51 resultados para Multi variate analysis


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The use of geographic information systems (GIS), combined with advanced analysis technique, enables the standardization and data integration, which are usually from different sources, allowing you to conduct a joint evaluation of the same, providing more efficiency and reliability in the decision-making process to promote the adequacy of land use. This study aimed to analyze the priority areas of the basin agricultural use of the Capivara River, Botucatu, SP, through multicriterial analysis, aiming at conservation of water resources. The results showed that the Geographic Information System Idrisi Selva combined with advanced analysis technique and the weighted linear combination method proved to be an effective tool in the combination of different criteria, allowing the determination of the adequacy of agricultural land use less subjective way. Environmental criteria were shown to be suitable for the combination and multi-criteria analysis, allowing the preparation of the statement of suitability classes for agricultural use and can be useful for regional planning and decision-making by public bodies and environmental agents because the method takes into account the rational use of land and allowing the conservation of hydrics resources.

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From the characterization of biophysical attributes of the watershed (slope, soil types, capacity to land use and land cover), this article, used the multi-criteria analysis method – Weighted Linear Combination, defined priority areas for adaptation to the use of land as to its capacity of use. With this methodological approach, were created for the watershed under study, four classes, formed by different combinations of biophysical attributes (discrete data), representing levels of priorities for agricultural land use. The Multicriteria Evaluation in a GIS is suitable for the mapping of priority areas to the suitability of land use in watersheds. The geospatial information on the biophysical environment, generated from the methodological procedures described in this article, has a high positive potential to guide the rational planning of the use of natural resources and territorial occupation, besides serving as a powerful instrument to guide policies and collective processes of decision on the use and land cover.

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This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

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The present study introduces a multi-agent architecture designed for doing automation process of data integration and intelligent data analysis. Different from other approaches the multi-agent architecture was designed using a multi-agent based methodology. Tropos, an agent based methodology was used for design. Based on the proposed architecture, we describe a Web based application where the agents are responsible to analyse petroleum well drilling data to identify possible abnormalities occurrence. The intelligent data analysis methods used was the Neural Network.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)