Articles | Volume 16, issue 19
https://doi.org/10.5194/gmd-16-5685-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-5685-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
Tiejun Wang
CORRESPONDING AUTHOR
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station, Tianjin University, Tianjin, 300072, China
Yonggen Zhang
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, School of Geographic Sciences, Hebei Normal University, Shijiazhuang, 050024, China
Xi Chen
Institute of Surface-Earth System Science, School of Earth System
Science, Tianjin University, Tianjin, 300072, China
Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim, Tianjin University, Tianjin, 300072, China
Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station, Tianjin University, Tianjin, 300072, China
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Cited articles
Abbott, B. W., Bishop, K., Zarnetske, J. P., Minaudo, C., Chapin, F. S.,
Krause, S., Hannah, D. M., Conner, L., Ellison, D., Godsey, S. E., Plont,
S., Marçais, J., Kolbe, T., Huebner, A., Frei, R. J., Hampton, T., Gu,
S., Buhman, M., Sara Sayedi, S., Ursache, O., Chapin, M., Henderson, K. D.,
and Pinay, G.: Human domination of the global water cycle absent from
depictions and perceptions, Nat. Geosci., 12, 533–540,
https://doi.org/10.1038/s41561-019-0374-y, 2019.
Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019.
Araújo, M. B. and New, M.: Ensemble forecasting of species
distributions, Trends Ecol. Evol., 22, 42–47,
https://doi.org/10.1016/j.tree.2006.09.010, 2007.
Bai, Y., Zhang, J., Zhang, S., Yao, F., and Magliulo, V.: A remote
sensing-based two-leaf canopy conductance model: Global optimization and
applications in modeling gross primary productivity and evapotranspiration
of crops, Remote Sens. Environ., 215, 411–437,
https://doi.org/10.1016/j.rse.2018.06.005, 2018.
Bai, Y., Zhang, S., Bhattarai, N., Mallick, K., Liu, Q., Tang, L., Im, J.,
Guo, L., and Zhang, J.: On the use of machine learning based ensemble
approaches to improve evapotranspiration estimates from croplands across a
wide environmental gradient, Agr. Forest Meteorol., 298–299,
108308, https://doi.org/10.1016/j.agrformet.2020.108308, 2021.
Carsel, R. F. and Parrish, R. S.: Developing joint probability distributions
of soil water retention characteristics, Water Resour. Res., 24,
755–769, https://doi.org/10.1029/WR024i005p00755, 1988.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.: SMOTE:
synthetic minority over-sampling technique, J. Artif.
Intell. Res., 16, 321–357, https://doi.org/10.1613/jair.953, 2002.
Chen, H.: Global maps of soil water-retention parameters (field capacity and permanent wilting point) at different soil depths, Figshare [data set], https://doi.org/10.6084/m9.figshare.17098487.v1, 2021.
Chen, H.: AutoML-Ens, Figshare [software], https://doi.org/10.6084/m9.figshare.21547134.v3, 2022.
Chen, H., Zhang, W., and Jafari Shalamzari, M.: Remote detection of
human-induced evapotranspiration in a regional system experiencing increased
anthropogenic demands and extreme climatic variability, Int.
J. Remote Sens., 40, 1887–1908,
https://doi.org/10.1080/01431161.2018.1523590, 2019a.
Chen, H., Zhang, W., Nie, N., and Guo, Y.: Long-term groundwater storage
variations estimated in the Songhua River Basin by using GRACE products,
land surface models, and in-situ observations, Sci. Total
Environ., 649, 372–387, https://doi.org/10.1016/j.scitotenv.2018.08.352,
2019b.
Dai, Y., Shangguan, W., Duan, Q., Liu, B., Fu, S., and Niu, G.: Development
of a China Dataset of Soil Hydraulic Parameters Using Pedotransfer Functions
for Land Surface Modeling, J. Hydrometeorol., 14, 869–887,
https://doi.org/10.1175/jhm-d-12-0149.1, 2013.
Dai, Y., Xin, Q., Wei, N., Zhang, Y., Shangguan, W., Yuan, H., Zhang, S.,
Liu, S., and Lu, X.: A Global High-Resolution Data Set of Soil Hydraulic and
Thermal Properties for Land Surface Modeling, J. Adv.
Model. Earth Sy., 11, 2996–3023, https://doi.org/10.1029/2019MS001784,
2019a.
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019b.
Duan, Z. and Bastiaanssen, W. G. M.: First results from Version 7 TRMM 3B43
precipitation product in combination with a new downscaling–calibration
procedure, Remote Sens. Environ., 131, 1–13,
https://doi.org/10.1016/j.rse.2012.12.002, 2013.
