Artificial Neural Network

Part of the SpringerBriefs in Water Science and Technology book series (BRIEFSWATER)


Artificial Neural network or ANN is a very popular method for predictive or optimization or simulation objectives. ANN mimics the human nervous system to solve problems in a parallel manner. ANN are known to be adaptable with situations, flexible with data and efficient enough for predicting any kind of problems. The limitation of ANN lies into the overdependence on data for learning the problem. Also there is no specific rule for the selection of activation function and number of hidden layers. However the application of ANN is still growing and various new forms of ANN is now utilized to solve problems from engineering, science as well as literature. The new methods mainly tries to solve the above discussed limitations by merging ANN with other or developing completely new algorithms.


Feedforward neural networks Training algorithms Hidden layers Classification of neural networks 


  1. Dehghani M, Saghafian B, Nasiri Saleh F, Farokhnia A, Noori R (2014) Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation. Int J Climatol 34:1169–1180. doi: 10.1002/joc.3754 CrossRefGoogle Scholar
  2. Jain A, Varshney AK, Joshi UC (2001) Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks, Water Resour Manage 15(5):291–321Google Scholar
  3. Razavi BS (2014) Predicting the trend of land use changes using artificial neural network and markov chain model (case study: Kermanshah city). Res J Env Earth Sci 6(4):215–226Google Scholar
  4. Mukherjee S, Veer V (2014) Water resource management in a part of Hindon basin, India using artificial neural networking and image processing. Int J Innovations Adv Comput Sci 3(4)Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  1. 1.National Institute of Technology AgartalaAgartalaIndia

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