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Seasonal precipitation forecast based on artificial neural networks

da Paz, Adriano Rolim ; Uvo, Cíntia Bertacchi LU ; Bravo, Juan Martín ; Collischonn, Walter and da Rocha, Humberto Ribeiro (2010) p.326-354
Abstract

Agriculture is vulnerable to the interannual climate variability and to its unpredictability, in such a way that most agricultural decisions taken within the time horizon of several months are made in a conservative manner, supposing a near-pessimist scenario. The improvement of climate prediction may help the strategic view, mitigating unwanted impacts and taking advantage of favorable conditions. This chapter presents the development of an Artificial Neural Network (ANN) model for seasonal precipitation forecast based on climate indices, focusing on the practical aspects of selecting the best predictors, defining ANN architecture, data handling and ANN training and validation. The study case is the Pardo/Mogi-Guaçu rivers watershed in... (More)

Agriculture is vulnerable to the interannual climate variability and to its unpredictability, in such a way that most agricultural decisions taken within the time horizon of several months are made in a conservative manner, supposing a near-pessimist scenario. The improvement of climate prediction may help the strategic view, mitigating unwanted impacts and taking advantage of favorable conditions. This chapter presents the development of an Artificial Neural Network (ANN) model for seasonal precipitation forecast based on climate indices, focusing on the practical aspects of selecting the best predictors, defining ANN architecture, data handling and ANN training and validation. The study case is the Pardo/Mogi-Guaçu rivers watershed in Brazil, which is characterized by intense sugarcane plantation for both ethanol and sugar industries. The results demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Computational Methods for Agricultural Research : Advances and Applications - Advances and Applications
pages
29 pages
publisher
IGI Global
external identifiers
  • scopus:84900586298
ISBN
9781616928711
DOI
10.4018/978-1-61692-871-1.ch016
language
English
LU publication?
yes
id
6a14beec-1d32-4460-b701-ca7b098e5cc5
date added to LUP
2018-11-01 12:21:05
date last changed
2020-01-13 01:07:38
@inbook{6a14beec-1d32-4460-b701-ca7b098e5cc5,
  abstract     = {<p>Agriculture is vulnerable to the interannual climate variability and to its unpredictability, in such a way that most agricultural decisions taken within the time horizon of several months are made in a conservative manner, supposing a near-pessimist scenario. The improvement of climate prediction may help the strategic view, mitigating unwanted impacts and taking advantage of favorable conditions. This chapter presents the development of an Artificial Neural Network (ANN) model for seasonal precipitation forecast based on climate indices, focusing on the practical aspects of selecting the best predictors, defining ANN architecture, data handling and ANN training and validation. The study case is the Pardo/Mogi-Guaçu rivers watershed in Brazil, which is characterized by intense sugarcane plantation for both ethanol and sugar industries. The results demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices.</p>},
  author       = {da Paz, Adriano Rolim and Uvo, Cíntia Bertacchi and Bravo, Juan Martín and Collischonn, Walter and da Rocha, Humberto Ribeiro},
  booktitle    = {Computational Methods for Agricultural Research : Advances and Applications},
  isbn         = {9781616928711},
  language     = {eng},
  month        = {12},
  pages        = {326--354},
  publisher    = {IGI Global},
  title        = {Seasonal precipitation forecast based on artificial neural networks},
  url          = {http://dx.doi.org/10.4018/978-1-61692-871-1.ch016},
  doi          = {10.4018/978-1-61692-871-1.ch016},
  year         = {2010},
}