Soil & Water Res., 2023, 18(2):67-80 | DOI: 10.17221/94/2022-SWR

Past, present and future of the applications of machine learning in soil science and hydrologyReview

Xiangwei Wang1, Yizhe Yang2, Jianglong Lv1,3, Hailong He1,3*
1 College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi, P.R. China
2 Shaanxi Provincial Farmland Quality and Agricultural Environmental Protection Workstation, Department of Agriculture and Rural Affairs of Shaanxi Province, Xi’an, Shaanxi, P.R. China
3 Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Northwest A&F University, Yangling, P.R. China

Machine learning can handle an ever-increasing amount of data with the ability to learn models from the data. It has been widely used in a variety of disciplines and is gaining increasingly more attention nowadays. As it is challenging to map soil and hydrological information that are characterised with high spatial and temporal variability, applications of machine learning in soil science and hydrology (AMLSH) have become popularised. To better understand the current state of AMLSH research, a scientific and quantitative approach was performed to statistically analyse publication information from 1973 to 2021 archived in the Scopus database using scientometric analysis tools, including VOSviewer, CiteSpace, and the open-source R package “bibliometrix”. The results show a significant increase in the number of publications on AMLSH since 2006. The major contributions were identified based on country origins (China, the USA, and India), institutions (Hohai University, Islamic Azad University, and Wuhan University), and journals (Journal of Hydrology, Remote Sensing, and Geoderma). The keywords analysis of the AMLSH research demonstrates four research hotspots: neural network, artificial intelligence, machine learning, and soil. The most frequently utilised machine learning (ML) methods are neural networks, decision trees, random forests and other methods for image processing and predictive analysis. McBratney et al. 2003 is the most highly cited article. Our research sheds light on the research process on AMLSH and concludes with future research perspectives.

Keywords: machine learning; science mapping; scientometric analysis; soil

Received: July 26, 2022; Accepted: February 9, 2023; Prepublished online: March 22, 2023; Published: May 22, 2023  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Wang X, Yang Y, Lv J, He H. Past, present and future of the applications of machine learning in soil science and hydrology. Soil & Water Res. 2023;18(2):67-80. doi: 10.17221/94/2022-SWR.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Abraham A., Pedregosa F., Eickenberg M., Gervais P., Mueller A., Kossaifi J., Gramfort A., Thirion B., Varoquaux G. (2014): Machine learning for neuroirnaging with scikit-learn. Frontiers in Neuroinformatics, 8: 14. Go to original source... Go to PubMed...
    2. Acker A. (2015): Toward a hermeneutics of data. Ieee Annals of the History of Computing, 3: 70-75. Go to original source...
    3. Aha D.W., Kibler D., Albert M.K. (1991): Instance-based learning algorithms. Machine Learning, 1: 37-66. Go to original source...
    4. Ao Y.L., Li H.Q., Zhu L.P., Ali S., Yang Z.G. (2019): The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering, 174: 776-789. Go to original source...
    5. Aria M., Cuccurullo C. (2017): Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 4: 959-975. Go to original source...
    6. Ayoubi S., Karchegani P.M. (2012): Determination the factors explaining variability of physical soil organic carbon fractions using Artificial Neural Network. International Journal of Soil Science, 1: 1-14. Go to original source...
    7. Azadmard B., Mosaddeghi M.R., Ayoubi S., Chavoshi E., Raoof M. (2020): Estimation of near-saturated soil hydraulic properties using hybrid genetic algorithm-artificial neural network. Ecohydrology and Hydrobiology, 3: 437-449. Go to original source...
    8. Başkaya O., Jurgens D. (2016): Semi-supervised learning with induced word senses for state of the art word sense disambiguation. Journal of Artificial Intelligence Research, 55: 1025-1058. Go to original source...
    9. Besalatpour A., Hajabbasi M.A., Ayoubi S., Gharipour A., Jazi A.Y. (2012): Prediction of soil physical properties by optimized support vector machines. International Agrophysics, 2: 109-115. Go to original source...
    10. Besalatpour A.A., Ayoubi S., Hajabbasi M.A., Mosaddeghi M.R., Schulin R. (2013): Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. Catena, 111: 72-79. Go to original source...
