An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.

Spatial and temporal variability of soil CO2 emission in a sugarcane area under green and slash-and-burn managements

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Spatial variability of infiltration and its relationship to some physical properties

Water infiltration into soil is one of the basic factors for estimating irrigation intensity according to the plants' requirements; this is aimed at avoiding problems of surface run-off and degradation. The purpose of the present investigation was to determine the spatial variation of infiltration and its relationship to some physical properties of soil by means of geostatistical techniques in Typic Plinthaquult soils having average texture and flat relief. A 113 point mesh was designned, having a regular distance of 10 m between points, samples being taken from 0 to 0.20 meters depth. Sand, silt and clay content, bulk density, macroporosity, microporosity and total porosity were determined. Infiltration tests were carried out in the field by means of a 15 cm diameter ring. Descriptive statistics and geostatistics were used for analysing the data. Infiltration, silt and microporosity data did not fit a normal distribution curve. Infiltration had high variability, having an average 36.03 mm h(-1). Total porosity was 56.73%, this being the only property that did not show spatial dependency. The smallest ranges were observed for bulk density, macroporosity and microporosity, having values of less than 40 m. The smallest degrees of spatial dependence were observed for infiltration, silt and clay, evidence also being shown of the influence of silt and clay on infiltration rate. Contour maps were constructed; fitting them to the semivariogram models, together with studying the correlations, led to establishing relationships between the properties.

Local dimension and finite time prediction in coupled map lattices

Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour. In this paper, we discuss the use of the bred vector (BV) dimension, a recently introduced statistics, to identify the regimes where a finite time forecast is feasible. Using the tools from dynamical systems theory and Bayesian modelling, we show the finite time predictability in two-dimensional coupled map lattices in the regions of low BV dimension. © Indian Academy of Sciences.

Relief influence on the spatial distribution of the Atlantic Forest cover on the Ibiúna Plateau, SP

Several studies suggest that, on a large scale, relief conditions influence the Atlantic Forest cover. The aim of this work was to explore these relationships on a local scale, in Caucaia do Alto, on the Ibiúna Plateau. Within an area of about 78 km2, the distribution of forest cover, divided into two successional stages, was associated with relief attribute data (slope, slope orientation and altitude). The mapping of the vegetation was based on the interpretation of stereoscopic pairs of aerial photographs, from April 2000, on a scale of 1:10,000, while the relief attributes were obtained by geoprocessing from digitalized topographic maps on a scale of 1:10,000. Statistical analyses, based on qui-square tests, revealed that there was a more extensive forest cover, irrespective of the successional stage, in steeper areas (>10 degrees) located at higher altitudes (>923 m), but no influence of the slope orientation. There was no sign of direct influence of relief on the forest cover through environmental gradients that might have contributed to the forest regeneration. Likewise, there was no evidence that these results could have been influenced by the distance from roads or urban areas or with respect to permanent preservation areas. Relief seems to influence the forest cover indirectly, since agricultural land use is preferably made in flatter and lower areas. These results suggest a general distribution pattern of the forest remnants, independent of the scale of study, on which relief indirectly has a strong influence, since it determines human occupation.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Spatial electric load forecasting using a local movement approach

An agent based model for spatial electric load forecasting using a local movement approach for the spatiotemporal allocation of the new loads in the service zone is presented. The density of electrical load for each of the major consumer classes in each sub-zone is used as the current state of the agents. The spatial growth is simulated with a walking agent who starts his path in one of the activity centers of the city and goes to the limits of the city following a radial path depending on the different load levels. A series of update rules are established to simulate the S growth behavior and the complementarity between classes. The results are presented in future load density maps. The tests in a real system from a mid-size city show a high rate of success when compared with other techniques. The most important features of this methodology are the need for few data and the simplicity of the algorithm, allowing for future scalability. © 2009 IEEE.

Spatial load forecasting using a demand propagation approach

A method for spatial electric load forecasting using multi-agent systems, especially suited to simulate the local effect of special loads in distribution systems is presented. The method based on multi-agent systems uses two kinds of agents: reactive and proactive. The reactive agents represent each sub-zone in the service zone, characterizing each one with their corresponding load level, represented in a real number, and their relationships with other sub-zones represented in development probabilities. The proactive agent carry the new load expected to be allocated because of the new special load, this agent distribute the new load in a propagation pattern. The results are presented with maps of future expected load levels in the service zone. The method is tested with data from a mid-size city real distribution system, simulating the effect of a load with attraction and repulsion attributes. The method presents good results and performance. © 2011 IEEE.

Métodos geoestatísticos na caracterização espacial de óxidos de ferro em diferentes pedoformas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelagem e mapas de capacidade suporte de carga de solos cultivados com cana-de-açúcar

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Spatial Distribution of the Risk of Dengue and the Entomological Indicators in Sumare, State of Sao Paulo, Brazil

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

Amazon forest biomass density maps: tackling the uncertainty in carbon emission estimates

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

A non-homogeneous poisson model with spatial anisotropy applied to ozone data from Mexico City

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Correlations of trace elements in breast human tissues: Evaluation of spatial distribution using μ-XRF

The aim of this work is to investigate microscopic correlations between trace elements in breast human tissues. A synchrotron X-ray fluorescence microprobe system (μ-XRF) was used to obtain two-dimensional distribution of trace element Ca, Fe, Cu and Zn in normal (6 samples) and malignant (14 samples) breast tissues. The experiment was performed in X-ray Fluorescence beam line at Laboratório Nacional de Luz Síncrotron (LNLS), Campinas, Brazil. The white microbeam was generated with a fine conical capillary with a 20 μm output diameter. The samples were supported on a XYZ table. An optical microscope with motorized zoom was used for sample positioning and choice the area to be scanned. Automatic two-dimensional scans were programmed and performed with steps of 30 μm in each direction (x, y) on the selected area. The fluorescence signals were recorded using a Si(Li) detector, positioned at 90 degrees with respect to the incident beam, with a collection time of 10 s per point. The elemental maps obtained from each sample were overlap to observe correlation between trace elements. Qualitative results showed that the pairs of elements Ca-Zn and Fe-Cu could to be correlated in malignant breast tissues. Quantitative results, achieved by Spearman correlation tests, indicate that there is a spatial correlation between these pairs of elements (p < 0.001) suggesting the importance of these elements in metabolic processes associated with the development of the tumor.