Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review

  • Sina Ardabili
  • Amir MosaviEmail author
  • Majid Dehghani
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)


Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.


Machine learning Deep learning Big data Hydrology Climate change Global warming Hydrological model Earth systems 



Artificial neural network


Extreme learning machine


Machine learning


Support vector machine


Wavelet neural networks


Deep learning


Autoregressive integrated moving average


Feed-forward neural networks


Multi layered perceptron


Decision tree


Response surface methodology


Back propagation neural network


Gradient boosting decision tree


Adaptive neuro fuzzy inference system


Central processing unit


Fire-fly algorithm


Deep neural network


Random forest


Deep feedforward neural network


Recurrent neural network


Partial least squares


Discriminant analysis


Principal component analysis


Linear discriminant analysis


Support vector regression




Sparse Bayesian


Standard precipitation evapotranspiration index


Genetic programming


Multi linear regression


Moderate Resolution Imaging Spectroradiometer


Reduced order model


Wise step fire-fly algorithm


Deep belief networks.



This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.


  1. 1.
    Taylor, R.G., et al.: Ground water and climate change. Nat. Clim. Chang. 3(4), 322 (2013)CrossRefGoogle Scholar
  2. 2.
    Wang, C., et al.: Most of the northern hemisphere permafrost remains under climate change. Sci. Rep. 9(1), 3295 (2019)CrossRefGoogle Scholar
  3. 3.
    Baynes, E.R.C., et al.: Beyond equilibrium: re-evaluating physical modelling of fluvial systems to represent climate changes. Earth Sci. Rev. 181, 82–97 (2018)CrossRefGoogle Scholar
  4. 4.
    Bouhal, T., et al.: Technical feasibility of a sustainable concentrated solar power in morocco through an energy analysis. Renew. Sustain. Energy Rev. 81, 1087–1095 (2018)CrossRefGoogle Scholar
  5. 5.
    Carrassi, A., et al.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdiscip. Rev. Clim. Chang. 9(5), (2018)Google Scholar
  6. 6.
    He, C., et al.: Review and comparison of empirical thermospheric mass density models. Prog. Aerosp. Sci. 103, 31–51 (2018)CrossRefGoogle Scholar
  7. 7.
    Hill, J., Buddenbaum, H., Townsend, P.A.: Imaging spectroscopy of forest ecosystems: perspectives for the use of space-borne hyperspectral earth observation systems. Surv. Geophys. 40(3), 553–588 (2019)CrossRefGoogle Scholar
  8. 8.
    Qin, W., et al.: Comparison of deterministic and data-driven models for solar radiation estimation in China. Renew. Sustain. Energy Rev. 81, 579–594 (2018)CrossRefGoogle Scholar
  9. 9.
    Raseman, W.J., et al.: Emerging investigators series: A critical review of decision support systems for water treatment: Making the case for incorporating climate change and climate extremes. Environ. Sci.: Water Res. Technol. 3(1), 18–36 (2017)Google Scholar
  10. 10.
    Schenato, L.: A review of distributed fibre optic sensors for geo-hydrological applications. Appl. Sci. 7(9), (2017) (Switzerland)CrossRefGoogle Scholar
  11. 11.
    Seixas, M., et al.: Active bending and tensile pantographic bamboo hybrid amphitheater structure. J. Int. Assoc. Shell Spat. Struct. 58(3), 239–252 (2017)Google Scholar
  12. 12.
    Aslam, R.A., Shrestha, S., Pandey, V.P.: Groundwater vulnerability to climate change: A review of the assessment methodology. Sci. Total Environ. 612, 853–875 (2018)CrossRefGoogle Scholar
  13. 13.
    Devi, R.M., et al.: Understanding the linkages between climate change and forest. Curr. Sci. 114(5), 987–996 (2018)CrossRefGoogle Scholar
  14. 14.
    Estévez, J., et al.: Introduction to the special issue on “hydro-meteorological time series analysis and their relation to climate change”. Acta Geophys. 66(3), 317–318 (2018)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Flowers, G.E.: Hydrology and the future of the greenland ice sheet. Nat. Commun. 9(1) (2018)Google Scholar
  16. 16.
