Chandrashekar, G. and Sahin, F.: A survey on feature selection methods,
Comput. Electr. Eng., 40, 16–28, https://doi.org/10.1016/j.compeleceng.2013.11.024, 2014.
Dai, C., Xue, L., Zhang, D., and Guadagnini, A.: Data-worth analysis through
probabilistic collocation-based Ensemble Kalman Filter, J. Hydrol., 540,
488–503, https://doi.org/10.1016/j.jhydrol.2016.06.037, 2016.
Dausman, A. M., Doherty, J., Langevin, C. D., and Sukop, M. C.: Quantifying
Data Worth Toward Reducing Predictive Uncertainty, Groundwater, 48, 729–740, https://doi.org/10.1111/j.1745-6584.2010.00679.x, 2010.
Dobriyal, P., Qureshi, A., Badola, R., and Hussain, S. A.: A review of the
methods available for estimating soil moisture and its implications for
water resource management, J. Hydrol., 458–459, 110–117,
https://doi.org/10.1016/j.jhydrol.2012.06.021, 2012.
Dunne, S. and Entekhabi, D.: An ensemble-based reanalysis approach to land
data assimilation, Water Resour. Res., 41, W02013, https://doi.org/10.1029/2004WR003449, 2005.
Fienen, M. N., Doherty, J. E., Hunt, R. J., and Reeves, H. W.: Using
prediction uncertainty analysis to design hydrologic monitoring networks:
example applications from the Great Lakes water availability pilot project,
US Geological Survey,
https://pubs.usgs.gov/sir/2010/5159/ (last access: 15 July 2023), 2010.
Finsterle, S.: Practical notes on local data-worth analysis, Water Resour.
Res., 51, 9904–9924, https://doi.org/10.1002/2015WR017445, 2015.
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., and Herrera, F.: Big data preprocessing: methods and prospects, Big Data Anal., 1, 9, https://doi.org/10.1186/s41044-016-0014-0, 2016.
García-Gil, D., Luengo, J., García, S., and Herrera, F.: Enabling
Smart Data: Noise filtering in Big Data classification, Inform. Sci., 479,
135–152, https://doi.org/10.1016/j.ins.2018.12.002, 2019.
Gu, H., Lin, Z., Guo, W., and Deb, S.: Retrieving Surface Soil Water Content
Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System
Images, Remote Sens., 13, 145, https://doi.org/10.3390/rs13010145, 2021.
Hall, M. A.: Correlation-based feature selection for machine learning, The University of Waikato,
https://researchcommons.waikato.ac.nz/handle/10289/15043 (last access: 15 July 2023), 1999.
Hamilton, F., Berry, T., and Sauer, T.: Kalman-Takens filtering in the
presence of dynamical noise, Eur. Phys. J. Spec. Top., 226, 3239–3250,
https://doi.org/10.1140/epjst/e2016-60363-2, 2017.
Hill, M. C. and Tiedeman, C. R.: Effective groundwater model calibration:
with analysis of data, sensitivities, predictions, and uncertainty, John
Wiley & Sons,
https://wwwbrr.cr.usgs.gov/projects/GW_ModUncert/hill_tiedeman_book/exercise-files-UCODE_2005/ExerciseInstructions-mfi05-uc-v17.pdf (last access: 15 July 2023), 2006.
Hughes, G.: On the mean accuracy of statistical pattern recognizers, IEEE
T. Inform. Theory, 14, 55–63, https://doi.org/10.1109/TIT.1968.1054102, 1968.
Ju, L., Zhang, J., Meng, L., Wu, L., and Zeng, L.: An adaptive Gaussian
process-based iterative ensemble smoother for data assimilation, Adv. Water
Resour., 115, 125–135, https://doi.org/10.1016/j.advwatres.2018.03.010, 2018.