    11. Bonakdari H., Ebtehaj I., Samui P., Gharabaghi B. (2019): Lake water-level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine. Water Resources Management, 11: 3965-3984. Go to original source...
    12. Brillinger D.R. (1985): Fourier inference: Some methods for the analysis of array and nongaussian series data. JAWRA Journal of the American Water Resources Association, 5: 743-756. Go to original source...
    13. Brungard C.W., Boettinger J.L., Duniway M.C., Wills S.A., Edwards T.C. (2015): Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239: 68-83. Go to original source...
    14. Bui D.T., Tsangaratos P., Nguyen V.T., Liem N.V., Trinh P.T. (2020): Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188: 104426. Go to original source...
    15. Burrough P.A. (1986): Principles of geographical information systems for land resources assessment, Geocarto International, 1: 54. Go to original source...
    16. Buszewski B., Kowalkowski T. (2006): A new model of heavy metal transport in the soil using nonlinear artificial neural networks. Environmental Engineering Science, 4: 589-595. Go to original source...
    17. Certes C., Hubert P. (1985): Application de la programmation logique en hydrologie. Definition d'un programme d'interpretation automatique des pompages d'essai. Journal of Hydrology, 1-2: 137-155. Go to original source...
    18. Chen C. (2004): Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101 (Suppl 1): 5303-5310. Go to original source... Go to PubMed...
    19. Chen H., Xu C.Y., Guo S. (2012): Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology, 434-435: 36-45. Go to original source...
    20. Chen S., Arrouays D., Leatitia Mulder V., Poggio L., Minasny B., Roudier P., Libohova Z., Lagacherie P., Shi Z., Hannam J., Meersmans J., Richer-De-Forges A.C., Walter C. (2022): Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma, 409: 115567. Go to original source...
    21. Chen W., Hong H., Li S., Shahabi H., Wang Y., Wang X., Ahmad B.B. (2019): Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. Journal of Hydrology, 575: 864-873. Go to original source...
    22. Choi R.Y., Coyner A.S., Kalpathy-Cramer J., Chiang M.F., Campbell J.P. (2020): Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology, 2: 12. Go to original source... Go to PubMed...
    23. Cooley R.L., Konikow L.F., Naff R.L. (1986): Nonlinear-regression groundwater flow modeling of a deep regional aquifer system. Water Resources Research, 13: 1759-1778. Go to original source...
    24. Cronican A.E., Gribb M.M. (2004): Hydraulic conductivity prediction for sandy soils. Ground Water, 3: 459-464. Go to original source... Go to PubMed...
    25. Dai L., Ge J., Wang L., Zhang Q., Liang T., Bolan N., Lischeid G., Rinklebe J. (2022): Influence of soil properties, topography, and land cover on soil organic carbon and total nitrogen concentration: A case study in Qinghai-Tibet plateau based on random forest regression and structural equation modeling. Science of the Total Environment, 821: 153440. Go to original source... Go to PubMed...
    26. Duffy J., Franklin M. (1973): Case study of environmental system modeling with the group method od data handling. Joint Automatic Control Conference, 11: 101-111.
    27. Eagleson P.S. (1994): The evolution of modern hydrology (from watershed to continent in 30 years). Advances in Water Resources, 1-2: 3-18. Go to original source...
    28. Egghe L. (2006): Theory and practise of the g-index. Scientometrics, 1: 131-152. Go to original source...
    29. Fajardo M., McBratney A., Whelan B. (2016): Fuzzy clustering of Vis-NIR spectra for the objective recognition of soil morphological horizons in soil profiles. Geoderma, 263: 244-253. Go to original source...
    30. Fang K., Shen C., Kifer D., Yang X. (2017): Prolongation of SMAP to spatiotemporally seamless coverage of Continental U.S. using a deep learning neural network. Geophysical Research Letters, 44: 11030-011039. Go to original source...
    31. Fidêncio P.H., Ruisánchez I., Poppi R.J. (2001): Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy. Analyst, 12: 2194-2200. Go to original source... Go to PubMed...
    32. Gharahi Ghehi N., Nemes A., Verdoodt A., Van Ranst E., Cornelis W.M., Boeckx P. (2012): Nonparametric techniques for predicting soil bulk density of tropical rainforest topsoils in Rwanda. Soil Science Society of America Journal, 4: 1172-1183. Go to original source...
