Spatiotemporal groundwater level forecasting and monitoring using a neural network-based approach in a semi arid zone
by Soumaya Hajji; Wafik Hachicha; Salem Bouri; Hamed Ben Dhia
International Journal of Hydrology Science and Technology (IJHST), Vol. 2, No. 4, 2012

Abstract: Forecasting the groundwater level (GWL) of an aquifer is one of the most important tasks in hydrogeology. Such forecasts prove vital to develop better exploitation strategies of water resources and to carry up-to-date groundwater monitoring. In this paper, an artificial neural network (ANN)-based approach, which was based on linear regression model, is presented to simulate and predict both temporal and spatial GWL. The novelty of the proposed ANN model lies in the use of a non-linear model toward the input variables and a linear one toward the coefficients. The proposed approach was applied to the Middle Miocene aquifer groundwater (MMAGW) of Hajeb Elyoun Jelma (HJ) multilayered hydro-system, central Tunisia. The investigation was based on a limited database set of 22 wells during the period between 1994 and 2010. The design of the appropriate ANN model over third-order linear regression model and the reverse GWL prediction were assisted by the neuro software. Then, an iterative water exploitation strategy for the period 2011 to 2013, was performed based on this GWL spatiotemporal ANN model and through various three-dimensional piezometric maps performed by kriging method. Promising results were found for forecasting and monitoring MMAGW in HJ basin.

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