The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Determining spatial resolution in spatial load forecasting using a grid-based model

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo da dinâmica climática e sua influência na qualidade da água de reservatórios urbanos

The present study sought to develop a methodology to analyze the water quality based on concepts and methods of climate and climatology respectively. In this way, it was tried to relate techniques and methodologies hydro and limno-meteorological with a rhythmic analysis technique developed within the Brazilian geographical climatology to assess and analyze the blooms of cyanobacteria, the main index of water quality found in reservoirs of “Alto Tietê” Basin and consequently the Metropolitan Region of São Paulo, an area of high environmental complexity due to its high degree of development and high population density. The meteorological data used to develop the study were collected from the meteorological station of the IAG / USP and the limnological data were collected from the Hydrological Monitoring System implemented by SABESP in Billings and Guarapiranga reservoirs as well as data laboratory of the same entity. The results obtained by the rhythmic and integrated analyze showed that the process of blooms of cyanobacteria is dependent not only on one specific factor, but a combination of meteorological factors that may disrupt the stability of reservoir and which can, during the stabilization process, provides the necessary conditions to the development of cyanobacteria. It was also shown by the results that the pace of Atlantic Polar Front Entrance during the winter in São Paulo, is a limiting factor to the growth of cyanobacteria due to their high frequency, keeping the reservoir balance throughout the period. Furthermore, another importance of this study is the possibility of prevention and forecasting periods which are inappropriate for the use of these reservoirs mainly for recreational activities

The climatic rhythm and blooms of cyanobacteria in a tropical reservoir in São Paulo, Brazil

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

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Analysis of NDVI time series using cross-correlation and forecasting methods for monitoring sugarcane fields in Brazil

Brazil is the largest sugarcane producer in the world and has a privileged position to attend to national and international market places. To maintain the high production of sugarcane, it is fundamental to improve the forecasting models of crop seasons through the use of alternative technologies, such as remote sensing. Thus, the main purpose of this article is to assess the results of two different statistical forecasting methods applied to an agroclimatic index (the water requirement satisfaction index; WRSI) and the sugarcane spectral response (normalized difference vegetation index; NDVI) registered on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite images. We also evaluated the cross-correlation between these two indexes. According to the results obtained, there are meaningful correlations between NDVI and WRSI with time lags. Additionally, the adjusted model for NDVI presented more accurate results than the forecasting models for WRSI. Finally, the analyses indicate that NDVI is more predictable due to its seasonality and the WRSI values are more variable making it difficult to forecast.

Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series

This paper addressed the problem of water-demand forecasting for real-time operation of water supply systems. The present study was conducted to identify the best fit model using hourly consumption data from the water supply system of Araraquara, Sa approximate to o Paulo, Brazil. Artificial neural networks (ANNs) were used in view of their enhanced capability to match or even improve on the regression model forecasts. The ANNs used were the multilayer perceptron with the back-propagation algorithm (MLP-BP), the dynamic neural network (DAN2), and two hybrid ANNs. The hybrid models used the error produced by the Fourier series forecasting as input to the MLP-BP and DAN2, called ANN-H and DAN2-H, respectively. The tested inputs for the neural network were selected literature and correlation analysis. The results from the hybrid models were promising, DAN2 performing better than the tested MLP-BP models. DAN2-H, identified as the best model, produced a mean absolute error (MAE) of 3.3 L/s and 2.8 L/s for training and test set, respectively, for the prediction of the next hour, which represented about 12% of the average consumption. The best forecasting model for the next 24 hours was again DAN2-H, which outperformed other compared models, and produced a MAE of 3.1 L/s and 3.0 L/s for training and test set respectively, which represented about 12% of average consumption. DOI: 10.1061/(ASCE)WR.1943-5452.0000177. (C) 2012 American Society of Civil Engineers.

Space-temporal dynamics of limnological variables in a floodplain wetland located at Ribeira do Iguape Valley (São Paulo, Brazil) at different hydrological periods

