Advertisement

Emotional ANN (EANN): A New Generation of Neural Networks for Hydrological Modeling in IoT

  • Vahid Nourani
  • Amir Molajou
  • Hessam Najafi
  • Ali Danandeh MehrEmail author
Chapter
First Online:
Part of the Transactions on Computational Science and Computational Intelligence book series (TRACOSCI)

Abstract

Emotional artificial neural network (EANN) is a cutting-edge artificial intelligence method that has been used by researchers in the engineering and medical sciences over the recent years. First introduced in the 1999s, EANN is the combination of physiological and neural sciences for investigation of complex processes. Rainfall-runoff is a complex hydrological process that may be modeled by EANN methods to attain information about the response of a catchment to a rainfall event. In practice, the response is surface runoff either in the form of streamflow or flood in the catchment of interest. Thus, a reliable rainfall-runoff model is an inevitable component of a watershed so that decision-makers may use it to reduce the relevant vulnerability against extreme rainfall events. Undoubtedly, one way to empower the capabilities of rainfall-runoff models is the integration of recent achievements in the Internet of Things (IoT) with robust modeling algorithms such as EANN. Relying on the huge amount of knowledge within IoT components, the hybrid IoT-EANN can yield in the high-resolution space-time estimations of runoff that is a practical way to mitigate potential hazards of flooding through real time or in advance actions. With this chapter, we provide a short overview of the state-of-the-art EANN and its application in rainfall-runoff modeling. In addition, a concise review of the applications of IoT in hydro-environmental issues is provided. The chapter reveals that integrations of IoT with hydro-environmental studies are in their infancy. Being a new class of investigation, there is no hybrid rainfall-runoff model within the literature coupling IoT technology with artificial intelligence.

