This paper is published in Volume-4, Issue-3, 2018
Area
Artificial Intelligence Techniques in Hydrology
Author
Vikas Poonia
Co-authors
Dr. H. L. Tiwari, Dr. Satanand Mishra
Org/Univ
Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India
Pub. Date
07 May, 2018
Paper ID
V4I3-1310
Publisher
Keywords
Artificial neural network (ANN), Feedforward, Hydrology, Precipitation, Rainfall-runoff, Stream-flow.

Citationsacebook

IEEE
Vikas Poonia, Dr. H. L. Tiwari, Dr. Satanand Mishra. Hydrological Analysis by Artificial Neural Network: A Review, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Vikas Poonia, Dr. H. L. Tiwari, Dr. Satanand Mishra (2018). Hydrological Analysis by Artificial Neural Network: A Review. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Vikas Poonia, Dr. H. L. Tiwari, Dr. Satanand Mishra. "Hydrological Analysis by Artificial Neural Network: A Review." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

In this paper, a deep review is conducted on Artificial Neural Network. ANN is used for real-world problems which are related to the hydrological field. Computational Intelligence methods such as Artificial Neural Network are very necessary because conventional methods are very complex and vexatious. Artificial Intelligence operation is based on the transformation of unknown relationship into the known sensible relationship, and hence this transformation helps in modelling real-world problems. Various applications of AI operation are carried out at present time, such as Rainfall-Runoff modelling, Groundwater modelling, water quality modelling, modelling stream flow etc. In recent years, Artificial Neural Network has shown exceptional performance as regression tools, especially when it is used for pattern recognition and function estimation. This paper mainly focuses on various ANN models for solving real and complex hydrological problems with great accuracy, and these are proposed as efficient tools for prediction in hydrology.
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