Solar flare forecasting model supported with artificial neural network techniques

被引：46
|
作者：
Wang, H. N. [1 ]
Cui, Y. M. [1 ]
Li, R. [1 ]
Zhang, L. Y. [1 ]
Han, H. [1 ]
机构：
[1] Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
来源：
ADVANCES IN SPACE RESEARCH | 2008年 / 42卷 / 09期
关键词：
Solar magnetic field; Solar activity; Forecast;
D O I：
10.1016/j.asr.2007.06.070
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
Nowadays operational models for solar activity forecasting are still based on the statistical relationship between solar activity and solar magnetic field evolution. In order to set up this relationship, many parameters have been proposed to be the measures. Conventional measures are based on the sunspot group classification which provides limited information from sunspots. For this reason, new measures based on solar magnetic field observations are proposed and a solar flare forecasting model supported with an artificial neural network is introduced. This model is equivalent to a person with a long period of solar flare forecasting experience. (C) 2007 COSPAR. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：1464 / 1468
页数：5
相关论文
共 50 条
  • [41] Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
    Ramos-Perez, Eduardo
    Alonso-Gonzalez, Pablo J.
    Javier Nunez-Velazquez, Jose
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2019, 129 : 1 - 9
  • [42] Artificial Neural Network Model for Forecasting the Stock Price of Indian IT Company
    Sen, Joydeep
    Das, Arup K.
    [J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2012), 2014, 236 : 1153 - 1159
  • [43] An artificial neural network (p, d, q) model for timeseries forecasting
    Khashei, Mehdi
    Bijari, Mehdi
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (01) : 479 - 489
  • [44] Based on Artificial Neural Network Model of Regional Logistics Demand Forecasting
    Yang, Shu-xia
    Cui, Dan
    [J]. 3RD INTERNATIONAL CONFERENCE ON EDUCATION REFORM AND MODERN MANAGEMENT, 2016, 2016, : 151 - 155
  • [45] A dynamic artificial neural network model for forecasting time series events
    Ghiassi, M
    Saidane, H
    Zimbra, DK
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2005, 21 (02) : 341 - 362
  • [46] Artificial neural network model as a potential alternative for groundwater salinity forecasting
    Banerjee, Pallavi
    Singh, V. S.
    Chatttopadhyay, Kausik
    Chandra, P. C.
    Singh, Bhoop
    [J]. JOURNAL OF HYDROLOGY, 2011, 398 (3-4) : 212 - 220
  • [47] Artificial neural network model for river flow forecasting in a developing country
    Shamseldin, Asaad Y.
    [J]. JOURNAL OF HYDROINFORMATICS, 2010, 12 (01) : 22 - 35
  • [48] Urban water demand forecasting with a dynamic artificial neural network model
    Ghiassi, M.
    Zimbra, David K.
    Saidane, H.
    [J]. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE, 2008, 134 (02): : 138 - 146
  • [49] Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model
    Ali, Zulifqar
    Hussain, Ijaz
    Faisal, Muhammad
    Nazir, Hafiza Mamona
    Hussain, Tajammal
    Shad, Muhammad Yousaf
    Shoukry, Alaa Mohamd
    Gani, Showkat Hussain
    [J]. ADVANCES IN METEOROLOGY, 2017, 2017
  • [50] Wavelet-artificial neural network model for water level forecasting
    Nguyen Thi Ngoc Anh
    Nguyen Quang Dat
    Nguyen Thi Van
    Nguyen Ngoc Doanh
    Ngo Le An
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON RESEARCH IN INTELLIGENT AND COMPUTING IN ENGINEERING (RICE III), 2018,
12345