Rainfall Forecast of a Synoptic Station using Artificial Neural Network

HASAN, ALMASI and SAEID, FAZLI (2015) Rainfall Forecast of a Synoptic Station using Artificial Neural Network. In: Third International Conference on Advances in Computing, Electronics and Communication - ACEC 2015, 10-11 October, 2015, Zurich, Switzerland.

[img]
Preview
Text
20151023_065840.pdf - Published Version

Download (712kB) | Preview
Official URL: https://www.seekdl.org/conferences/paper/details/6...

Abstract

In this paper, we have utilized ANN (Artificial Neural Network) modeling for forecasting rainfall in Babolsar synoptic station in Iran. To achieve such a model, we have used daily rainfall data from 1978 to 2007 for the synoptic station. A Time Delay Series Neural Network is used in this work. Two hidden layers are considered for the neural network. Inputs are daily rainfall with variable lag to achieve optimal prediction. Using this method as a black box model, we have realized the hidden dynamics of rainfall through the past information of the system. Root Mean Square Error (RMSE) and Correlation Coefficient (r2) are evaluated for comparison purposes. Optimum delay days are calculated for rain forecasting which can be used in climatology application.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Daily Rainfall Forecasting, Back Propagation Neural Network, Time Delay Series, optimum lag.
Depositing User: Mr. John Steve
Date Deposited: 19 Apr 2019 12:06
Last Modified: 19 Apr 2019 12:06
URI: http://publications.theired.org/id/eprint/1387

Actions (login required)

View Item View Item