Kashif Gill, M., Kemblowski, M. W., and McKee, M.: Soil Moisture Data
Assimilation Using Support Vector Machines and Ensemble Kalman Filter1, J. Am. Water Resour. Assoc., 43, 1004–1015, https://doi.org/10.1111/j.1752-1688.2007.00082.x, 2007.
Kisekka, I., Migliaccio, K. W., Muñoz-Carpena, R., Schaffer, B., and Khare, Y.: Modelling soil water dynamics considering measurement uncertainty, Hydrol. Process., 29, 692–711, https://doi.org/10.1002/hyp.10173, 2015.
Lannoy, G. J. M. D., Verhoest, N. E. C., Houser, P. R., Gish, T. J., and
Meirvenne, M. V.: Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3), J. Hydrol., 331, 719–730, https://doi.org/10.1016/j.jhydrol.2006.06.016, 2006.
Leube, P. C., Geiges, A., and Nowak, W.: Bayesian assessment of the expected
data impact on prediction confidence in optimal sampling design, Water Resour. Res., 48, W02501, https://doi.org/10.1029/2010WR010137, 2012.
Li, C. and Ren, L.: Estimation of Unsaturated Soil Hydraulic Parameters Using the Ensemble Kalman Filter, Vadose Zone J., 10, 1205–1227,
https://doi.org/10.2136/vzj2010.0159, 2011.
Li, P., Zha, Y., Shi, L., Tso, C.-H. M., Zhang, Y., and Zeng, W.: Comparison
of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics, J. Hydrol., 584, 124692, https://doi.org/10.1016/j.jhydrol.2020.124692, 2020.
Li, X., Shi, L., Zha, Y., Wang, Y., and Hu, S.: Data assimilation of soil
water flow by considering multiple uncertainty sources and spatial–temporal
features: a field-scale real case study, Stoch. Environ. Res. Risk A., 32, 2477–2493, https://doi.org/10.1007/s00477-018-1541-1, 2018.
Liu, H. L., Yang, J. Y., Tan, C. S., Drury, C. F., Reynolds, W. D., Zhang, T. Q., Bai, Y. L., Jin, J., He, P., and Hoogenboom, G.: Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model, Agr. Water Manage., 98, 1105–1111, https://doi.org/10.1016/j.agwat.2011.01.017, 2011.
Liu, K., Huang, G., Jiang, Z., Xu, X., Xiong, Y., Huang, Q., and Šimůnek, J.: A gaussian process-based iterative Ensemble Kalman Filter for parameter estimation of unsaturated flow, J. Hydrol., 589, 125210, https://doi.org/10.1016/j.jhydrol.2020.125210, 2020.
Man, J., Zhang, J., Li, W., Zeng, L., and Wu, L.: Sequential ensemble-based
optimal design for parameter estimation, Water Resour. Res., 52, 7577–7592,
https://doi.org/10.1002/2016WR018736, 2016.
Minns, A. W. and Hall, M. J.: Artificial neural networks as rainfall-runoff
models, Hydrolog. Sci. J., 41, 399–417, https://doi.org/10.1080/02626669609491511, 1996.
Montzka, C., Moradkhani, H., Weihermüller, L., Franssen, H.-J. H., Canty, M., and Vereecken, H.: Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter, J. Hydrol., 399, 410–421, https://doi.org/10.1016/j.jhydrol.2011.01.020, 2011.
Neuman, S. P., Xue, L., Ye, M., and Lu, D.: Bayesian analysis of data-worth
considering model and parameter uncertainties, Adv. Water Resour., 36, 75–85, https://doi.org/10.1016/j.advwatres.2011.02.007, 2012.
Nowak, W., Rubin, Y., and de Barros, F. P. J.: A hypothesis-driven approach
to optimize field campaigns, Water Resour. Res., 48, W06509, https://doi.org/10.1029/2011WR011016, 2012.
Olvera-López, J. A., Carrasco-Ochoa, J. A., Martínez-Trinidad, J. F., and Kittler, J.: A review of instance selection methods, Artif. Intell. Rev., 34, 133–143, https://doi.org/10.1007/s10462-010-9165-y, 2010.
