Avaliação do horário de verão brasileiro como política pública de eficiência energética

Sediment runoff from dense highland field areas greatly affects the quality of downstream lakes and drinking water sources. In this study, multiple linear regression (MLR) models were built to predict diffuse pollutant discharge using the environmental parameters of a basin. Explanatory variables that influence the sediment and pollutant discharge can be identified with the model, and such research could play an important role in limiting sediment erosion in the dense highland field area. Pollutant load per event, event mean concentration (EMC), and pollutant load per area were estimated from stormwater survey data from the Lake Soyang basin. During the wet season, heavy rains cause large amounts of suspended sediment and the occurrence of such rains is increasing due to climate change. The explanatory variables used in the MLR models are the percentage of fields, subbasin area, and mean slope of subbasin as topographic parameters, and the number of preceding dry days, rainfall intensity, rainfall depth, and rainfall duration as rainfall parameters. In the MLR modeling process, four types of regression equations with and without log transformation of the explanatory and response variables were examined to identify the best performing regression model. The performance of the MLR models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation of the root mean square error (CV(RMSE)), the ratio of the RMSE to the standard deviation of the observed data (RSR) and the Nash–Sutcliffe model efficiency (NSE). The performance of the MLR models of pollutant load except total nitrogen (TN) was good under the condition of RSR, and satisfactory for the NSE and R2. In the EMC and load/area models, the performance for suspended solids (SS) and total phosphorus (TP) was good for the RSR, and satisfactory for the NSE and R2. The standardized coefficients for the models were analyzed to identify the influential explanatory variables in the models. In the final performance evaluation, the results of jackknife validation indicate that the MLR models are robust.

Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R2 value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings.

Multiple linear regression (MLR) models for predicting zinc (Zn) toxicity to freshwater organisms were developed based on three toxicity-modifying factors: dissolved organic carbon (DOC), hardness, and pH. Species-specific, stepwise MLR models were developed to predict acute Zn toxicity to four invertebrates and two fish, and chronic toxicity to three invertebrates, a fish, and a green alga. Stepwise regression analyses found that hardness had the most consistent influence on Zn toxicity among species, whereas DOC and pH had a variable influence. Pooled acute and chronic MLR models were also developed, and a k-fold cross-validation was used to evaluate the fit and predictive ability of the pooled MLR models. The pooled MLR models and an updated Zn biotic ligand model (BLM) performed similarly based on (1) R2 , (2) the percentage of effect concentration (ECx) predictions within a factor of 2.0 of observed ECx, and (3) residuals of observed/predicted ECx versus observed ECx, DOC, hardness, and pH. Although fit of the pooled models to species-specific toxicity data differed among species, species-specific differences were consistent between the BLM and MLR models. Consistency in the performance of the two models across species indicates that additional terms, beyond DOC, hardness, and pH, included in the BLM do not help explain the differences among species. The pooled acute and chronic MLR models and BLM both performed better than the US Environmental Protection Agency's existing hardness-based model. We therefore conclude that both MLR models and the BLM provide an improvement over the existing hardness-only models and that either could be used for deriving ambient water quality criteria. Environ Toxicol Chem 2023;42:393-413. © 2022 SETAC.

Electrical energy has become the main pillar in modern human activities. Electrical energy consumption continues to increase from time to time, including in the Province of West Nusa Tenggara (NTB), whose economy is heavily supported by the tourism sector and the mining industry. Previous studies have proven that the increase in electrical energy consumption is triggered by various factors, both demographic and economic factors. This research aims to find out what factors determine the amount of electrical energy consumption in NTB Province. This quantitative research uses secondary data in the form of time series data from 2000-2022. The data in this study was analyzed using a multiple linear regression model. The results of this research conclude that the development of the tourism sector, industrial sector and population simultaneously have a significant influence on electrical energy consumption in West Nusa Tenggara Province. However, partially the development of the tourism sector and the development of the industrial sector have no effect on electrical energy consumption, while the population partially has a significant effect on electrical energy consumption in NTB Province.

