The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

Linear models are widely used to perform time series forecasting. The Autoregressive models stand out, due to their simplicity in the parameters adjustment based on close-form solution. The Autoregressive and Moving Average models (ARMA) and Infinite Impulse Response filters (IIR) are also good alternatives, since they are recurrent structures. However, their adjustment is more complex, since the problem has no analytical solution. This investigation performs linear models to predict monthly seasonal streamflow series, from to Brazilian hydroelectric plants. The goal is to reach the best achievable performance addressing linear approaches. We propose the application of recurrent models, estimating their parameters via an immune algorithm. To compare the optimization performance, the Least Mean Square (LMS) and Recursive Prediction Error (RPE) algorithms are utilized. Also, the AR model and the Holt-Winters method were performed. The results showed that the insertion of feedback loops increases the quality of the responses. The ARMA models optimized by the immune algorithms achieved the best overall performance.

This work carried out an empirical study with the objective of determining the most efficient strategy in guaranteeing the radiality of the distribution network during the reconfiguration. The reconfiguration problem in a distribution network aims to reduce technical losses by changing the paths that energy takes through the network until it reaches consumers. In this problem, a mandatory constraint is to ensure that the network operates radially. However, the literature does not present a consensus on the best way to guarantee this restriction. Computational experiments were carried out using the two main methodologies in the literature: limiting the number of input edges; and search with removal of cycles in each partial solution. A third and new strategy was proposed as an alternative to the literature using the concept of minimal cuts in graphs. Computational tests showed that the strategy of limiting the number of input edges was the most efficient in ensuring radiality. We also point out a drawback in the applicability of this strategy to networks with high penetrations of distributed generation (DG). For this reason, our experiments also analyze the other strategies that are suitable for DG power flows. For this case, we show that the methodology proposed in this paper provides a better way to tackle modern networks arising from applications of smart grids.