ARTIFICIAL NEURAL NETWORK METHOD FOR ESTIMATION OF MISSING DATA

  • GHUGE H
  • REGULWAR D
N/ACitations
Citations of this article
15Readers
Mendeley users who have this article in their library.

Abstract

The availability of precipitation data plays important role for analysis of various systems required for design of water resources systems. The perfect measurements are not available always. The scientist/hydrologists come across the problem of missing data due to a variety of reasons. There may be various reasons of unavailability of data. Measurement of hydrologic variables (e.g. rainfall, stream flows, etc.) is prone to various instrumental/systematic, manual and random errors. In the current study, missing rainfall data is evaluated by using Artificial Neural Network Method. Historical precipitation data from 6 rain-gauge stations in the Maharashtra State, India, are used to train and test the ANN method and derive conclusions from the improvements in result given by ANN. Results suggest that ANN model can be work for estimation of missing data.

Cite

CITATION STYLE

APA

GHUGE, H. K., & REGULWAR, D. G. (2012). ARTIFICIAL NEURAL NETWORK METHOD FOR ESTIMATION OF MISSING DATA. International Journal of Advanced Technology in Civil Engineering, 248–251. https://doi.org/10.47893/ijatce.2012.1039

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free