Keywords

Artificial intelligence Neural networks Emotion Runoff prediction Internet of things 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Abarghouei, H. B., & Hosseini, S. Z. (2016). Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates. Arabian Journal of Geosciences, 9(15), 663.CrossRefGoogle Scholar
  2. 2.
    Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., et al. (2012). Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Progress in Physical Geography, 36(4), 480–513.CrossRefGoogle Scholar
  3. 3.
    Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga-Zielinski, B., & Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resources Research, 48(1). https://doi.org/10.1029/2010WR009945.
  4. 4.
    Anmala, J., Zhang, B., & Govindaraju, R. S. (2000). Comparison of ANNs and empirical approaches for predicting watershed runoff. Journal of Water Resources Planning and Management, 126(3), 156–166.CrossRefGoogle Scholar
  5. 5.
    ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000). Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124–137.CrossRefGoogle Scholar
  6. 6.
    Bonala, S. (2009). A study on neural network based system identification with application to heating, ventilating and air conditioning (hvac) system (MSc dissertation). National Institute of Technology, Rourkela.Google Scholar
  7. 7.
    Chang, F. J., & Tsai, M. J. (2016). A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques. Journal of Hydrology, 535, 256–269.CrossRefGoogle Scholar
  8. 8.
    Chau, K. W., & Wu, C. L. (2010). A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics, 12(4), 458–473.CrossRefGoogle Scholar
  9. 9.
    Danandeh Mehr, A., & Kahya, E. (2017). Climate change impacts on catchment-scale extreme rainfall variability: Case study of Rize Province, Turkey. Journal of Hydrologic Engineering, 22(3), 05016037. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001477.CrossRefGoogle Scholar
  10. 10.
    Danandeh Mehr, A., Kahya, E., & Olyaie, E. (2013). Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. Journal of Hydrology, 505, 240–249.CrossRefGoogle Scholar
  11. 11.
    Danandeh Mehr, A., Nourani, V., Hrnjica, B., & Molajou, A. (2017). A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events. Journal of Hydrology, 555, 397–406. https://doi.org/10.1016/j.jhydrol.2017.10.039.CrossRefGoogle Scholar
  12. 12.
    Danandeh, Mehr, A., Nourani, V., Khosrowshahi, V. K., & Ghorbani, M. A. (2018). A hybrid support vector regression–firefly model for monthly rainfall forecasting. International journal of Environmental Science and Technology, 1–12.Google Scholar
  13. 13.
    Danandeh Mehr, A., Kahya, E., Şahin, A., & Nazemosadat, M. J. (2015). Successive-station monthly streamflow prediction using different artificial neural network algorithms. International journal of Environmental Science and Technology, 12(7), 2191–2200.CrossRefGoogle Scholar
  14. 14.
    Dawson, C. W., & Wilby, R. L. (2001). Hydrological modelling using artificial neural networks. Progress in Physical Geography, 25(1), 80–108.CrossRefGoogle Scholar
  15. 15.
    Dlodlo, N., & Kalezhi, J. (2015). The internet of things in agriculture for sustainable rural development. In Emerging Trends in Networks and Computer Communications (ETNCC), 2015 international conference on (pp. 13–18). IEEE.Google Scholar
  16. 16.
    Du, K., Mu, C., Deng, J., & Yuan, F. (2013). Study on atmospheric visibility variations and the impacts of meteorological parameters using high temporal resolution data: An application of environmental internet of things in China. International Journal of Sustainable Development & World Ecology, 20(3), 238–247.CrossRefGoogle Scholar
  17. 17.
    Fang, S., Xu, L., Zhu, Y., Liu, Y., Liu, Z., Pei, H., et al. (2015). An integrated information system for snowmelt flood early-warning based on internet of things. Information Systems Frontiers, 17(2), 321–335.CrossRefGoogle Scholar
  18. 18.
    Fellous, J. M. (1999). Neuromodulatory basis of emotion. The Neuroscientist, 5(5), 283–294.CrossRefGoogle Scholar
  19. 19.
    Haykin, S. (1994). Neural networks: A comprehensive foundation. Upper Saddle River: Prentice Hall PTR.zbMATHGoogle Scholar
  20. 20.
    Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366.zbMATHCrossRefGoogle Scholar
  21. 21.
    González-Briones, A., Castellanos-Garzón, J. A., Mezquita Martín, Y., Prieto, J., & Corchado, J. M. (2018). A framework for knowledge discovery from wireless sensor networks in rural environments: A crop irrigation systems case study. Wireless Communications and Mobile Computing, 2018, 1.Google Scholar
  22. 22.
    Hsu, K. L., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10), 2517–2530.CrossRefGoogle Scholar
  23. 23.
    Jain, A., & Srinivasulu, S. (2006). Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. Journal of Hydrology, 317(3–4), 291–306.CrossRefGoogle Scholar
  24. 24.
    Khan, R., Khan, S. U., Zaheer, R., & Khan, S..(2012). Future internet: the internet of things architecture, possible applications and key challenges. In Frontiers of Information Technology (FIT), 2012 10th International Conference on (pp. 257–260). IEEE.Google Scholar
  25. 25.
    Khashman, A. (2008). A modified backpropagation learning algorithm with added emotional coefficients. IEEE Transactions on Neural Networks, 19(11), 1896–1909.CrossRefGoogle Scholar
  26. 26.
    Kisi, O., & Cimen, M. (2011). A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399(1–2), 132–140.CrossRefGoogle Scholar
  27. 27.
    Kisi, O., & Shiri, J. (2011). Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resources Management, 25(13), 3135–3152.CrossRefGoogle Scholar
  28. 28.
    Kuo, C. C., Gan, T. Y., & Yu, P. S. (2010). Wavelet analysis on the variability, teleconnectivity, and predictability of the seasonal rainfall of Taiwan. Monthly Weather Review, 138(1), 162–175.CrossRefGoogle Scholar