Pechenizkiy, M., Tsymbal, A., Puuronen, S., and Pechenizkiy, O.: Class Noise
and Supervised Learning in Medical Domains: The Effect of Feature Extraction, in: 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 22–23 June 2006, Salt Lake City, UT, USA, 708713, https://doi.org/10.1109/CBMS.2006.65, 2006.
Rasmussen, C. E.: Gaussian Processes in Machine Learning, in: Advanced
Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia,
February 2–14, 2003, Tübingen, Germany, August 4–16, 2003, Revised
Lectures, edited by: Bousquet, O., von Luxburg, U., and Rätsch, G.,
Springer, Berlin, Heidelberg, 63–71, https://doi.org/10.1007/978-3-540-28650-9_4, 2004.
Reichle, R. H., Crow, W. T., and Keppenne, C. L.: An adaptive ensemble
Kalman filter for soil moisture data assimilation, Water Resour. Res., 44,
W03423, https://doi.org/10.1029/2007WR006357, 2008.
Richards, L. A.: Capillary Conduction Of Liquids Through Porous Mediums,
Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Richardson, L. F.: Weather prediction by numerical process, Cambridge University Press, UK, https://doi.org/10.1017/CBO9780511618291, 1922.
Ross, P. J.: Modeling Soil Water and Solute Transport – Fast, Simplified Numerical Solutions, Agron. J., 95, 1352–1361, https://doi.org/10.2134/agronj2003.1352, 2003.
Shannon, C. E.: Communication in the Presence of Noise, Proc. IRE, 37, 10–21, https://doi.org/10.1109/JRPROC.1949.232969, 1949.
Shi, C., Xie, Z., Qian, H., Liang, M., and Yang, X.: China land soil moisture EnKF data assimilation based on satellite remote sensing data, Sci. China Earth Sci., 54, 1430–1440, https://doi.org/10.1007/s11430-010-4160-3, 2011.
Shuwen, Z., Haorui, L., Weidong, Z., Chongjian, Q., and Xin, L.: Estimating
the soil moisture profile by assimilating near-surface observations with the
ensemble Kaiman filter (EnKF), Adv. Atmos. Sci., 22, 936–945,
https://doi.org/10.1007/BF02918692, 2005.
Šimůnek, J., Van Genuchten, M. T., and Šejna, M.: The HYDRUS software package for simulating two-and three-dimensional movement of water, heat, and multiple solutes in variably saturated media, Tech. Man. Version 1, 241 pp., https://www.researchgate.net/profile/Jiri-Jirka-Simunek/publication/236901785_The_HYDRUS-2D_Software_Package_for_Simulating_Water (last access: 15 July 2023), 2006.
Singh, K., Sandu, A., Jardak, M., Bowman, K. W., and Lee, M.: A Practical
Method to Estimate Information Content in the Context of 4D-Var Data Assimilation, SIAMASA J. Uncertain. Quantif., 1, 106–138,
https://doi.org/10.1137/120884523, 2013.
Song, X., Shi, L., Ye, M., Yang, J., and Navon, I. M.: Numerical Comparison
of Iterative Ensemble Kalman Filters for Unsaturated Flow Inverse Modeling,
Vadose Zone J., 13, vzj2013.05.0083, https://doi.org/10.2136/vzj2013.05.0083, 2014.
van Dam, J. C. and Feddes, R. A.: Numerical simulation of infiltration,
evaporation and shallow groundwater levels with the Richards equation, J.
Hydrol., 233, 72–85, https://doi.org/10.1016/S0022-1694(00)00227-4, 2000.
Vauclin, M., Khanji, D., and Vachaud, G.: Experimental and numerical study of a transient, two-dimensional unsaturated-saturated water table recharge problem, Water Resour. Res., 15, 1089–1101, https://doi.org/10.1029/WR015i005p01089, 1979.
