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Infilling of missing daily rainfall records using artificial neural network
Precipitation is a major factor controlling the hydrology of a region. It has been experienced that the precipitation data are sometimes lost by virtue of instrument malfunctioning, human error(s), climatic conditions which becomes an impediment in the hydrologic and hydraulic designs where rainfall series is a major input. Such missing values could be retrieved using estimated value by employing a large number of conventional hard computing techniques such as normal ratio method, inverse distance value method to name a few. The present work deals with estimation of missing daily rainfall values at 11 rain gauge stations in Pune District of India using soft computing technique of artificial neural networks and by coefficient of correlation weighing method. According to the correlations of averages of all stations, different groups comprising of 3–4 stations each were formed. Models were developed for each group to estimate daily rainfall values at any one station as output while data of other stations in that network were used as an input. This was repeated for all the stations one by one. All the models exhibit reasonable performances as evident from high values of correlation coefficient and low values of error measures.
Infilling of missing daily rainfall records using artificial neural network
Precipitation is a major factor controlling the hydrology of a region. It has been experienced that the precipitation data are sometimes lost by virtue of instrument malfunctioning, human error(s), climatic conditions which becomes an impediment in the hydrologic and hydraulic designs where rainfall series is a major input. Such missing values could be retrieved using estimated value by employing a large number of conventional hard computing techniques such as normal ratio method, inverse distance value method to name a few. The present work deals with estimation of missing daily rainfall values at 11 rain gauge stations in Pune District of India using soft computing technique of artificial neural networks and by coefficient of correlation weighing method. According to the correlations of averages of all stations, different groups comprising of 3–4 stations each were formed. Models were developed for each group to estimate daily rainfall values at any one station as output while data of other stations in that network were used as an input. This was repeated for all the stations one by one. All the models exhibit reasonable performances as evident from high values of correlation coefficient and low values of error measures.
Infilling of missing daily rainfall records using artificial neural network
Londhe, Shreenivas (author) / Dixit, Pradnya (author) / Shah, Shalaka (author) / Narkhede, Shweta (author)
ISH Journal of Hydraulic Engineering ; 21 ; 255-264
2015-09-02
10 pages
Article (Journal)
Electronic Resource
English
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