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Mitigating the harmful effects of flooding is essential in the current climate change and urban growth scenario, where these natural phenomena are expected to occur more often. Addressing this problem, we present a novel graph-based forecasting model for predicting urban flooding and showcase its application in the Bow River in the City of Calgary, Alberta, Canada. The proposed model is based on graph theory and deep learning paradigms and was used to forecast flooding occurrences up to 24 h ahead. Our new approach developed the SAGE algorithm, a hybrid learning and planning approach that accurately predicted floodings for various analyzed forecasting horizons, increasing the forecasting performance by up to 44% compared to the persistence model. The SAGE model also returned superior or competitive results compared to other models in the literature. The superiority of the SAGE model was highlighted for longer forecast horizons of 24 h ahead, where it reached a maximum improvement of 79%, suggesting its superiority in capturing spatiotemporal information in the dataset. These results indicate that the SAGE approach is a cutting-edge tool for flooding forecasting, and it could be used to develop early flood warning systems to reduce potential flooding impacts. [ABSTRACT FROM AUTHOR]

The presence of pollutants in our atmosphere has become one of humanity's greatest challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to human health, our climate and agriculture. This work proposes the use of a spatiotemporal graph neural network, designed to forecast ozone concentration based on the GraphSAGE paradigm, to aid in our understanding of the dynamic nature of these pollutants' production and proliferation in urban areas. This model was trained and tested using data from Houston, Texas, the United States, with varying numbers of time-lags, forecast horizons (1, 3, 6 h ahead), input data and nearby stations. The results show that the proposed GNN-SAGE model successfully recognized spatiotemporal patterns underlying these data, bolstering its forecasting performance when compared with a benchmarking persistence model by 33.7%, 48.7% and 57.1% for 1, 3 and 6 h forecast horizons, respectively. The proposed model produces error levels lower than we could find in the existing literature. The conclusions drawn from variable importance SHAP analysis also revealed that when predicting ozone, solar radiation becomes relevant as the forecast time horizon is raised. According to EPA regulation, the model also determined nonattainment conditions for the reference station. [ABSTRACT FROM AUTHOR]

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows. [ABSTRACT FROM AUTHOR]

The increasing concern about climate change and global warming led to the necessity to develop new energy sources. Within this scenario, wind power has become a major player due to its global availability and competitive prices. However, the intermittency and stochastic characteristics of the wind ultimately damper its efficient usage, increasing its operational costs. To mitigate this barrier, this work investigates the spatiotemporal phenomena underpinning wind speed and their usefulness when it comes to wind forecasting. We propose a new ensemble model based on Graph Attention Network (GAT) and GraphSAGE to predict wind speed in a bi-dimensional approach. The model was trained and validated using the Dutch dataset and considered several time horizons, timelags, and the impact of weather stations distributed across the country. To benchmark the results, the proposed model was compared against the persistence and LSTM models, as well as state-of-the-art paradigms such as LSSTM, GNN-GAT, and GNN-SAGE. The results showed that the ensemble SAGE-GAT was equivalent to or outperformed all benchmarking models and had lesser error values than those found in reference literature. The results also showed that the longer the horizon is to predict wind speed, the more relevant the spatial information passed from the stations is. • Spatiotemporal GNN-DNN models are developed and tested for 2D wind speed forecasting. • The output is the bidimensional wind velocity vector of the Dutch wind speed database. • Multifeature LSTM, Spatial LSTM, GNN-GAT, GNN-SAGE, and an ensemble model were tested. • 1- up to 12-h forecasting taking information from several nearby stations (0 up to 30). • The developed GNN ensemble model was consistently equivalent to or better than the others. [ABSTRACT FROM AUTHOR]

This paper proposes to analyze the performance increase in the forecasting of solar irradiance and wind speed by implementing a dynamic ensemble architecture for intra-hour horizon ranging from 10 to 60 min for a 10 min time step data. Global horizontal irradiance (GHI) and wind speed were computed using four standalone forecasting models (random forest, k-nearest neighbors, support vector regression, and elastic net) to compare their performance against two dynamic ensemble methods, windowing and arbitrating. The standalone models and the dynamic ensemble methods were evaluated using the error metrics RMSE, MAE, R2, and MAPE. This work's findings showcased that the windowing dynamic ensemble method was the best-performing architecture when compared to the other evaluated models. For both cases of wind speed and solar irradiance forecasting, the ensemble windowing model reached the best error values in terms of RMSE for all the assessed forecasting horizons. Using this approach, the wind speed forecasting gain was 0.56% when compared with the second-best forecasting model, whereas the gain for GHI prediction was 1.96%, considering the RMSE metric. The development of an ensemble model able to provide accurate and precise estimations can be implemented in real-time forecasting applications, helping the evaluation of wind and solar farm operation. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the analysis and comparison of 7 (seven) numerical methods for the assessment of effectiveness in determining the parameters for the Weibull distribution, using wind speed data collected in Camocim and Paracuru cities, State of Ceará, in the northeast region of Brazil, in the period from August 2004 to April 2006, obtained by the Department of Infrastructure of the State of Ceará. One method is not well known, namely the equivalent energy method, and its performance is compared to the others. By using the methods of analysis of variance, RMSE (root mean square error), and chi-square tests to compare the proposed methods, this study aims to determine which ones are effective in determining the parameters of the Weibull distribution for the available data, in an attempt to establish acceptable criteria to a better utilization of wind power in the State of Ceará, which is a national prominence in the use of renewable sources for electricity generation in Brazil. [Copyright &y& Elsevier]