Wang, Y., Shi, L., Zha, Y., Li, X., Zhang, Q., and Ye, M.: Sequential data-worth analysis coupled with ensemble Kalman filter for soil water flow:
A real-world case study, J. Hydrol., 564, 76–88, https://doi.org/10.1016/j.jhydrol.2018.06.059, 2018.
Wang, Y., Shi, L., Lin, L., Holzman, M., Carmona, F., and Zhang, Q.: A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning, Vadose Zone J., 19, e20026, https://doi.org/10.1002/vzj2.20026, 2020.
Wang, Y., Shi, L., Xu, T., Zhang, Q., Ye, M., and Zha, Y.: A nonparametric
sequential data assimilation scheme for soil moisture flow, J. Hydrol., 593,
125865, https://doi.org/10.1016/j.jhydrol.2020.125865, 2021a.
Wang, Y., Shi, L., Zhang, Q., and Qiao, H.: A gradient-enhanced sequential
nonparametric data assimilation framework for soil moisture flow, J. Hydrol., 603, 126857, https://doi.org/10.1016/j.jhydrol.2021.126857, 2021b.
Wierenga, P. J., Gelhar, L. W., Simmons, C. S., Gee, G. W., and Nicholson, T. J.: Validation of stochastic flow and transport models for unsaturated soils: a comprehensive field study, United States, OSTI.GOV,
https://www.osti.gov/biblio/5367083 (last access: 15 July 2023), 1986.
Xu, Q.: Measuring information content from observations for data assimilation: relative entropy versus shannon entropy difference, Tellus A, 59, 198–209, https://doi.org/10.1111/j.1600-0870.2006.00222.x, 2007.
Xu, T. and Valocchi, A. J.: Data-driven methods to improve baseflow prediction of a regional groundwater model, Comput. Geosci., 85, 124–136,
https://doi.org/10.1016/j.cageo.2015.05.016, 2015.
Yamanaka, A., Maeda, Y., and Sasaki, K.: Ensemble Kalman filter-based data
assimilation for three-dimensional multi-phase-field model: Estimation of
anisotropic grain boundary properties, Mater. Des., 165, 107577,
https://doi.org/10.1016/j.matdes.2018.107577, 2019.
Yang, J., Li, B., and Shiping, L.: A large weighing lysimeter for
evapotranspiration and soil-water–groundwater exchange studies, Hydrol.
Process., 14, 1887–1897, https://doi.org/10.1002/1099-1085(200007)14:10<1887::AID-HYP69>3.0.CO;2-B, 2000.
Yeh, T.-C. J., Gelhar, L. W., and Gutjahr, A. L.: Stochastic Analysis of
Unsaturated Flow in Heterogeneous Soils: 1. Statistically Isotropic Media,
Water Resour. Res., 21, 447–456, https://doi.org/10.1029/WR021i004p00447, 1985.
Zha, Y., Shi, L., Ye, M., and Yang, J.: A generalized Ross method for two-
and three-dimensional variably saturated flow, Adv. Water Resour., 54,
67–77, https://doi.org/10.1016/j.advwatres.2013.01.002, 2013.
Zhang, J., Zeng, L., Chen, C., Chen, D., and Wu, L.: Efficient Bayesian
experimental design for contaminant source identification, Water Resour. Res., 51, 576–598, https://doi.org/10.1002/2014WR015740, 2015.
Zhang, Q., Shi, L., Holzman, M., Ye, M., Wang, Y., Carmona, F., and Zha, Y.:
A dynamic data-driven method for dealing with model structural error in soil
moisture data assimilation, Adv. Water Resour., 132, 103407,
https://doi.org/10.1016/j.advwatres.2019.103407, 2019.
Zhu, X. and Wu, X.: Class Noise vs. Attribute Noise: A Quantitative Study,
Artif. Intell. Rev., 22, 177–210, https://doi.org/10.1007/s10462-004-0751-8, 2004.
