Rainfall estimation based on artificial neural network (ANN) models for monsoon season
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Sprache:Englisch
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Format
Kopierschutz
Nein
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Text-to-Speech
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Erscheinungsdatum
24.10.2019
Verlag
GRINSeitenzahl
128 (Printausgabe)
Dateigröße
6702 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9783346043382
Master's Thesis from the year 2014 in the subject Geography / Earth Science - Meteorology, Aeronomy, Climatology, grade: 6.84, , course: Soil and Water Conservation Engineering, language: English, abstract: In this master thesis the author will estimate the rainfall based on artificial neural network (ANN) models for monsoon season. The accurate rainfall prediction is one of the greatest challenges in hydrology. Forecast of any natural and usual event call for information regarding its phase of occurrence as well as nature. In the present study, artificial neural network (ANN) with different activation functions has been employed, to estimate daily monsoon rainfall of Pusa, Samastipur, in Bihar, India.
The daily mean temperature, relative humidity, vapour pressure and rainfall data of period (1st June to 30th September) for years 1981-1989, 1992-1994, 1996-2002 and 2004-2008 were used for training and data for years 2009-2013 were used to test the models. The sensitivity analysis was carried out to identify the most significant parameter for daily rainfall prediction. The Neuro solution 5.0 software was used for designing of ANN models based on sigmoid axon and hyperbolic tangent axon activation functions. All the ANN networks were trained and tested with feed forward back propagation algorithm. The performance of the models were evaluated qualitatively by visual observation and quantitatively using different statistical and hydrological indices viz. mean square error, correlation coefficient, akaike's information criterion, coefficient of efficiency and pooled average relative error.
It was found that the performance of the ANN single hidden layer model based on sigmoid axon activation function is better than the ANN model based on hyperbolic tangent axon activation function. The best ANN models revealed that two days lag time was found to be satisfactory for set of inputs to the model. The sensitivity analysis indicated that the most significant input parameter besides rainfall itself is the vapour pressure in daily rainfall prediction for study area.
The daily mean temperature, relative humidity, vapour pressure and rainfall data of period (1st June to 30th September) for years 1981-1989, 1992-1994, 1996-2002 and 2004-2008 were used for training and data for years 2009-2013 were used to test the models. The sensitivity analysis was carried out to identify the most significant parameter for daily rainfall prediction. The Neuro solution 5.0 software was used for designing of ANN models based on sigmoid axon and hyperbolic tangent axon activation functions. All the ANN networks were trained and tested with feed forward back propagation algorithm. The performance of the models were evaluated qualitatively by visual observation and quantitatively using different statistical and hydrological indices viz. mean square error, correlation coefficient, akaike's information criterion, coefficient of efficiency and pooled average relative error.
It was found that the performance of the ANN single hidden layer model based on sigmoid axon activation function is better than the ANN model based on hyperbolic tangent axon activation function. The best ANN models revealed that two days lag time was found to be satisfactory for set of inputs to the model. The sensitivity analysis indicated that the most significant input parameter besides rainfall itself is the vapour pressure in daily rainfall prediction for study area.
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