AIM: The main goal of this research was to investigate the influence of the hydrological pulses on the space-temporal dynamics of physical and chemical variables in a wetland adjacent to Jacupiranguinha River (São Paulo, Brazil); METHODS: Eleven sampling points were distributed among the wetland, a tributary by its left side and the adjacent river. Four samplings were carried out, covering the rainy and the dry periods. Measures of pH, dissolved oxygen, electrical conductivity and redox potential were taken in regular intervals of the water column using a multiparametric probe. Water samples were collected for the nitrogen and total phosphorus analysis, as well as their dissolved fractions (dissolved inorganic phosphorus, total dissolved phosphorus, ammoniacal nitrogen and nitrate). Total alkalinity and suspended solids were also quantified; RESULTS: The Multivariate Analysis of Variance showed the influence of the seasonality on the variability of the investigated variables, while the Principal Component Analysis gave rise in two statistical significant axes, which delimited two groups representative of the rainy and dry periods. Hydrological pulses from Jacupiranguinha River, besides contributing to the inputs of nutrients and sediments during the period of connectivity, accounted for the decrease in spatial gradients in the wetland. This "homogenization effect" was evidenced by the Cluster Analysis. The research also showed an industrial raw effluent as the main point source of phosphorus to the Jacupiranguinha River and, indirectly, to the wetland; CONCLUSIONS: Therefore, considering the scarcity of information about the wetlands in the study area, this research, besides contributing to the understanding of the influence of hydrological pulses on the investigated environmental variables, showed the need for adoption of conservation policies of these ecosystems face the increase anthropic pressures that they have been submitted, which may result in lack of their ecological, social and economic functions.

Forecasting the need for medical specialists in Spain: application of a system dynamics model

[EN] Background: Spain has gone from a surplus to a shortage of medical doctors in very few years. Medium and long-term planning for health professionals has become a high priority for health authorities. Methods: We created a supply and demand/need simulation model for 43 medical specialties using system dynamics. The model includes demographic, education and labour market variables. Several scenarios were defined. Variables controllable by health planners can be set as parameters to simulate different scenarios. The model calculates the supply and the deficit or surplus. Experts set the ratio of specialists needed per 1000 inhabitants with a Delphi method. Results: In the scenario of the baseline model with moderate population growth, the deficit of medical specialists will grow from 2% at present (2800 specialists) to 14.3% in 2025 (almost 21 000). The specialties with the greatest medium-term shortages are Anesthesiology, Orthopedic and Traumatic Surgery, Pediatric Surgery, Plastic Aesthetic and Reparatory Surgery, Family and Community Medicine, Pediatrics, Radiology, and Urology. Conclusions: The model suggests the need to increase the number of students admitted to medical school. Training itineraries should be redesigned to facilitate mobility among specialties. In the meantime, the need to make more flexible the supply in the short term is being filled by the immigration of physicians from new members of the European Union and from Latin America.

Phytoplankton community composition and size-abundance spectra in areas of the Alborán sea with different hydrological characteristics

[EN] Size-abundance spectra (SAS) were analyzed in different phytoplankton assemblages of the Alborán Sea collected in areas with contrasting hydrological features (upwelling areas and anticyclonic gyre). Abundance of micro-plankton cells decreased following the hydrological gradient from the most productive stations towards offshore stations. This size-fraction of phytoplankton was dominated by diatoms. Pico-plankton followed an opposite gradient. Concordantly, the slope of the SAS trended to decrease from coastal areas (upwelling) towards the anticyclonic gyre. However, phytoplankton assemblages featuring similar SAS values presented a quite different taxonomical composition of their diatom communities. According to the previous available information about the taxonomical composition of the phytoplankton communities in the Alborán Sea, these differences in diatom composition are indicative of differences in productivity of the phytoplankton. Consequently, the utility of phytoplankton SAS as an indicator of changes in the phytoplankton communities of the Alborán Sea is discussed.

Forecasting and visualization of wildfires in a 3D geographical information system

[EN]This paper describes a wildfi re forecasting application based on a 3D virtual environment and a fi re simulation engine. A novel open source framework is presented for the development of 3D graphics applications over large geographic areas, off ering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connection of several users for monitoring a real wildfi re event.

Ensemble forecasting in the Mediterranean sea

A new methodology is being devised for ensemble ocean forecasting using distributions of the surface wind field derived from a Bayesian Hierarchical Model (BHM). The ocean members are forced with samples from the posterior distribution of the wind during the assimilation of satellite and in-situ ocean data. The initial condition perturbations are then consistent with the best available knowledge of the ocean state at the beginning of the forecast and amplify the ocean response to uncertainty only in the forcing. The ECMWF Ensemble Prediction System (EPS) surface winds are also used to generate a reference ocean ensemble to evaluate the performance of the BHM method that proves to be eective in concentrating the forecast uncertainty at the ocean meso-scale. An height month experiment of weekly BHM ensemble forecasts was performed in the framework of the operational Mediterranean Forecasting System. The statistical properties of the ensemble are compared with model errors throughout the seasonal cycle proving the existence of a strong relationship between forecast uncertainties due to atmospheric forcing and the seasonal cycle.