    33. Gharib A., Davies E.G.R. (2021): A workflow to address pitfalls and challenges in applying machine learning models to hydrology. Advances in Water Resources, 152: 103920. Go to original source...
    34. Govindaraju R.S. (2000a): Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering, 2: 115-123. Go to original source...
    35. Govindaraju R.S. (2000b): Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 2: 124-137. Go to original source...
    36. Goyal M.K., Sharma A., Katsifarakis K.L. (2017): Prediction of flow rate of karstic springs using support vector machines. Hydrological Sciences Journal, 13: 2175-2186. Go to original source...
    37. Grimaldi S., Schumann G.J.P., Shokri A., Walker J.P., Pauwels V.R.N. (2019): Challenges, opportunities, and pitfalls for global coupled hydrologic-hydraulic modeling of floods. Water Resources Research, 7: 5277-5300. Go to original source...
    38. Han X., Chen X., Feng L. (2015): Four decades of winter wetland changes in Poyang Lake based on Landsat observations between 1973 and 2013. Remote Sensing of Environment, 156: 426-437. Go to original source...
    39. Hansen M.C., Loveland T.R. (2012): A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122: 66-74. Go to original source...
    40. Harshbarger J.W., Ferris J.G. (1963): Interdisciplinary training program in scientific hydrology. Groundwater, 2: 11-14. Go to original source...
    41. Hastie T., Friedman J., Tibshirani R. (2001): The Elements of Statistical Learning. New York, Springer. Go to original source...
    42. He H.L., Dyck M., Lv J.L. (2020): The heat pulse method for soil physical measurements: A bibliometric analysis. Applied Sciences-Basel, 18: 15. Go to original source...
    43. Hengl T., De Jesus J.M., Heuvelink G.B.M., Gonzalez M.R., Kilibarda M., Blagotic A., Shangguan W., Wright M.N., Geng X.Y., Bauer-Marschallinger B., Guevara M.A., Vargas R., Macmillan R.A., Batjes N.H., Leenaars J.G.B., Ribeiro E., Wheeler I., Mantel S., Kempen B. (2017): SoilGrids250 m: Global gridded soil information based on machine learning. PLoS ONE, 12: e0169748 . Go to original source... Go to PubMed...
    44. Hirsch J.E. (2005): An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 46: 16569-16572. Go to original source... Go to PubMed...
    45. Horton R.E. (1933): The relation of hydrology to the botanical sciences. Eos, Transactions American Geophysical Union, 1: 23-25. Go to original source...
    46. Hsu K.L., Gupta H.V., Sorooshian S. (1995): Artificial neural-network modeling of the rainfall-runoff process. Water Resources Research, 10: 2517-2530. Go to original source...
    47. Huang G., Song S.J., Gupta J.N.D., Wu C. (2014): Semi-supervised and unsupervised extreme learning machines. IEEE Transactions on Cybernetics, 12: 2405-2417. Go to original source... Go to PubMed...
    48. Huang X., Zhang L. (2012): Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1: 161-172. Go to original source...
    49. Huang Y., Lan Y., Thomson S.J., Fang A., Hoffmann W.C., Lacey R.E. (2010): Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture, 2: 107-127. Go to original source...
    50. Ikeda S., Sawaragi Y., Ochiai M. (1976): Sequential GMDH algorithm and its application to river flow prediction. IEEE Transactions on Systems, Man and Cybernetics, 7: 473-479. Go to original source...
    51. Ivakhnenko A.G. (1971): Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics, 4: 364-378. Go to original source...
    52. Jafari A., Khademi H., Finke P.A., Van De Wauw J., Ayoubi S. (2014): Spatial prediction of soil great groups by boosted regression trees using a limited point dataset in an arid region, southeastern Iran. Geoderma, 232-234: 148-163. Go to original source...
    53. Japkowicz N. (2001): Supervised versus unsupervised binary-learning by feedforward neural networks. Machine Learning, 1-2: 97-122. Go to original source...
    54. Jenny H. (1941): Factors of Soil Formation, A System of Quantitative Pedology. New York, Dover Publications.
    55. Jingyi Z., Hall M.J. (2004): Regional flood frequency analysis for the Gan-Ming River basin in China. Journal of Hydrology, 1-4: 98-117. Go to original source...