    Mudelsee, M.: Trend analysis of climate time series: a review of methods. Earth Sci. Rev. 190, 310–322 (2019)CrossRefGoogle Scholar
  17. 17.
    Murray, N.J., et al.: The role of satellite remote sensing in structured ecosystem risk assessments. Sci. Total Environ. 619–620, 249–257 (2018)CrossRefGoogle Scholar
  18. 18.
    Rhein, M.: Taking a close look at ocean circulation: Ocean circulation patterns in the North Atlantic provide a benchmark for climate models. Science 363(6426), 456–457 (2019)CrossRefGoogle Scholar
  19. 19.
    Newton, R.J., McClary, J.S.: The flux and impact of wastewater infrastructure microorganisms on human and ecosystem health. Curr. Opin. Biotechnol. 57, 145–150 (2019)CrossRefGoogle Scholar
  20. 20.
    Royapoor, M., Antony, A., Roskilly, T.: A review of building climate and plant controls, and a survey of industry perspectives. Energy Build. 158, 453–465 (2018)CrossRefGoogle Scholar
  21. 21.
    Sun, Z., et al.: Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO 2 trends. Sci. Total Environ. 668, 696–713 (2019)CrossRefGoogle Scholar
  22. 22.
    Akpoti, K., Kabo-bah, A.T., Zwart, S.J.: Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis. Agric. Syst. 173, 172–208 (2019)CrossRefGoogle Scholar
  23. 23.
    Lyubchich, V., et al.: Insurance risk assessment in the face of climate change: Integrating data science and statistics. Wiley Interdiscip. Rev. Comput. Stat. 11(4) (2019)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Akhter, M.N., et al.: Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques. IET Renew. Power Gener. 13(7), 1009–1023 (2019)CrossRefGoogle Scholar
  25. 25.
    Al Tarhuni, B., et al.: Large scale residential energy efficiency prioritization enabled by machine learning. Energy Effic. (2019)Google Scholar
  26. 26.
    Mosavi, A., Ozturk, P., Chau, K.W.: Flood prediction using machine learning models: Literature review. Water 10(11) (2018) (Switzerland)CrossRefGoogle Scholar
  27. 27.
    Ponsero, A.J., Hurwitz, B.L., The promises and pitfalls of machine learning for detecting viruses in aquatic metagenomes. Front. Microbiol. 10(MAR) (2019)Google Scholar
  28. 28.
    Shen, C.: A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resour. Res. 54(11), 8558–8593 (2018)CrossRefGoogle Scholar
  29. 29.
    Zhu, X.X., et al.: Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources. IEEE Geosci. Remote. Sens. Mag. 5(4), 8–36 (2017)CrossRefGoogle Scholar
  30. 30.
    Aram, F., et al.: Design and validation of a computational program for analysing mental maps: Aram mental map analyzer. Sustainability 11(14) (2019) (Switzerland)CrossRefGoogle Scholar
  31. 31.
    Asadi, E., et al.: Groundwater quality assessment for drinking and agricultural purposes in Tabriz Aquifer, Iran. (2019)Google Scholar
  32. 32.
    Asghar, M. Z., Subhan, F., Imran, M., Kundi, F.M., Shamshirband, S., Mosavi, A., Csiba, P.R., Várkonyi-Kóczy, A.: Performance evaluation of supervised machine learning techniques for efficient detection of emotions from online content. Pre-prints (2019), 2019080019
  33. 33.
    Bemani, A., Baghban, A., Shamshirband, S., Mosavi, A., Csiba, P., Várkonyi-Kóczy, A.R.: Applying ANN, ANFIS, and LSSVM models for estimation of acid sol-vent solubility in supercritical CO2. Preprints (2019), 2019060055
  34. 34.
    Choubin, B., et al.: Snow avalanche hazard prediction using machine learning methods. J. Hydrol. 577 (2019)CrossRefGoogle Scholar
  35. 35.
    Choubin, B., et al.: An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096 (2019)CrossRefGoogle Scholar
  36. 36.
    Dehghani, M., et al.: Prediction of hydropower generation using Grey wolf optimization adaptive neuro-fuzzy inference system. Energies 12(2) (2019)CrossRefGoogle Scholar
  37. 37.
    Dineva, A., et al.: Review of soft computing models in design and control of rotating electrical machines. Energies 12(6) (2019)CrossRefGoogle Scholar
  38. 38.
    Dineva, A., et al.: Multi-label classification for fault diagnosis of rotating electrical machines. (2019)Google Scholar
  39. 39.