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R.,
McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K.,
Miralles, D. D., Perret, J., Lagouarde, J.-P., Waliser, D., Purdy, A. J.,
French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.:
The future of evapotranspiration: Global requirements for ecosystem
functioning, carbon and climate feedbacks, agricultural management, and
water resources, Water Resour. Res., 53, 2618–2626,
https://doi.org/10.1002/2016WR020175, 2017.
Fragoso, T. M., Bertoli, W., and Louzada, F.: Bayesian Model Averaging: A
Systematic Review and Conceptual Classification, Int. Stat.
Rev., 86, 1–28, https://doi.org/10.1111/insr.12243, 2018.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Han, Q., Liu, Q., Wang, T., Wang, L., Di, C., Chen, X., Smettem, K., and
Singh, S. K.: Diagnosis of environmental controls on daily actual
evapotranspiration across a global flux tower network: the roles of water
and energy, Environ. Res. Lett., 15, 124070,
https://doi.org/10.1088/1748-9326/abcc8c, 2020.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G.
B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – Global Soil Information Based
on Automated Mapping, PLOS ONE, 9, e105992,
https://doi.org/10.1371/journal.pone.0105992, 2014.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLOS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Holtanová, E., Mendlik, T., Koláček, J., Horová, I., and Mikšovský, J.: Similarities within a multi-model ensemble: functional data analysis framework, Geosci. Model Dev., 12, 735–747, https://doi.org/10.5194/gmd-12-735-2019, 2019.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb,
W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P.,
Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl,
J., and Marshall, S.: The Community Earth System Model: A Framework for
Collaborative Research, B. Am. Meteorol. Soc., 94,
1339–1360, https://doi.org/10.1175/bams-d-12-00121.1, 2013.
Jena, S., Mohanty, B. P., Panda, R. K., and Ramadas, M.: Toward Developing a
Generalizable Pedotransfer Function for Saturated Hydraulic Conductivity
Using Transfer Learning and Predictor Selector Algorithm, Water Resour. Res., 57, e2020WR028862, https://doi.org/10.1029/2020WR028862, 2021.
Jia, X., Willard, J., Karpatne, A., Read, J. S., Zwart, J. A., Steinbach,
M., and Kumar, V.: Physics-Guided Machine Learning for Scientific Discovery:
An Application in Simulating Lake Temperature Profiles, ACM/IMS Trans. Data
Sci., 2, 20, https://doi.org/10.1145/3447814, 2021.
Jongjin, B., Jongmin, P., Dongryeol, R., and Minha, C.: Geospatial blending
to improve spatial mapping of precipitation with high spatial resolution by
merging satellite-based and ground-based data, Hydrol. Process., 30,
2789–2803, https://doi.org/10.1002/hyp.10786, 2016.
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J.,
Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, A.
J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball,
J., Law, B. E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D.,
Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N.,
Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline
in the global land evapotranspiration trend due to limited moisture supply,
Nature, 467, 951–954, https://doi.org/10.1038/nature09396, 2010.
Jury, W. A. and Horton, R.: Soil physics, John Wiley & Sons, ISBN 978-0-471-05965-3, 2004.
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A.,
Ganguly, A., Shekhar, S., Samatova, N., and Kumar, V.: Theory-Guided Data
Science: A New Paradigm for Scientific Discovery from Data, IEEE
T. Knowl. Data En., 29, 2318–2331,
https://doi.org/10.1109/TKDE.2017.2720168, 2017.
Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., and Kumar, V.:
Machine Learning for the Geosciences: Challenges and Opportunities, IEEE
T. Knowl. Data En., 31, 1544–1554,
https://doi.org/10.1109/TKDE.2018.2861006, 2019.
Kavzoglu, T.: Increasing the accuracy of neural network classification using
refined training data, Environ. Modell. Softw., 24, 850–858,
https://doi.org/10.1016/j.envsoft.2008.11.012, 2009.
Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., and Sharma, A.: A
framework for combining multiple soil moisture retrievals based on
maximizing temporal correlation, Geophys. Res. Lett., 42,
6662–6670, https://doi.org/10.1002/2015GL064981, 2015.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol.,
424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
LeDell, E. and Poiri, S.: H2O AutoML: Scalable Automatic Machine Learning, in:
7th ICML Workshop on Automated Machine Learning (AutoML), online, 18 July 2020, https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf (last access: 29 March 2021), 2020.
