, L., Yin, Z., Zhu, M., and Wen, X. (2021). Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas. Water, 13.","DOI":"10.3390\/w13182558"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s12517-020-05573-7","article-title":"Hydrochemical and isotopic characteristics of groundwater in the Jiuquan East Basin, China","volume":"13","author":"Ren","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5900","DOI":"10.1002\/wrcr.20421","article-title":"Predicting groundwater level changes using GRACE data","volume":"49","author":"Sun","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5973","DOI":"10.1029\/2017JD027468","article-title":"Statistical Downscaling of GRACE-Derived Groundwater Storage Using ET Data in the North China Plain","volume":"123","author":"Yin","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"6562","DOI":"10.1002\/2017WR020793","article-title":"The potential of GRACE gravimetry to detect the heavy rainfall-induced impoundment of a small reservoir in the upper Yellow River","volume":"53","author":"Yi","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1007\/s10040-016-1416-9","article-title":"Comparison of GRACE data and groundwater levels for the assessment of groundwater depletion in Jordan","volume":"24","author":"Liesch","year":"2016","journal-title":"Hydrogeol. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.jhydrol.2015.10.053","article-title":"Responses of shelterbelt stand transpiration to drought and groundwater variations in an arid inland river basin of Northwest China","volume":"531","author":"Shen","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jhydrol.2019.04.022","article-title":"Prediction of groundwater depth in an arid region based on maximum tree height","volume":"574","author":"Yang","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1111\/j.1936-704X.2019.03324.x","article-title":"Investigating Relationship Between Soil Moisture and Precipitation Globally Using Remote Sensing Observations","volume":"168","author":"Sehler","year":"2019","journal-title":"J. Contemp. Water Res. Educ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"09005","DOI":"10.1051\/e3sconf\/202126609005","article-title":"Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China","volume":"266","author":"Zhang","year":"2021","journal-title":"E3S Web Conf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"101185","DOI":"10.1016\/j.ejrh.2022.101185","article-title":"A new modelling framework to assess changes in groundwater level","volume":"43","author":"Kalu","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1016\/j.jhydrol.2015.09.038","article-title":"Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model","volume":"529","author":"Chang","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s10661-022-10277-4","article-title":"Application of artificial intelligence models for prediction of groundwater level fluctuations: Case study (Tehran-Karaj alluvial aquifer)","volume":"194","author":"Vadiati","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"126225","DOI":"10.1016\/j.jhydrol.2021.126225","article-title":"An integrated framework of input determination for ensemble forecasts of monthly estuarine saltwater intrusion","volume":"598","author":"Lu","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s10040-021-02306-2","article-title":"Machine-learning-based regional-scale groundwater level prediction using GRACE","volume":"29","author":"Malakar","year":"2021","journal-title":"Hydrogeol. J."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"100559","DOI":"10.1016\/j.gsd.2021.100559","article-title":"The appraisal of groundwater storage dwindling effect, by applying high resolution downscaling GRACE data in and around Mehsana district, Gujarat, India","volume":"13","author":"Karunakalage","year":"2021","journal-title":"Groundw. Sustain. Dev."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"125468","DOI":"10.1016\/j.jhydrol.2020.125468","article-title":"A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran","volume":"591","author":"Sharafati","year":"2020","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:55:36Z","timestamp":1760147736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":86,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010188"],"URL":"https:\/\/doi.org\/10.3390\/rs15010188","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,29]]}}}