Estimation Analysis of Rainfall Based on Weather Radar Measurements

Mr. Deepak Yadav; Prof. Murlimanohar Hinnwar

Rainfall estimation based on radar measurements using neural network is a nonparametric method for representing the relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. Data collected by a Weather Surveillance Bhopal 2008-2012 and a rain gauge network were used to evaluate the performance of the adaptive network for rainfall estimation to provide parameters for a closed-form rain-rate probability distribution model. It is shown that the adaptive network can estimate rainfall fairly accurately. In this paper we are taking the last five years (2008-2012) data from weather radar data at Bhopal in M.P. Region, India. With the help of this data we are trained the neural network. This project has been addressed two approaches. a) we are estimating the five years rainfall data month wise basis, b)The second problem we prepared analysis of five years rainfall data and make rainfall prediction model so on the basis of 2008 to 2012 basis we are made 2013 model to predict the rainfall and also its estimation.

Select Volume / Issues:

• Volume 3 Issue 6, Nov. 2012

Year:

2012

Type of Publication:

Article

Keywords:

Rain gauge; ANN; Rain-rate probability; Radar rain data

Journal:

IJECCE

Volume:

3

Number:

6

Pages:

1449-1451

Month:

November