Liu, F., Wu, H., Zhao, Y., Li, D., Yang, J.-L., Song, X., Shi, Z., Zhu, A.
X., and Zhang, G.-L.: Mapping high resolution National Soil Information
Grids of China, Sci. Bull., 67, 328–340,
https://doi.org/10.1016/j.scib.2021.10.013, 2021.
Liu, G., Tang, Z., Qin, H., Liu, S., Shen, Q., Qu, Y., and Zhou, J.:
Short-term runoff prediction using deep learning multi-dimensional ensemble
method, J. Hydrol., 609, 127762,
https://doi.org/10.1016/j.jhydrol.2022.127762, 2022.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A harmonized global land evaporation dataset from model-based products covering 1980–2017, Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, 2021.
Maclin, R. and Opitz, D. W.: Popular Ensemble Methods: An Empirical Study,
J. Artif. Intell. Res., 11, 169–198, https://doi.org/10.1613/jair.614, 1999.
Madadgar, S., Moradkhani, H., and Garen, D.: Towards improved
post-processing of hydrologic forecast ensembles, Hydrol. Process.,
28, 104–122, https://doi.org/10.1002/hyp.9562, 2014.
Montgomery, J. M., Hollenbach, F. M., and Ward, M. D.: Improving Predictions
using Ensemble Bayesian Model Averaging, Polit. Anal., 20, 271–291,
https://doi.org/10.1093/pan/mps002, 2017.
Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G., McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang, Y., and Seneviratne, S. I.: Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis, Hydrol. Earth Syst. Sci., 17, 3707–3720, https://doi.org/10.5194/hess-17-3707-2013, 2013.
Palmer, T. N., Doblas-Reyes, F. J., Hagedorn, R., and Weisheimer, A.:
Probabilistic prediction of climate using multi-model ensembles: from basics
to applications, Philos. T. Roy. Soc. B, 360, 1991–1998, https://doi.org/10.1098/rstb.2005.1750,
2005.
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, 2020.
Pascolini-Campbell, M., Reager, J. T., Chandanpurkar, H. A., and Rodell, M.:
A 10 per cent increase in global land evapotranspiration from 2003 to 2019,
Nature, 593, 543–547, https://doi.org/10.1038/s41586-021-03503-5, 2021.
Rawls, W. J. and D. L. Brakensiek: Prediction of Soil Water Properties for Hydrologic Modelling, in: Proceedings of a Symposium Watershed Management in the Eighties, edited by: Jones, E. B. and Ward, T. J., New York, 30 April–1 May 1985, 293–299,
ISBN-10: 0872624498,
ISBN-13: 978-0872624498, 1985.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Reshmidevi, T. V., Nagesh Kumar, D., Mehrotra, R., and Sharma, A.:
Estimation of the climate change impact on a catchment water balance using
an ensemble of GCMs, J. Hydrol., 556, 1192–1204,
https://doi.org/10.1016/j.jhydrol.2017.02.016, 2018.
Steffen, W., Richardson, K., Rockström, J., Schellnhuber, H. J., Dube,
O. P., Dutreuil, S., Lenton, T. M., and Lubchenco, J.: The emergence and
evolution of Earth System Science, Nature Reviews Earth & Environment, 1,
54–63, https://doi.org/10.1038/s43017-019-0005-6, 2020.
Sun, A. Y., Scanlon, B. R., Save, H., and Rateb, A.: Reconstruction of GRACE
Total Water Storage Through Automated Machine Learning, Water Resour. Res., 57, e2020WR028666, https://doi.org/10.1029/2020WR028666, 2021.
Tebaldi, C., Smith, R. L., Nychka, D., and Mearns, L. O.: Quantifying
Uncertainty in Projections of Regional Climate Change: A Bayesian Approach
to the Analysis of Multimodel Ensembles, J. Climate, 18, 1524–1540,
https://doi.org/10.1175/jcli3363.1, 2005.
Telteu, C.-E., Müller Schmied, H., Thiery, W., Leng, G., Burek, P., Liu, X., Boulange, J. E. S., Andersen, L. S., Grillakis, M., Gosling, S. N., Satoh, Y., Rakovec, O., Stacke, T., Chang, J., Wanders, N., Shah, H. L., Trautmann, T., Mao, G., Hanasaki, N., Koutroulis, A., Pokhrel, Y., Samaniego, L., Wada, Y., Mishra, V., Liu, J., Döll, P., Zhao, F., Gädeke, A., Rabin, S. S., and Herz, F.: Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication, Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, 2021.
