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.
GHUGE, HARSHANAND K. and REGULWAR, D.G.
"ARTIFICIAL NEURAL NETWORK METHOD FOR ESTIMATION OF MISSING DATA,"
International Journal of Advanced Technology in Civil Engineering: Vol. 1
, Article 6.
Available at: https://www.interscience.in/ijatce/vol1/iss4/6