    Farzaneh-Gord, M., et al.: Numerical simulation of pressure pulsation effects of a snubber in a CNG station for increasing measurement accuracy. Eng. Appl. Comput. Fluid Mech. 13(1), 642–663 (2019)Google Scholar
  40. 40.
    Ghalandari, M., et al.: Investigation of submerged structures’ flexibility on sloshing frequency using a boundary element method and finite element analysis. Eng. Appl. Comput. Fluid Mech. 13(1), 519–528 (2019)Google Scholar
  41. 41.
    Ghalandari, M., et al.: Flutter speed estimation using presented differential quadrature method formulation. Eng. Appl. Comput. Fluid Mech. 13(1), 804–810 (2019)Google Scholar
  42. 42.
    Karballaeezadeh, N., et al.: Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road). Eng. Appl. Comput. Fluid Mech. 13(1), 188–198 (2019)Google Scholar
  43. 43.
    Menad, N.A., et al.: Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming. Eng. Appl. Comput. Fluid Mech. 13(1), 724–743 (2019)MathSciNetGoogle Scholar
  44. 44.
    Mohammadzadeh, S., et al.: Prediction of compression index of fine-grained soils using a gene expression programming model. Infrastructures 4(2), 26 (2019)CrossRefGoogle Scholar
  45. 45.
    Mosavi, A. Edalatifar, M.: A hybrid neuro-fuzzy algorithm for prediction of reference evapotranspiration, in lecture notes in networks and systems, p. 235–243. Springer (2019)Google Scholar
  46. 46.
    Mosavi, A., Lopez, A., Varkonyi-Koczy A.R.: Industrial applications of big data: State of the art survey. In: Luca, D., Sirghi, L., Costin, C. (eds.) p. 225–232. Springer (2018)Google Scholar
  47. 47.
    Mosavi, A., Rabczuk, T., Learning and intelligent optimization for material design innovation. Kvasov, D.E., et al. (eds.) p. 358–363. Springer (2017)Google Scholar
  48. 48.
    Mosavi, A., Rabczuk, T., Varkonyi-Koczy, A.R.: Reviewing the novel machine learning tools for materials design. Luca, D., Sirghi, L., Costin, C. (eds.) p. 50–58. Springer (2018)Google Scholar
  49. 49.
    Mosavi, A., et al.: State of the art of machine learning models in energy systems, a systematic review. Energies 12(7) 2019CrossRefGoogle Scholar
  50. 50.
    Mosavi, A., et al.: Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Eng. Appl. Comput. Fluid Mech. 13(1), 482–492 (2019)Google Scholar
  51. 51.
    Mosavi, A., Varkonyi-Koczy, A.R.: Integration of machine learning and optimization for robot learning. In: Jablonski, R., Szewczyk, R. (eds). p. 349–355. Springer (2017)Google Scholar
  52. 52.
    Nosratabadi, S., et al.: Sustainable business models: A review. Sustainability 11(6) (2019) (Switzerland)CrossRefGoogle Scholar
  53. 53.
    Qasem, S.N., et al.: Estimating daily dew point temperature using machine learning algorithms. Water 11(3) (2019) (Switzerland)CrossRefGoogle Scholar
  54. 54.
    Rezakazemi, M., Mosavi, A., Shirazian, S.: ANFIS pattern for molecular membranes separation optimization. J. Mol. Liq. 274, 470–476 (2019)CrossRefGoogle Scholar
  55. 55.
    Riahi-Madvar, H., et al.: Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry. Eng. Appl. Comput. Fluid Mech. 13(1), 529–550 (2019)Google Scholar
  56. 56.
    Shabani, S., Samadianfard, S., Taghi Sattari, M., Shamshirband, S., Mosavi, A., Kmet, T. R., Várkonyi-Kóczy, A.: Modeling daily pan evaporation in humid climates using gaussian process regression. Preprints (2019), 2019070351
  57. 57.
    Shamshirband, S., Hadipoor, M., Baghban, A., Mosavi, A., Bukor J., Annamaria, R., Varkonyi-Koczy, A.: Developing an ANFIS-PSO model to predict mercury emissions in combustion flue gases. Preprints (2019), 2019070165
  58. 58.
    Shamshirband, S., et al.: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng. Appl. Comput. Fluid Mech. 13(1), 91–101 (2019)Google Scholar
  59. 59.
    Shamshirband, S., Mosavi, A., Rabczuk, T., Particle swarm optimization model to predict scour depth around bridge pier (2014). arXiv preprint arXiv:1906.08863
  60. 60.
    Taherei Ghazvinei, P., et al.: Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Eng. Appl. Comput. Fluid Mech. 12(1), 738–749 (2018)Google Scholar
  61. 61.