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands. [ABSTRACT FROM AUTHOR]

The world population is increasingly aware of environmental issues involving our planet. In search of solutions that will contribute to the conservation of the environment, you can use the renewable energies that are in great expansion, being the use of solar energy a solution for this purpose. This study aimed to evaluate the energy absorption capacity of a hybrid silver nanofluid and titanium dioxide used for direct solar absorption in a "Solar Wall." The experiments performed were exposed for 16 hours for several days. Various concentrations were analyzed, where a concentration of 23.2 ppm titanium dioxide was set, and silver varied to 0.40625 ppm, 0.8125 ppm, 1.625 ppm, and 3.25 ppm corresponding to a 3% molar fraction. 6%, 12% and 25% respectively. An analysis of the temperature profile was made, which showed a better utilization of the sample with a 12% molar fraction that obtained a temperature gain of 8.3 ° C corresponding to a 14.9% gain. An analysis of the stored energy ratio was analyzed, observing a good response of nanofluids at the early solar incidence and a maximum stored energy for the sample of 6%. The other metric analyzed was the specific absorption rate that reached a maximum value of 0.009293 KW / g. The work showed that for the preparation of hybrid nanofluid, not always increasing the silver concentration implied better results and through temperature profile analysis and energy analysis metrics, it was concluded that a silver concentration of 0.8125 ppm together With a concentration of 23.2 ppm (6% molar fraction) better gains are obtained for the use of nanofluid in the solar wall. [ABSTRACT FROM AUTHOR]

Abstract This work aims to propose a different approach to evaluate the operating conditions of a scaled wind turbine through vibration analysis. The turbine blades were built based on the NREL S809 profile and a 40-cm diameter, while the design blade tip speed ratio (λ) is equal to 7. Masses weighing 0.5, 1.0, and 1.5 g were added to the tip of one or two blades in a varying sequence with the intent of simulating potential problems and producing several scenarios from simple imbalances to severe rotor vibration levels to be compared to the control condition where the three blades and the system were balanced. The signals were processed and classified by a combination of detrended fluctuation analysis with Karhunen-Loève Transform, Gaussian discriminator, and Artificial Neural Network, which are pattern recognition techniques with supervised learning. Good results were achieved by employing the above cited recognition techniques as more than 95% of normal and imbalanced cases were correctly classified. In a general way, it was also possible to identify different levels of blade imbalance, thus proving that the present approach may be an excellent predictive maintenance tool for vibration monitoring of wind turbines. Highlights • Imbalance in wind turbines is a recurrent and important maintenance problem. • Detrended fluctuation analysis (DFA) was applied to preprocess vibration signals. • The predictors generated by DFA fed three classification algorithms. • The classification success rate of all imbalance levels was always superior to 93.4%. • All operating conditions were fine classified, regardless of rotational speed. [ABSTRACT FROM AUTHOR]

This work introduces a new approach for the evaluation of numerical methods used in calculating the Weibull parameters for the prediction of wind resource. In this evaluation, power is a key issue since it proves to be quite relevant in predicting the feasibility of a given site for efficient use of wind energy. Previous knowledge of such information is important for the decision process on the technical feasibility of installing industrial wind farms. The paper performed a statistical analysis of seven mathematical methods to estimate the shape parameter k and the scaling parameter c of the Weibull distribution. Wind speed and wind power data collected in two coastal cities (Icapuí and Camocim), State of Ceará, in the northeast region of Brazil were used. The methods used in the development of this research are the graphical method, maximum likelihood method, modified maximum likelihood method, empirical method, moment method, energy pattern factor method and the equivalent energy method. Comparative analysis of efficiency and accuracy involves the application of the following statistical tests: analysis of variance (R2), root mean square error (RMSE), and chi-square (X 2). The use of power as a parameter to be evaluated in this study by analyzing the Weibull distribution, is something different from what has been discussed in the literature, and indicates that methods which are well evaluated when the wind speed is used as a comparison factor can change position when ranked by power. It is important to emphasize that the values of k and c were calculated directly from the values of wind power (not the wind speed, as usual), and subsequently it was statistically tested the adequacy of the values in the prediction of wind speed and wind power. The presented results indicate that the values generated for the shape and scale parameters provided differences considering the databases formed by wind speed and wind power, namely the graphical method. The wind power data produced accurate results when submitted to statistical tests. [ABSTRACT FROM AUTHOR]

Abstract: This article presents expressions for the Sherwood number as a function of the Schmidt and Grashof numbers for the evaporation–condensation thermal processes in water at temperatures ranging from 70 °C to 99 °C. The aim is to predict the condensation rate in these processes using temperature data. To collect experimental data to develop the expressions, a one-stage thermal desalination unit was built with controlled temperatures. In the measurements, water evaporation temperature ranged from 20 °C to near boiling, at atmospheric pressure. By using a well thermally insulated unit, all losses were minimized and only drop back losses, which represent drops that do not reach the collection flow channel, were presented. These losses were reduced by the condensation surface tilt angle, which facilitated the flow of condensate. The measured temperatures and condensation rates allowed the calculation of the mass transfer coefficient and the Sherwood number, considering the average distance between the plates as the characteristic length. To estimate the constants in the expression for the Sherwood number, a non-linear regression model was implemented for 698 data points. The condensation surface tilt angles were: 10°, 14°, and 16.8°. The results show that the expressions are more adequate for a temperature range of 70 °C–95 °C, where there is a lack of more information in the literature. [Copyright &y& Elsevier]