Title: A survey on rainfall forecasting using artificial neural network

Authors: Qi Liu; Yanyun Zou; Xiaodong Liu; Nigel Linge

Addresses: Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China; School of Computing, Edinburgh Napier University, Edinburgh, Scotland, UK ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China ' School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK ' The University of Salford, Salford, Greater Manchester, M5 4WT, UK

Abstract: Rainfall has a great impact on agriculture and people's daily travel, so accurate prediction of precipitation is well worth studying for researchers. Traditional methods like numerical weather prediction (NWP) models or statistical models can't provide satisfied effect of rainfall forecasting because of nonlinear and dynamic characteristics of precipitation. However, artificial neural network (ANN) has an ability to obtain complicated nonlinear relationship between variables, which is suitable to predict precipitation. This paper mainly introduces background knowledge of ANN and several algorithms using neural network applied to precipitation prediction in recent years. It is proved that neural network can greatly improve the accuracy and efficiency of prediction.

Keywords: rainfall; prediction; precipitation forecasting; artificial neural network; ANN; training algorithms; nonlinear relationship; embedded systems.

DOI: 10.1504/IJES.2019.098300

International Journal of Embedded Systems, 2019 Vol.11 No.2, pp.240 - 249

Received: 05 Sep 2017
Accepted: 04 May 2018
Published online: 13 Mar 2019 *

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