    Torabi, M., et al.: A Hybrid clustering and classification technique for forecasting short-term energy consumption. Environ. Prog. Sustain. Energy 38(1), 66–76 (2019)CrossRefGoogle Scholar
  62. 62.
    Torabi, M., et al.: A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation, in Lecture Notes in Networks and Systems, p. 266–274. Springer (2019)Google Scholar
  63. 63.
    Fox, J.T., Magoulick, D.D.: Predicting hydrologic disturbance of streams using species occurrence data. Sci. Total Environ. 686, 254–263 (2019)CrossRefGoogle Scholar
  64. 64.
    Fan, J., et al.: Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric. For. Meteorol. 263, 225–241 (2018)CrossRefGoogle Scholar
  65. 65.
    Cai, Y., et al.: Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agric. For. Meteorol. 274, 144–159 (2019)CrossRefGoogle Scholar
  66. 66.
    Zewdie, G.K., et al.: Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data. Environ. Monit. Assess 191(7) (2019)Google Scholar
  67. 67.
    Kovačević, M., et al.: Application of artificial neural networks for hydrological modelling in karst. Gradjevinar 70(1), 1–10 (2018)Google Scholar
  68. 68.
    Ghimire, S., et al.: Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities. Remote Sens. Environ. 212, 176–198 (2018)CrossRefGoogle Scholar
  69. 69.
    Hu, R., et al.: Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method. J. Hydrol. 575, 911–920 (2019)CrossRefGoogle Scholar
  70. 70.
    Shen, R., et al.: Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. Int. J. Appl. Earth Obs. Geoinformation 79, 48–57 (2019)CrossRefGoogle Scholar
  71. 71.
    Shilon, I., et al.: Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data. Astropart. Phys. 105, 44–53 (2019)CrossRefGoogle Scholar
  72. 72.
    Matsuoka, D., et al.: Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model. Prog. Earth Planet. Sci. 5(1) (2018)Google Scholar
  73. 73.
    Xu, L., et al.: Simulation and prediction of hydrological processes based on firefly algorithm with deep learning and support vector for regression. Int. J. Parallel Emergent Distrib. Syst. (2019)Google Scholar
  74. 74.
    Ardabili, S., Mosavi, A., Mahmoudi, Mesri Gundoshmian, T, Nosratabadi, S., Var-konyi-Koczy, A.: Modelling temperature variation of mushroom growing hall using artificial neural networks. Preprints (2019)Google Scholar
  75. 75.
    Mesri Gundoshmian, T., Ardabili, S., Mosavi, A., Varkonyi-Koczy, A., Prediction of combine harvester performance using hybrid machine learning modeling and re-sponse surface methodology. Preprints (2019)Google Scholar
  76. 76.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Systematic review of deep learning and machine learning models in biofuels research. Preprints (2019)Google Scholar
  77. 77.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Advances in machine learning modeling reviewing hybrid and ensemble methods. Preprints (2019)Google Scholar
  78. 78.
    Ardabili, S., Mosavi, A., Varkonyi-Koczy, A.: Building Energy information: demand and consumption prediction with Machine Learning models for sustainable and smart cities. Preprints (2019)Google Scholar
  79. 79.
    Ardabili, S., Mosavi, A., Dehghani, M., Varkonyi-Koczy, A.: Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. Preprints (2019)Google Scholar
  80. 80.
    Mohammadzadeh D., Karballaeezadeh, N., Mohemmi, M., Mosavi, A., Varkonyi-Koczy A.: Urban train soil-structure interaction modeling and analysis. Preprints (2019)Google Scholar
  81. 81.
    Mosavi, A., Ardabili, S., Varkonyi-Koczy, A.: List of deep learning models. Preprints (2019)Google Scholar
  82. 82.
    Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S., Aram, F.: State of the art survey of deep learning and machine learning models for smart cities and urban sustainability. Preprints (2019)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Kalman Kando, Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Technical and Engineering Department, Faculty of Civil EngineeringVali-e-Asr University of RafsanjanRafsanjanIran
  5. 5.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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