    56. Jordan M.I., Mitchell T.M. (2015): Machine learning: Trends, perspectives, and prospects. Science, 6245: 255-260. Go to original source... Go to PubMed...
    57. 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., Zhang K. (2010): Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 7318: 951-954. Go to original source... Go to PubMed...
    58. Kinniburgh D.G. (1986): General purpose adsorption isotherms. Environmental Science and Technology, 9: 895-904. Go to original source... Go to PubMed...
    59. Kumar A., Ramsankaran R., Brocca L., Muñoz-Arriola F. (2021): A simple machine learning approach to model real-time streamflow using satellite inputs: Demonstration in a data scarce catchment. Journal of Hydrology, 595: 126046. Go to original source...
    60. Lane P.W. (2002): Generalized linear models in soil science. European Journal of Soil Science, 2: 241-251. Go to original source...
    61. Le Gratiet L., Garnier J. (2015): Asymptotic analysis of the learning curve for Gaussian process regression. Machine Learning, 3: 407-433. Go to original source...
    62. LeCun Y., Bengio Y., Hinton G. (2015): Deep learning. Nature, 7553: 436-444. Go to original source... Go to PubMed...
    63. Lee C.H., Shin D.G. (1999): Using Hellinger distance in a nearest neighbour classifier for relational databases. Knowledge-Based Systems, 7: 363-370. Go to original source...
    64. Lee S., Ryu J.H., Min K., Won J.S. (2003): Landslide susceptibility analysis using GIS and artificial neural network. Earth Surface Processes and Landforms, 12: 1361-1376. Go to original source...
    65. Li Y., Shi Z., Li F., Li H.Y. (2007): Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture, 2: 174-186. Go to original source...
    66. Li Y., Zhao Z., Wei S., Sun D., Yang Q., Ding X. (2021): Prediction of regional forest soil nutrients based on gaofen-1 remote sensing data. Forests, 12: 1430. Go to original source...
    67. Ließ M., Glaser B., Huwe B. (2012): Uncertainty in the spatial prediction of soil texture. Comparison of regression tree and Random Forest models. Geoderma, 170: 70-79. Go to original source...
    68. Liu Z., Huang S.L., Jin W., Mu Y. (2021): Broad learning system for semi-supervised learning. Neurocomputing, 444: 38-47. Go to original source...
    69. Loganathan P., Mahindrakar A.B. (2021): Intercomparing the robustness of machine learning models in simulation and forecasting of streamflow. Journal of Water and Climate Change, 5: 1824-1837. Go to original source...
    70. Ma Y.X., Minasny B., Malone B.P., McBratney A.B. (2019): Pedology and digital soil mapping (DSM). European Journal of Soil Science, 2: 216-235. Go to original source...
    71. Maulik U., Bandyopadhyay S. (2000): Genetic algorithm-based clustering technique. Pattern Recognition, 9: 1455-1465. Go to original source...
    72. McBratney A., De Gruijter J., Bryce A. (2019): Pedometrics timeline. Geoderma, 338: 568-575. Go to original source...
    73. McBratney A.B., Minasny B., Cattle S.R., Vervoort R.W. (2002): From pedotransfer functions to soil inference systems. Geoderma, 1-2: 41-73. Go to original source...
    74. McBratney A.B., Mendonça Santos M.L., Minasny B. (2003): On digital soil mapping. Geoderma, 1-2: 3-52. Go to original source...
    75. Minns A.W., Hall M.J. (1996): Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal, 3: 399-417. Go to original source...
    76. Mishina Y., Murata R., Yamauchi Y., Yamashita T., Fujiyoshi H. (2015): Boosted random forest. IEICE Transactions on Information and Systems, 9: 1630-1636. Go to original source...
    77. Mittermeier M., Braun M., Hofstätter M., Wang Y., Ludwig R.. (2019): Detecting climate change effects on Vb cyclones in a 50-member single-model ensemble using machine learning. Geophysical Research Letters, 24: 14653-14661. Go to original source...
    78. Mjolsness E., Decoste D. (2001): Machine learning for science: State of the art and future prospects. Science, 5537: 2051-2055. Go to original source... Go to PubMed...
