Forecasting Bangladeshi monsoon rainfall using neural network and genetic algorithm approaches

DOI

10.2991/itmr.2009.2.1.1How to use a DOI?

Keywords

Atmospheric dynamics, forecasting, artificial intelligence, neural network, fuzzy inference system, genetic algorithm

Abstract

True information about rainfall is crucial for human activities such as the use and the management of water resources, hydroelectric power projects, warning to impend droughts or floods, urban areas sewer systems and many others. This paper investigates the development of an efficient model to forecast monthly monsoon rainfall for a number of stations, namely Barishal, Chittagong, Dhaka, Khulna, Rajshahi and Sylhet. It is believed that rainfall forecasting is difficult and also a challenging task for anyone because rainfall data are multidimensional and nonlinear. Therefore, to model rainfall data, the AI forecasting models, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and genetic algorithm (GA) have been used. Results found by the AI models are also compared to the linear regression model to show advantages of selecting these models.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Shipra Banik
AU  - Mohammed Anwer
AU  - A.F.M. Khodadad Khan
AU  - Rifat Ara Rouf
AU  - Farah Habib Chanchary
PY  - 2009
DA  - 2009/10/01
TI  - Forecasting Bangladeshi monsoon rainfall using neural network and genetic algorithm approaches
JO  - The International Technology Management Review
SP  - 1
EP  - 18
VL  - 2
IS  - 1
SN  - 1835-5269
UR  - https://doi.org/10.2991/itmr.2009.2.1.1
DO  - 10.2991/itmr.2009.2.1.1
ID  - Banik2009
ER  -