Selection and validation of parameters in multiple linear and principal component regressions

Background: Obstructive sleep apnea (OSA) and the metabolic syndrome are recognized as a risk factor for cardiovascular disease. Cardiovascular events have been reported to have a peak incidence in the early hours after waking in OSA patients. This study was designed to examine the influence of OSA on endothelial function in the early morning in patients with the metabolic syndrome. Methods: The severity of sleep-disordered breathing was evaluated by polysomnography in patients with the metabolic syndrome. Ten OSA patients (an apnea-hypopnea index [AHI] >30) with the metabolic syndrome was included in this study, and we also included age-and sex-matched ten non-OSA patients (AHI <5) with the metabolic syndrome in this study. All subjects received pioglitazone for 1 month (1Mo), and then OSA patients received pioglitazone and nasal continuous positive airway pressure (CPAP) treatment for next 1 month (2Mo). Flow-mediated dilatation (FMD) and nitroglycerin-induced dilatation (NID) of brachial artery were measured by using ultrasound system. We also assessed insulin resistance by HOMA-IR. Measurements were performed in the early morning (6:00AM) and the late morning (11:00AM) at baseline, 1Mo, and 2Mo. Results: At baseline, there were not differences in FMD, and NID between the early morning and the late morning. After the treatment with pioglitazone (1Mo), FMD in the non-OSA patients was increased in the early and late morning, but FMD in the OSA patients was increased only in the late morning. After the CPAP treatment (2Mo), FMD in the OSA patients was increased in the early and late morning. HOMA-IR was improved at 1Mo in the non-OSA patients, and was improved at 2Mo in the OSA patients. Conclusion: OSA is associated with endothelial dysfunction in the early morning and insulin resistance in patients with the metabolic syndrome, and CPAP treatment is effective on the improvement of endothelial dysfunction in the early morning in OSA patients with the metabolic syndrome.

Multiple linear regression models are widely used in applied statistical techniques and they are most useful devices for extracting and understanding the essential features of datasets. However, in multiple linear regression models, problems arise when multicollinearity or a serious outlier observation present in the data. Multicollinearity is a linear dependency between two or more explanatory variables in the regression models which can seriously affect the least squares estimated regression surface. The other important problem is outlier; they can strongly influence the estimated model, especially when using least squares method. Nevertheless, outlier data are often the special points of interests in many practical situations. The purpose of this study is to performance comparison of Akaike Information Criterion (AIC'), Bayesian Information Criterion (BIC’) and Information Complexity Criterion (ICOMP'(IFIM)) for detecting outliers using Genetic Algorithms when multiple regression model having multicollinearity problems. Keywords: Akaike Information Criterion; Bayesian Information Criterion; Information Complexity Criterion; Genetic Algorithms; multicollinearity; outlier detection. 2010 Mathematics Subject Classification: 62J05

This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM2.5 concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model’s characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM2.5 concentrations.

Agriculture is the main foundation of the economy of Bangladesh. This sector contributes about 17.22% to the country's GDP and accommodates around 45.6% of the labor force. New technologies have increased over the past few decades in Bangladesh's agriculture. As a result, agricultural production in the country has grown tremendously, but due to an inefficient marketing system, farmers do not receive the advantage of the enormous output. Because of some inefficiency in the agricultural marketing system, farmers are deprived of the fair price of their produce. Several factors influence the price received by the farmers for their agricultural commodities. This study thus aims to examine marketing practice and the degree of influence of these practices on farmers' profit in Northern Bangladesh. Two districts, namely Naogaon and Dinajpur, were selected purposively from two divisions in Northern Bangladesh. Two upazillas and two villages were chosen following a simple random sampling (SRS) method for collecting data. The study used a set of questionnaires with five sections to collect data. To serve research objectives, 216 farmers were interviewed using a structured questionnaire with a face-to-face interview, and 32 key informant respondents were interviewed using a checklist. To achieve the goal, a multiple linear regression model was used, considering the farmer's profit as a dependent variable, marketing practices as an independent variable, and financial factors as an independent variable. The multiple linear regression model was estimated. The study found that almost cent percent of farmers sell their produce from farmhouses or to the rural Hat at Bepari. It was found that different types of intermediaries were functioning in agricultural marketing: farmers, Farias, Beparies, Aratders, wholesalers, Millers, cold storage owners, and retailers. Using a multiple linear regression model, it was found that three explanatory variables, i.e., the Sale of an agricultural commodity at a town market, Crop storage status, sell produce to public procurement, positively affects the farmer's profit. The remaining two explanatory variables, i.e., the Sale of agricultural commodities during the harvesting period and receiving a loan from informal sources, negatively affect the farmer's profit. Only one independent variable, i.e., Crop storing status, is a statistically insignificant factor. The rest of the four independent variables are statistically significant factors affecting farmers' profit in Northern Bangladesh.

Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques

Quantifying biosphere–atmosphere exchange of CO2 using eddy covariance, wavelet denoising, neural networks, and multiple regression models

This study was carried out to estimate the relationship between hand length, foot length and stature using multiple linear regression analyses based on a sample of male and female adult Turks residing in Adana. Measurements of hand length, foot length and stature were taken from 155 adult Turks (80 male, 75 female) aged 17-23 years. The participants were students of the Medical Faculty of Cukurova University. A multiple linear regression model was fitted to the observed data. Stature was taken as the response or dependent variable, hand length and foot length were taken as explanatory variables or regressors. All possible (simple and multiple) linear regression models for each of males, females and both genders together were tested for the best model. The multiple linear regression model for both genders together was found to be the best model with the highest values for the coefficients of determination R2 = 0.861 and R2adjusted = 0.859, and multiple correlation coefficient R = 0.928.

The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km2), post-glacial lake located in a temperate climate zone by means of artificial neural networks and multiple linear regression. The area of study was Lake Serwy, located in northeastern Poland. Two artificial neural network (ANN) multilayer perceptron (MLP) and multiple linear regression (MLR) models were built. The following explanatory variables were considered: maximal and minimal temperature (Tmax, Tmin) wind speed (WS), vertical circulation (VC) and water level from previous periods (WL). Additionally, a binary variable describing the period of the year (winter, summer) has been considered in one of the two MLP and MLR models. The forecasting models have been assessed based on selected criteria: mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (R2) and mean biased error. Considering their values and absolute deviations from observed values it was concluded that the ANN model using an additional binary variable (MLP_B+) has the best forecasting performance. Absolute deviations from observed values were the determining factor which made this model the most efficient. In the case of the MLP_B+ model, those values were about 10% lower than in other models. The conducted analyses indicated good performance of ANN networks as a forecasting tool for relatively small lakes located in temperate climate zones. It is acknowledged that they enable water level forecasting with greater precision and lower absolute deviations than the use of multiple linear regression models.

Electrical conductivity (EC) is an important indicator for monitoring water quality in riverine systems. EC is inherently associated with the concentration of dissolved ionic compounds present in aqueous environments, including various salts and minerals. EC estimations are crucial for environmental monitoring and the overall health assessment of aquatic ecosystems. The present study investigated the application of discrete wavelet transform (DWT) in conjunction with artificial neural networks (ANNs) and multiple linear regression (MLR) models to predict daily river water EC. For this purpose, daily river discharge (Q) and EC time series from a hydrology station on the Medina River in San Antonio, Texas, USA, were used. DWT was used to decompose the daily data into several subseries. Then, to estimate one-day-ahead EC values, these subseries were introduced to the ANN and MLR models. To assess the prediction accuracy of the improved wavelet-neural network (WANN) and wavelet-regression (WR) models, EC estimation was also carried out using MLR and ANN models with the original data. Both the WANN and WR techniques outperformed single MLR and ANN methods. A comparison of the results indicated that the WR model had superior performance than the WANN, MLR, and ANN models for daily EC prediction. The R2 values for the WR, WANN, MLR, and ANN models were 0.92, 0.87, 0.74, and 0.74, respectively. For the WR model, the root-mean-square error (RMSE) was 45.55, 46.08, and 25.19% less than those presented by the MLR, ANN, and WANN models, respectively. By the application of the WR method, an accurate daily EC estimator formula was obtained as well. The WR model also satisfactorily simulated the hysteresis in EC, demonstrating the effectiveness of wavelet analysis in extracting essential information embedded in original data.