    79. Moran C.J., Bui E.N. (2002): Spatial data mining for enhanced soil map modelling. International Journal of Geographical Information Science, 6: 533-549. Go to original source...
    80. Mukerji A., Chatterjee C., Singh Raghuwanshi N. (2009): Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. Journal of Hydrologic Engineering, 6: 647-652. Go to original source...
    81. Nemmour H., Chibani Y. (2006): Multiple support vector machines for land cover change detection: An application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 2: 125-133. Go to original source...
    82. Pachepsky Y.A., Timlin D.J., Rawls W.J. (2001): Soil water retention as related to topographic variables. Soil Science Society of America Journal, 6: 1787-1795. Go to original source...
    83. Pappenberger F., Beven K.J., Hunter N.M., Bates P.D., Gouweleeuw B.T., Thielen J., De Roo A.P.J. (2005): Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS). Hydrology and Earth System Sciences, 4: 381-393. Go to original source...
    84. Peng L., Niu R., Huang B., Wu X., Zhao Y., Ye R. (2014): Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology, 204: 287-301. Go to original source...
    85. Plasek A. (2016): On the cruelty of really writing a history of machine learning. IEEE Annals of the History of Computing, 4: 6-8. Go to original source...
    86. Qiu L., Wang K., Long W., Wang K., Hu W., Amable G.S. (2016): A comparative assessment of the influences of human impacts on soil Cd concentrations based on stepwise linear regression, classification and regression tree, and random forest models. PLoS ONE, 11: e0151131. Go to original source... Go to PubMed...
    87. Rindfuss R.R., Walsh S.J., Turner Ii B.L., Fox J., Mishra V. (2004): Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences of the United States of America, 39: 13976-13981. Go to original source... Go to PubMed...
    88. Rossiter D.G. (2018): Past, present & future of information technology in pedometrics. Geoderma, 342: 131-137. Go to original source...
    89. Rudin C., Wagstaff K.L. (2014): Machine learning for science and society. Machine Learning, 1: 1-9. Go to original source...
    90. Savic D.A., Walters G.A., Davidson J.W. (1999): A genetic programming approach to rainfall-runoff modelling. Water Resources Management, 3: 219-231. Go to original source...
    91. Schaap M.G., Leij F.J., van Genuchten M.T. (2001): ROSETTA: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 3-4: 163-176. Go to original source...
    92. Shadbolt N., Hall W., Berners-Lee T. (2006): The semantic Web revisited. IEEE Intelligent Systems, 3: 96-101. Go to original source...
    93. Sharifi A., Dinpashoh Y., Mirabbasi R. (2017): Daily runoff prediction using the linear and non-linear models. Water Science and Technology, 4: 793-805. Go to original source... Go to PubMed...
    94. Sireesha Naidu G., Pratik M., Rehana S. (2020): Modelling hydrological responses under climate change using machine learning algorithms - Semi-arid river basin of peninsular India. H2Open Journal, 1: 481-498. Go to original source...
    95. Taghizadeh-Mehrjardi R., Schmidt K., Amirian-Chakan A., Rentschler T., Zeraatpisheh M., Sarmadian F., Valavi R., Davatgar N., Behrens T., Scholten T. (2020): Improving the spatial prediction of soil organic carbon content in two contrasting climatic regions by stacking machine learning models and rescanning covariate space. Remote Sensing, 12: 1095. Go to original source...
    96. Tajik S., Ayoubi S., Nourbakhsh F. (2012): Prediction of soil enzymes activity by digital terrain analysis: Comparing artificial neural network and multiple linear regression models. Environmental Engineering Science, 8: 798-806. Go to original source...
    97. Tan Q.F., Lei X.H., Wang X., Wang H., Wen X., Ji Y., Kang A.Q. (2018): An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of Hydrology, 567: 767-780. Go to original source...
    98. Tien Bui D., Hoang N.D., Martínez-Álvarez F., Ngo P.T.T., Hoa P.V., Pham T.D., Samui P., Costache R. (2020): A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of the Total Environment, 701: 134413. Go to original source... Go to PubMed...
