Hydrological Data Network Modelling using Artificial Neural Network in Betwa Catchment
Ritu Ahlawat

Ritu Ahlawat, Department of Geography, Miranda House, University of Delhi, Delhi, India.
Manuscript received on December 08, 2014. | Revised Manuscript received on December 15, 2014. | Manuscript published on January 05, 2014. | PP: 132-134 | Volume-3 Issue-6, January 2014. | Retrieval Number: F2019013614/2014©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Design of hydrological data network depends not only on physical parameters but also uncertainty in volume and flow of rainfall and runoff. Bias in uncertainty level can’t be removed fully, hence use of artificial neural network (ANN) based on training of past dataset can provide useful insight in determination of optimal network. In this paper, an attempt has been made to use the power of soft computing in terms of ANN based analysis of rainfall data of Betwa river catchment. Genetic feed forward algorithm and sensitivity analysis of mean data was done in EXCEL based version of NeuroSolution Software. Minimum spatial error in rainfall values of catchment provided clues about location of stations.
Keywords: Spatial error, sensitivity, hydrological data network, uncertainty in rainfall