Skip navigation


  • DSpace logo
  • Home
  • Collections
  • Researcher Profile
  • Explore by
    • Researcher Profile
  • VGU library
  • Help
  • User Guide
  • Sign on to:
    • My DSpace
    • Receive email
      updates
    • Edit Account details

VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/65
DC FieldValueLanguage
dc.contributor.authorDuong Tran Anhen_US
dc.contributor.authorDang Duc Thanhen_US
dc.contributor.authorPham Van Songen_US
dc.date.accessioned2020-03-12T16:54:12Z-
dc.date.available2020-03-12T16:54:12Z-
dc.date.issued2019-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/65-
dc.description.abstractRainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.en_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofJ.Multi-displinary Scientific Journalen_US
dc.relation.ispartofseriesVol. 2(1);65-83-
dc.subjectseasonal decompositionen_US
dc.subjectartificial neural networken_US
dc.subjectrainfall forecastingen_US
dc.subjectmodel selectionen_US
dc.titleImproved rainfall prediction using combined pre-processing methods and feed forward neural networksen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/j2010006-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1other-
Appears in Collections:ARTICLE/BOOK PUBLICATION
Show simple item record

Page view(s)

83
checked on Aug 23, 2025

Google ScholarTM

Check

Altmetric

Altmetric


Items are protected by © Copyright of Vietnamese - German University Library

© Copyright 2020 by Vietnamese - German University Library.
Add: Ring road 4, Quarter 4, Thoi Hoa Ward, Ben Cat City, Binh Duong Province
Tel.:(0274) 222 0990. Ext.: 70206