  29. 29.
    Lewin, D. I. (2001). Why is that computer laughing? IEEE Intelligent Systems, 16(5), 79–81.CrossRefGoogle Scholar
  30. 30.
    Lotfi, E., & Akbarzadeh-T, M. R. (2014). Practical emotional neural networks. Neural Networks, 59, 61–72.CrossRefGoogle Scholar
  31. 31.
    Lotfi, E., & Akbarzadeh-T, M. R. (2016). A winner-take-all approach to emotional neural networks with universal approximation property. Information Sciences, 346, 369–388.CrossRefGoogle Scholar
  32. 32.
    Maier, H. R., & Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications. Environmental Modelling & Software, 15(1), 101–124.CrossRefGoogle Scholar
  33. 33.
    Moren, J. (2002). Emotion and learning: a computational model of theamygdala, PhD Thesis, Lund university, Lund, Sweden.Google Scholar
  34. 34.
    Moustris, K. P., Larissi, I. K., Nastos, P. T., & Paliatsos, A. G. (2011). Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resources Management, 25(8), 1979–1993.CrossRefGoogle Scholar
  35. 35.
    Nasseri, M., Asghari, K., & Abedini, M. J. (2008). Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3), 1415–1421.CrossRefGoogle Scholar
  36. 36.
    Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291(1–2), 52–66.CrossRefGoogle Scholar
  37. 37.
    Nourani, V. (2017). An emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, 544, 267–277.CrossRefGoogle Scholar
  38. 38.
    Nourani, V., & Molajou, A. (2017). Application of a hybrid association rules/decision tree model for drought monitoring. Global and Planetary Change, 159, 37–45.CrossRefGoogle Scholar
  39. 39.
    Nourani, V., Alami, M. T., & Aminfar, M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(3), 466–472.CrossRefGoogle Scholar
  40. 40.
    Nourani, V., Komasi, M., & Alami, M. T. (2011). Hybrid wavelet–genetic programming approach to optimize ANN modeling of rainfall–runoff process. Journal of Hydrologic Engineering, 17(6), 724–741.CrossRefGoogle Scholar
  41. 41.
    Nourani, V., Khanghah, T. R., & Baghanam, A. H. (2015). Application of entropy concept for input selection of wavelet-ANN based rainfall-runoff modeling. Journal of Environmental Informatics, 26(1), 52–70.Google Scholar
  42. 42.
    Nourani, V., Sattari, M. T., & Molajou, A. (2017). Threshold-based hybrid data mining method for long-term maximum precipitation forecasting. Water Resources Management, 31(9), 2645–2658.CrossRefGoogle Scholar
  43. 43.
    Perlovsky, L. I. (2006). Toward physics of the mind: Concepts, emotions, consciousness, and symbols. Physics of Life Reviews, 3(1), 23–55.MathSciNetCrossRefGoogle Scholar
  44. 44.
    Picard, R. W. (1997). Affective computing. Cambridge, MA: MIT Press.Google Scholar
  45. 45.
    Pongracz, R., Bartholy, J., & Bogardi, I. (2001). Fuzzy rule-based prediction of monthly precipitation. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(9), 663–667.CrossRefGoogle Scholar
  46. 46.
    Rahman, M. A., Milasi, R. M., Lucas, C., Araabi, B. N., & Radwan, T. S. (2008). Implementation of emotional controller for interior permanent-magnet synchronous motor drive. IEEE Transactions on Industry Applications, 44(5), 1466–1476.CrossRefGoogle Scholar
  47. 47.
    Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101, 63–80.CrossRefGoogle Scholar
  48. 48.
    Salas, J. D., Delleur, J. W., Yevjevich, V., & Lane, W. L. (1980). Applied modeling of hydrological time series. Littleton, CO: Water Resource.Google Scholar
  49. 49.
    Sang, Y. F. (2013). Improved wavelet modeling framework for hydrologic time series forecasting. Water Resources Management, 27(8), 2807–2821.CrossRefGoogle Scholar
  50. 50.
    Sanyal, J., & Lu, X. X. (2006). GIS-based flood hazard mapping at different administrative scales: A case study in Gangetic West Bengal, India. Singapore Journal of Tropical Geography, 27(2), 207–220.CrossRefGoogle Scholar
  51. 51.
    Sharghi, E., Nourani, V., Najafi, H., & Molajou, A. (2018). Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process. Water Resources Management, 32(10), 3441–3456.CrossRefGoogle Scholar
  52. 52.
    Shenan, Z. F., Marhoon, A. F., & Jasim, A. A. (2017). IoT based intelligent greenhouse monitoring and control system. Basrah Journal for Engineering Sciences, 1(17), 61–69.Google Scholar
  53. 53.
    Shiri, J., & Kisi, O. (2010). Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. Journal of Hydrology, 394(3–4), 486–493.CrossRefGoogle Scholar
  54. 54.
    Uckelmann, D., Harrison, M., & Michahelles, F. (2011). An architectural approach towards the future internet of things. In Architecting the internet of things (pp. 1–24). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  55. 55.
    World Meteorological Organization (WMO), (2012). Guidelines on Ensemble Prediction Systems and Forecasting Report WMO-No. 1091, Switzerland.Google Scholar
  56. 56.
    Xiaoying, S., & Huanyan, Q. (2011). Design of wetland monitoring system based on the internet of things. Procedia Environmental Sciences, 10, 1046–1051.CrossRefGoogle Scholar
  57. 57.
    Zhang, Q., Wang, B. D., He, B., Peng, Y., & Ren, M. L. (2011). Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting. Water Resources Management, 25(11), 2683–2703.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Vahid Nourani
    • 1
  • Amir Molajou
    • 2
  • Hessam Najafi
    • 1
  • Ali Danandeh Mehr
    • 3
    Email author
  1. 1.Department of Water Resources Engineering, Faculty of Civil EngineeringUniversity of TabrizTabrizIran
  2. 2.Department of Water Resources Engineering, Faculty of Civil EngineeringIran University of Science & TechnologyTehranIran
  3. 3.Department of Civil Engineering, Faculty of EngineeringAntalya Bilim UniversityAntalyaTurkey

Personalised recommendations

Cite chapter

Buy options