Tortell, P. D.: Earth 2020: Science, society, and sustainability in the
Anthropocene, P. Natl. Acad. Sci. USA, 117,
8683–8691, https://doi.org/10.1073/pnas.2001919117, 2020.
Truong, A. T., Walters, A., Goodsitt, J., Hines, K. E., Bruss, C. B., and
Farivar, R.: Towards Automated Machine Learning: Evaluation and Comparison
of AutoML Approaches and Tools, in: 2019 IEEE 31st International Conference on
Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 4–6 November 2019, 1471–1479,
https://doi.org/10.1109/ICTAI.2019.00209, 2019.
Tuggener, L., Amirian, M., Rombach, K., Lörwald, S., Varlet, A.,
Westermann, C., and Stadelmann, T.: Automated Machine Learning in Practice:
State of the Art and Recent Results, 2019 6th Swiss Conference on Data
Science (SDS), 31–36, https://doi.org/10.1109/SDS.2019.00-11, 2019.
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G.,
Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J.,
Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.:
Pedotransfer Functions in Earth System Science: Challenges and Perspectives,
Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581,
2017.
Vereecken, H., Maes, J., Feyen, J., and Darius, P.: Estimating the soil
moisture retention characteristic from texture, bulk density, and carbon
content, Soil Sci., 148, 389–403,
https://doi.org/10.1097/00010694-198912000-00001, 1989.
Wang, K. and Dickinson, R. E.: A review of global terrestrial
evapotranspiration: Observation, modeling, climatology, and climatic
variability, Rev. Geophys., 50, RG2005,
https://doi.org/10.1029/2011RG000373, 2012.
Weynants, M., Vereecken, H., and Javaux, M.: Revisiting Vereecken
Pedotransfer Functions: Introducing a Closed-Form Hydraulic Model, Vadose
Zone J., 8, 86–95, https://doi.org/10.2136/vzj2008.0062, 2009.
Wösten, J. H. M., Lilly, A., Nemes, A., and Le Bas, C.: Development and
use of a database of hydraulic properties of European soils, Geoderma, 90,
169–185, https://doi.org/10.1016/S0016-7061(98)00132-3, 1999.
Yao, Q., Wang, M., Escalante, H. J., Guyon, I., Hu, Y.-Q., Li, Y.-F., Tu,
W.-W., Yang, Q., and Yu, Y.: Taking Human out of Learning Applications: A
Survey on Automated Machine Learning, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.13306,
2018.
Yilmaz, M. T., Crow, W. T., Anderson, M. C., and Hain, C.: An objective
methodology for merging satellite- and model-based soil moisture products,
Water Resour. Res., 48, W11502, https://doi.org/10.1029/2011WR011682, 2012.
Zaherpour, J., Mount, N., Gosling, S. N., Dankers, R., Eisner, S., Gerten,
D., Liu, X., Masaki, Y., Müller Schmied, H., Tang, Q., and Wada, Y.:
Exploring the value of machine learning for weighted multi-model combination
of an ensemble of global hydrological models, Environ. Modell. Softw., 114, 112–128, https://doi.org/10.1016/j.envsoft.2019.01.003, 2019.
Zhang, Y. and Schaap, M. G.: Weighted recalibration of the Rosetta
pedotransfer model with improved estimates of hydraulic parameter
distributions and summary statistics (Rosetta3), J. Hydrol., 547,
39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017.
Zhang, Y., Schaap, M. G., and Zha, Y.: A High-Resolution Global Map of Soil
Hydraulic Properties Produced by a Hierarchical Parameterization of a
Physically Based Water Retention Model, Water Resour. Res., 54,
9774–9790, https://doi.org/10.1029/2018WR023539, 2018.
Zhang, Y., Schaap, M. G., and Wei, Z.: Development of Hierarchical Ensemble
Model and Estimates of Soil Water Retention With Global Coverage,
Geophys. Res. Lett., 47, e2020GL088819,
https://doi.org/10.1029/2020GL088819, 2020.
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin,
C., Li, X., and Qiu, G. Y.: Physics-Constrained Machine Learning of
Evapotranspiration, Geophys. Res. Lett., 46, 14496–14507,
https://doi.org/10.1029/2019GL085291, 2019.
Zounemat-Kermani, M., Batelaan, O., Fadaee, M., and Hinkelmann, R.: Ensemble
machine learning paradigms in hydrology: A review, J. Hydrol.,
598, 126266, https://doi.org/10.1016/j.jhydrol.2021.126266, 2021.
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Effectively assembling multiple models for approaching a benchmark solution remains a...