Comparative analysis of performance of neural network and neuro-fuzzy model in prediction of groundwater table fluctuation
by P.R. Maiti; Medha Jha; Sabita Madhvi Singh
International Journal of Hydrology Science and Technology (IJHST), Vol. 2, No. 3, 2012

Abstract: Ground water is underlying natural resources widely distributed under the ground and not visible from the earth surface. Prediction of state of groundwater table is very important for water resources planning and management. To estimate this valuable natural resource, indirect methods have been adopted since past decades. An artificial neural network (ANN) and neuro-fuzzy network (NFN) model both does not require the knowledge of internal details of the hydrological processes to predict the nature of the system are applied here to predict groundwater depth before and after monsoon in villages near Jabalpur, Madhya Pradesh, India. The ground water level prediction procedure has been developed in this study incorporating all possible influencing parameters such as mean annual rainfall, temperature, evaporation, infiltration capacity using artificial intelligence tools. Predicting capabilities and performance of ANN and NFN are presented for pre-monsoon and post-monsoon ground water level in terms of squared error.

Online publication date: Sat, 16-Aug-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Hydrology Science and Technology (IJHST):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com