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Titel:

Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Tran Anh, Duong; Duc Dang, Thanh; Pham Van, Song
Abstract:
Rainfall 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 Artific...     »
Stichworte:
seasonal decomposition; artificial neural network; rainfall forecasting; model selection
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
J – Multidisciplinary Scientific Journal
Jahr:
2019
Band / Volume:
2
Heft / Issue:
1
Seitenangaben Beitrag:
65-83
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.3390/j2010006
WWW:
https://doi.org/10.3390/j2010006
Verlag / Institution:
Molecular Diversity Preservation International (MDPI)
Verlagsort:
Basel, Switzerland
E-ISSN:
2571-8800
Copyright Informationen:
Open Access. Copyright: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. (CC BY 4.0)
Format:
Text
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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