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Prediction of missing rainfall data using conventional and artificial neural network techniques

Roman, U.C. / Patel, P.L. / Porey, P.D.

Prediction of missing rainfall of rain gauge stations is essentially required for efficient rainfall-runoff modelling in a catchment. Existing conventional methods are being used for prediction of missing rainfall at a station using the data of the same duration from surrounding stations. Owing to its pattern recognition ability and understanding of nonlinear phenomena, an artificial neural network (ANN) has been used in the present study to develop a model for prediction of missing rainfall at a rain gauge station using past observed data of surrounding stations and the station for which part of time series were missing. The performance of the proposed ANN model (BPNN and LM algorithm) has been verified with existing conventional methods for two rain gauge stations for which part of rainfall time series were missing, in the Purna catchment of Tapi basin. The ANN model performed relatively better in rainfall prediction of both the stations for all time scales studied—daily, 10-day and monthly durations.

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Prediction of missing rainfall data using conventional and artificial neural network techniques

Roman, U.C. / Patel, P.L. / Porey, P.D.

Prediction of missing rainfall of rain gauge stations is essentially required for efficient rainfall-runoff modelling in a catchment. Existing conventional methods are being used for prediction of missing rainfall at a station using the data of the same duration from surrounding stations. Owing to its pattern recognition ability and understanding of nonlinear phenomena, an artificial neural network (ANN) has been used in the present study to develop a model for prediction of missing rainfall at a rain gauge station using past observed data of surrounding stations and the station for which part of time series were missing. The performance of the proposed ANN model (BPNN and LM algorithm) has been verified with existing conventional methods for two rain gauge stations for which part of rainfall time series were missing, in the Purna catchment of Tapi basin. The ANN model performed relatively better in rainfall prediction of both the stations for all time scales studied—daily, 10-day and monthly durations.

Prediction of missing rainfall data using conventional and artificial neural network techniques

Roman, U.C. / Patel, P.L. / Porey, P.D.

Prediction of missing rainfall of rain gauge stations is essentially required for efficient rainfall-runoff modelling in a catchment. Existing conventional methods are being used for prediction of missing rainfall at a station using the data of the same duration from surrounding stations. Owing to its pattern recognition ability and understanding of nonlinear phenomena, an artificial neural network (ANN) has been used in the present study to develop a model for prediction of missing rainfall at a rain gauge station using past observed data of surrounding stations and the station for which part of time series were missing. The performance of the proposed ANN model (BPNN and LM algorithm) has been verified with existing conventional methods for two rain gauge stations for which part of rainfall time series were missing, in the Purna catchment of Tapi basin. The ANN model performed relatively better in rainfall prediction of both the stations for all time scales studied—daily, 10-day and monthly durations.

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Titel:

Prediction of missing rainfall data using conventional and artificial neural network techniques

Beteiligte:

Roman, U.C. (Autor:in) / Patel, P.L. (Autor:in) / Porey, P.D. (Autor:in)

Erschienen in:

ISH Journal of Hydraulic Engineering ; 18 ; 224-231

Erscheinungsdatum:

01.09.2012

Format / Umfang:

8 pages

ISSN:

0971-5010 , 2164-3040

DOI:

https://doi.org/10.1080/09715010.2012.721660

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

rainfall-runoff modelling , missing rainfall , pattern recognition , artificial neural network , Tapi basin

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