Seasonal precipitation forecast based on artificial neural networks
(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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- author
- da Paz, Adriano Rolim ; Uvo, Cíntia Bertacchi LU ; Bravo, Juan Martín ; Collischonn, Walter and da Rocha, Humberto Ribeiro
- organization
- publishing date
- 2010-12-01
- 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
- 2022-03-02 17:21:42
@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}}, }