Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

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

Forecasting Particulate Matter Concentrations: Use of Unorganized Machines

Air pollution is an environmental issue studied worldwide, as it has serious impacts on human health. Therefore, forecasting its concentration is of great importance. Then, this study presents an analysis comprising the appliance of Unorganized Machines – Extreme Learning Machines (ELM) and Echo State Networks (ESN) aiming to predict particulate matter with aerodynamic diameter less than 2.5 m (PM2.5) and less than 10 m (PM10). The databases were from Kallio and Vallilla stations in Helsinki, Finland. The computational results showed that the ELM presented best results to PM2.5, while the ESN achieved the best performance to PM10

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

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&rsquo 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&rsquo health related to road transport variables for S&atilde o Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron&mdash MLP, Extreme Learning Machines&mdash ELM, and Echo State Neural Networks&mdash ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity&mdash 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&mdash good health and well-being, 7&mdash affordable and clean energy, and 11&mdash sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system. Document type: Articl

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting

Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide due to global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O3, NO2, NO, PM2.5, and PM10, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution

Assessing Atmospheric Pollution and Its Impacts on the Human Health

This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations

Integrando redes neurais : avaliação do impacto do MP2,5 na saúde para cidades de médio porte em perspectiva aos novos limites da OMS

Orientador: Prof. Dr. Ricardo H. Moreton GodoiCoorientadora: Profa. Dra. Yara de Souza TadanoTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Ambiental. Defesa : Curitiba, 06/11/2023Inclui referênciasResumo: A poluição atmosférica é atualmente a principal causa de mortes evitáveis no mundo, e grande parte dessas mortes ocorre em países de baixa/média renda, onde há falta de monitoramento da qualidade do ar, geralmente em cidades de médio/ baixo porte. Além disso, as formas solúveis dos elementos contidos no material particulado fino (MP2,5) têm o potencial de penetrar profundamente no pulmão, onde são translocados por meio da barreira sanguínea para o sistema circulatório, acumulando-se em outros órgãos do corpo humano causando efeitos sistêmicos à saúde. No entanto, geralmente, as diretrizes globais de qualidade do ar são baseadas nas funções dose-resposta apenas da concentração mássica do MP, tratando todas as partículas finas como igualmente tóxicas, independente de sua fonte e composição. Este estudo teve como objetivo investigar a tendência de longo prazo do MP2,5 na cidade de médio porte de Londrina, Brasil. MP2,5 foi amostrado diariamente por quase dois anos, e obteve-se a concentração mássica de MP2,5, carbono negro e composição elementar. As possíveis fontes do poluente foram estudadas empregando-se a Análise de Componentes Principais Absoluta e o Fator de Enriquecimento. O risco à saúde foi avaliado com a bioacessibilidade do conteúdo inorgânico do MP no fluido pulmonar artificial, com a avaliação de risco químico à saúde e por meio da avaliação do efeito da exposição ao MP2,5 nas internações hospitalares por desfechos respiratórios (DR) e circulatórios (DC) com a aplicação de Redes Neurais Artificiais (RNA). Três diferentes RNA foram testadas (Perceptron de Múltiplas Camadas (MLP), Máquinas de Aprendizado Extremo, e as Redes Neurais com Estados de Eco), e o desempenho delas foi avaliado por meio do Erro Quadrático Médio (MSE) e o Erro Percentual Absoluto Médio (MAPE). A concentração média do MP2,5 ficou abaixo da diretriz da OMS, no entanto, a exposição ao MP2,5 possui riscos de 5 a cada 100.000 habitantes desenvolverem doenças carcinogênicas ao longo da vida. Além disso, foi observada a influência do MP2,5 nas internações hospitalares por DR e DC com a MLP, RNA que apresentou os menores valores de MSE (12,49 para DR e 15,64 para DC) e MAPE (27,1 para DR e 28,42 para DC). A bioacessibilidade média de 58,6%, 41,7% e 16,5% para Cu, Pb e Mn, respectivamente revelou o variável potencial de mobilidade para o sistema circulatório das fontes do MP na cidade: trânsito, poeira de rua e processos industriais. Esses resultados indicam que mesmo para cidades de médio porte existem riscos à saúde associados à exposição à poluição atmosférica, e ressaltam a necessidade de mais desenvolvimento de pesquisas nestas localidades. Além disso, há um amplo espectro de suscetibilidade ao poluente em relação às características econômicas específicas de cada cidade em particular, mesmo em cidades de pequeno/médio porte, destacando a hipótese geral tácita de que nenhum nível de MP2,5 é seguro, mesmo abaixo dos limites de qualidade do ar da OMS. Dada a evidência emergente de que a composição química do MP é tão, ou até mais, importante do que os níveis de concentração de massa, esta pesquisa pode abrir caminho para as colaborações inter e intra-cidades necessárias para lidar com esse problema de saúde globalAbstract: Air pollution is currently the leading global risk factor for death, and a significant proportion of these deaths occur in low- and middle-income countries (LMICs), where there is a lack of air quality monitoring, general in middle- and small-sized cities. Moreover, the soluble forms of the elements present in fine particulate matter (PM2.5) can deeply penetrate the lungs. From there, they translocate across the blood barrier into the circulatory system and accumulate in various organs, leading to systemic health effects. The global air quality guidelines are generally based on the doseresponse functions of only the PM2.5 mass concentration, treating all fine particles as equally toxic, regardless of their source or composition. This study aimed to investigate the long-term trend of PM2.5 in the middle-sized Brazilian city of Londrina. PM2.5 was sampled daily for almost two years, and PM2.5 concentration, black carbon, and elemental composition were obtained. The possible sources were identified using absolute principal component analysis and enrichment factor analysis. The health risk was evaluated by assessing the bioaccessibility of inorganic contents in artificial lung fluid, conducting a chemical health assessment for resident exposure, and using artificial neural networks (ANN) to assess the effect of PM2.5 exposure on hospital admissions due to respiratory (RO) and circulatory (CO) outcomes. Tree different ANN models were tested (Multilayer Perceptron (MLP), Extreme Learning Machines (ELM), and Echo State Networks (ESN)), and their performance was evaluated based on the mean square error (MSE), and the Mean Absolute Percentual Error (MAPE). Although the average concentration of PM2.5 was below WHO guidelines, the exposure do PM2.5 leads to the risk of 5 in each group of 100,000 inhabitants to develop carcinogenic diseases over a lifetime. Furthermore, the influence of MP2.5 exposure on hospital admissions for RO and CO was observed with MLP, ANN that presented the lowest values of MSE (12.49 for RO and 15.64 for CO) and MAPE (27.1 for RO and 28.42 for CO).The average bioaccessibility of 58.6% for Cu, 41.7% for Pb and 16.5% for Mn, revealed the diverse mobility potential for the circulatory system for PM sources in the city: traffic, street dust and processes industrial. These results indicate that even in medium-sized cities, there are health risks associated with air pollution exposure, and highlight the need for more research in this regions. Moreover, there is a broad spectrum of susceptibility to pollutants, likely related to the specific and diversified economic base of each city, even in smalland medium-sized cities, highlighting the tacit general assumption that no level of PM2.5 is safe, even below WHO air quality limits. Given the further emerging evidence that PM chemical composition is as, or even more, important than mass concentration levels, the research reported in the paper could pave the way for the necessary inter- and inner-city collaborations that are needed to address this global health challeng

A Builder's Guide to Water and Energy

The work on which this report is based was supported in part by funds provided by the Office of Water Research and Technology (Project A-Q65-ALAS), US. Department of the interior, Washington, D.C., as authorized by the Water Research and Development Act of 1978