Abstract

The onset, withdrawal and quantity of rainfall greatly influence the agricultural yield, economy, water resources, power generation and ecosystem. Hence, if the variations in rainfall are known well in advance, it would be possible to reduce the adverse impacts related to excess or deficient rainfall, providing us prior information about droughts and floods. The principles of stochastic processes have been increasingly and successfully applied in the past three decades to model many of the hydrological processes which are stochastic in nature. Time series modelling has been extensively used in stochastic hydrology for predicting hydrological processes like rainfall and runoff. Time lagged models extract maximum possible information from the available record for forecasting. In this study, rainfall forecast of Navsari was attempted using rainfall training data of 30 years (1983-2012) and runoff was estimated using curve number method for each micro watershed of coastal Navsari. The models used for forecasting rainfall, based on the time series data, were seasonal ARIMA model and ANN multilayer perceptron model. A seasonal ARIMA model, based on Box Jenkins method model was built by determining its appropriate parameters and the ANN model was built using Levenberg Marquardt algorithm. The seasonal ARIMA model was determined using four steps namely identification of autoregressive and moving average terms, estimating the parameters using maximum likelihood method, diagnostic checking of residuals and forecasting. A number of ANN model structures were tested and the appropriate ANN model was selected based on its performance on validation data after which the model was deployed for forecasting. It was concluded that the non linear ANN model was superior to linear ARIMA model in terms of its forecasting accuracy tested on validation data. The predicted rainfall was then utilized to estimate the runoff using Curve number method for each micro watershed of Navsari coast.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.