Bootstrap-based artificial neural network analysis for estimation of daily sediment yield from a small agricultural watershed
by Gurjeet Singh; Rabindra K. Panda
International Journal of Hydrology Science and Technology (IJHST), Vol. 5, No. 4, 2015

Abstract: Accurate estimation of sediment yield from watershed and its sub-watersheds is a prerequisite for effective watershed management. Reported study was undertaken in a small agricultural watershed namely Kapgari in Eastern India for estimation of daily sediment yield. On the basis of drainage network and land topography, the watershed was subdivided into three sub-watersheds. Bootstrap technique was used to develop unbiased artificial neural network (ANN) models to estimate the daily sediment yield with limited quantum of continuously monitored sediment yield data from the watershed. Bootstrap-based artificial neural network (BANN) were developed using only major weather variables such as rainfall and temperature for estimation of daily sediment yield. Results illustrate that the highest coefficient of simulation efficiency values of 0.887, 0.869, 0.904 and 0.898 for estimation of daily sediment yield from watershed and its sub-watersheds were observed by addition of one day lag rainfall and present day maximum and minimum temperature with present day rainfall.

Online publication date: Thu, 22-Oct-2015

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