Data mining climatic for frost and deficit hidric forescast / Mineração de dados climaticos para previsão local de geada e deficiencia hidrica
AUTOR(ES)
Luciana Copas Bucene
DATA DE PUBLICAÇÃO
2008
RESUMO
The losses that occur in agriculture are high, mainly due to the occurrence of crop damages due to climatic events. Many times, the social and economic impacts caused by the damages are significant, since they involve factors such as the production and the price of foods. For example, coffee and sugarcane production in São Paulo State suffer alternations motivated by adverse climatic events and, in special, frost and drought, that greatly reduce the production. The purpose of this study is to identify relationships between climatic parameters, such as maximum temperature, minimum temperature, precipitation, etc., in order to discover eventual new knowledge, from known behavior of the climatic attributes already occurred in the past, with the objective of developing local frost and deficit water forecast models. To achieve this, data mining techniques were applied to climatic data bases. WEKA and the DISCOVER tools had been used and considered satisfactory, since they reached the objectives. The available databases were suitable for the accomplishment of the project, presenting enough volume of data and attributes so that it could generate results for the frost and water deficit forecast. Concerning to the results, with up to 1 day of antecedence to the frost, the generated model was considered trustworthy. From 2 days of antecedence to the frost the results present a reduction in the accuracy. For water deficit, results were differentiated, depending on the class. For the not class, from 1 to 15 days of antecedence to the event, the accuracy was high and acceptable. The strong class, following the not class, is the one that presents better results, falling down for the other classes. Up to 3 days of antecedence to the event water deficit and, depending on the month, the accuracy is acceptable. For 4 days or more in advance, the results showed that the generated model is not acceptable.
ASSUNTO(S)
arvore de decisão meteorologia agricola inteligencia artificial intelligence sytems decision tree aprendizado do computador artificial intelligence agricultura - previsão climatic alert agricultura - fatores climaticos
ACESSO AO ARTIGO
http://libdigi.unicamp.br/document/?code=000439290Documentos Relacionados
- Mineração de dados climáticos para previsão de geada e deficiência hídrica para as culturas do café e da cana-de-açúcar para o Estado de São Paulo.
- Mineração de dados de padrões climáticos sazonais usando a lógica paraconsistente
- Mineração de dados de padrões climáticos sazonais usando a lógica paraconsistente
- Técnicas de mineração de dados para análise de imagens
- Metodologia de mineração de dados para ambientes educacionais online