    99. Usama M., Qadir J., Raza A., Arif H., Yau K.L.A, Elkhatib Y., Hussain A., Al-Fuqaha A. (2019): Unsupervised machine learning for networking: techniques, applications and research challenges. IEEE Access, 78713992: 65579-65615. Go to original source...
    100. Van Eck N.J., Waltman L. (2010): Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 2: 523-538. Go to original source... Go to PubMed...
    101. Van Engelen J.E., Hoos H.H. (2020): A survey on semi-supervised learning. Machine Learning, 2: 373-440. Go to original source...
    102. Vilchez-Roman C. (2014): Bibliometric factors associated with h-index of Peruvian researchers with publications indexed on Web of Science and Scopus databases. Transinformacao, 2: 143-154. Go to original source...
    103. Wadoux A.M.J.C., Minasny B., McBratney A.B. (2020): Machine learning for digital soil mapping: Applications, challenges and suggested solutions. Earth-Science Reviews, 210: 103359. Go to original source...
    104. Wang H.B., Xu W.Y., Xu R.C. (2005): Slope stability evaluation using Back Propagation Neural Networks. Engineering Geology, 3-4: 302-315. Go to original source...
    105. Wang N., Xue J., Peng J., Biswas A., He Y., Shi Z. (2020): Integrating remote sensing and landscape characteristics to estimate soil salinity using machine learning methods: A case study from Southern Xinjiang, China. Remote Sensing, 12: 1-21. Go to original source...
    106. Weng Q.H., Lu D.S., Schubring J. (2004): Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 4: 467-483. Go to original source...
    107. Xie H.L., Zhang Y.W., Wu Z.L., Lv T.G. (2020): A bibliometric analysis on land degradation: Current status, development, and future directions. Land, 9: 28. Go to original source...
    108. Xu Y.Y., Zhou Y., Sekula P., Ding L.Y. (2021): Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 6: 100045. Go to original source...
    109. Yang J., Wang X., Wang R., Wang H. (2020): Combination of convolutional neural networks and recurrent neural networks for predicting soil properties using Vis-NIR spectroscopy. Geoderma, 380: 114616. Go to original source...
    110. Yuan Q., Shen H., Li T., Li Z., Li S., Jiang Y., Xu H., Tan W., Yang Q., Wang J., Gao J., Zhang L. (2020): Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241: 111716. Go to original source...
    111. Zeraatpisheh M., Jafari A., Bagheri Bodaghabadi M., Ayoubi S., Taghizadeh-Mehrjardi R., Toomanian N., Kerry R., Xu M. (2020): Conventional and digital soil mapping in Iran: Past, present, and future. Catena, 188: 104424. Go to original source...
    112. Zhang H., Wu P., Yin A., Yang X., Zhang M., Gao C. (2017): Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model. Science of the Total Environment, 592: 704-713. Go to original source... Go to PubMed...
    113. Zhang H.L., Liu X.Y., Yi J., Yang X.F., Wu T.I., He Y., Duan H., Liu M.X., Tian P. (2020a): Bibliometric analysis of research on soil water from 1934 to 2019. Water, 12: 1631. Go to original source...
    114. Zhang T., Wang C.J., Liu S.Y., Zhang N., Zhang T.W. (2020b): Assessment of soil thermal conduction using artificial neural network models. Cold Regions Science and Technology, 169: 102907. Go to original source...
    115. Zhu A.X., Band L.E. (1994): A knowledge-based approach to data integration for soil mapping. Canadian Journal of Remote Sensing, 4: 408-418. Go to original source...
    116. Zhu R., Yang L., Liu T., Wen X., Zhang L., Chang Y. (2019): Hydrological responses to the future climate change in a data scarce region, northwest China: Application of machine learning models. Water (Switzerland), 11: 1588. Go to original source...
    117. Zhu S.N., Lu H.F., Ptak M., Dai J.Y., Ji Q.F. (2020): Lake water-level fluctuation forecasting using machine learning models: A systematic review. Environmental Science and Pollution Research, 36: 44807-44819. Go to original source... Go to PubMed...
    118. Zolfaghari Z., Mosaddeghi M.R., Ayoubi S. (2015): ANN-based pedotransfer and soil spatial prediction functions for predicting Atterberg consistency limits and indices from easily available properties at the watershed scale in western Iran. Soil Use and Management, 1: 142-154. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content