Multiple Regression Prediction Research Articles

Research on New Energy Vehicles Based on Multiple Linear Regression and Gray Prediction

Research on New Energy Vehicles Based on Multiple Linear Regression and Gray Prediction

Analysis of Tianjin real estate market: multiple linear regression prediction method

In this paper, the price prediction of Tianjin real estate market is studied, and the key factors affecting housing prices are systematically analyzed by using the method of multiple linear regression prediction. The research data comes from the real estate transaction data of Tianjin from 2018 to 2022, covering independent variable information such as housing area, housing age, geographical location and supporting facilities. By constructing multiple linear regression model, it is found that housing area and supporting facilities have a significant positive impact on housing prices, while housing age and distance from the city center have a significant negative impact on housing prices. Specifically, for every 1 square meter increase in housing area, house prices are expected to rise by 450 yuan; Every year the age of the house increases, the house price is expected to drop by 200 yuan; Every kilometer away from the city center, house prices are expected to drop in 800 yuan; Every time the score of supporting facilities is increased by 1 point, the house price is expected to increase by 1200 yuan. The goodness-of-fit test of the model shows that the R value is 0.85, which shows that the model has strong explanatory power. In addition, through the correlation analysis of independent variables, it is found that there is a positive correlation between housing area and supporting facilities, but the correlation with other variables is weak. This study not only enriches the theoretical research of the real estate market, but also provides valuable decision-making reference for the government, enterprises and consumers, and helps the healthy and stable development of the real estate market in Tianjin.

Scale-specific controls of monthly suspended sediment load in a typical inland river

Sediment transport is a multi-scale and non-linear process controlled by multiple variables. Given the intricate nature of sediment transport mechanisms and the overlap of different factors’ effects on it at various time scales, unraveling the relationship between sediment load and its influencing factors is challenging. This study aimed to elucidate the multi-timescale effects of factors on monthly suspended sediment load in a seasonal inland river. In this study, monthly sediment loads measured by the depth-integrating method were obtained from the Tabu River Basin in northern China from 2007 to 2021, alongside data on four controlling factors: runoff, precipitation, water surface evaporation, and the normalized difference vegetation index (NDVI). The results showed that sediment load, runoff, and precipitation showed strong variability during 2007–2021, while water surface evaporation and NDVI exhibited moderate variability. Multivariate empirical mode decomposition (MEMD) decomposed the temporal patterns of sediment load and associated variables into five intrinsic mode functions (IMF) and a residue. IMF1 (4.7 months), IMF2 (9.0 months) and IMF3 (14.2 months) collectively explained 115.88 %, 91.73 %, 91.28 %, 107.69 %, and 106.09 % of the total variability in sediment load, runoff, precipitation, water surface evaporation, and NDVI, respectively. Subsequently, the time-dependent intrinsic correlation (TDIC) was utilized to illustrate the inherent relationships between sediment load and its controlling factors across various time scales. The associations between sediment load and the controlling factors changed significantly with the scale. Runoff was the dominant factor controlling sediment load at IMF1 (4.7 months), IMF2 (9.0 months), IMF3 (14.2 months), and IMF4 (25.1 months). Water surface evaporation controlled the sediment transport process at IMF5 (63.6 months). The prediction of sediment load on the measurement scale, using scale-specific controls analyzed with MEMD (R2 = 0.993), demonstrated superior performance compared to traditional multiple linear regression prediction (R2 = 0.748). This study will provide a theoretical basis for effective prevention and control of soil erosion and watershed management in inland river basins.

Regional Carbon Emission Forecasting Model Considering Temporal Characteristics and Analysis of Peak Carbon Assessment in Typical Scenarios

Leading high-quality economic development with green development is an important development concept for Chinese-style modernization, and achieving the dual-carbon goals of carbon peaking and carbon neutrality is a key initiative to promote high-quality economic and social development. In order to explore the correlation between carbon emissions and population, economy and energy consumption, and to propose a reasonable carbon emissions prediction model, this paper proposes an evaluation index system integrating fossil energy consumption and new energy consumption by analyzing the sample data obtained from the research in a hierarchical, industry- and sector-specific manner. And then, we compare the reliability of the results obtained from the multiple linear regression prediction model based on the above index system and the Kaya carbon emissions prediction model, respectively. By comparing the reliability of the results obtained from the carbon emission multiple linear regression prediction model based on the above index system and the Kaya carbon emission prediction model, it is initially determined that the Kaya model will be used for regional carbon emission prediction. In order to optimize the sensitivity of the model to the temporal Characterization of the indicators, a carbon emission prediction model that incorporates the temporal characteristics of the data is set up. Lastly, the carbon emission is predicted according to the three typical scenarios analyzed according to the intensity of the macro-policy, and the year of carbon peak and the year of carbon peak are analyzed for the scenarios. Three typical scenarios are analyzed according to the intensity of macro policies, and carbon emissions are predicted under different scenarios, and the year in which each scenario reaches the carbon peak is analyzed, as well as the correlation with the implementation of related policies and energy use, with a view to providing support for the planning of the dual-carbon target value and the path of achieving it, as well as the formulation of related policies.

Data-Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network.

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects.

Lumbar Moment Estimation of Engine Drivers in a Static Sitting Working Position by using Multiple Linear Regression

The paper presents a simpler and more precise model of lumbar moment prediction based on single linear, or multiple linear regression with two predictors. The body mass index (BMI) as the predictor contains two of the most important static anthropometric measures, height and mass, whose separated role in lumbar moment prediction, as well as their mutual relations, have not been sufficiently investigated. This study analysed mass, height, age and BMI as lumbar moment predictors, on a sample of 50 Croatian male engine drivers. Two prediction models were compared: (1) multiple linear regression prediction with mass and height as predictors; (2) single linear regression with mass as the only predictor. Results confirmed the multiple regression model as the best one (R2= 0.9015 with standard error of prediction 1.26), having the mass of the best predictor. Surprisingly, the single regression model with mass as predictor explained only 3.6% of lumbar moment variance less than multiple regression model, with related standard error of prediction 1.46 (mean percentage value of the relative error was only 0.8% higher than at multiple regression model). The obtained findings suggest high prediction potential of mass and height that should be verified on various subject samples.

Development and validation of a novel risk classification tool for predicting long length of stay in NICU blood transfusion infants

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Vibration prediction and analysis of the main beam of the TBM based on a multiple linear regression model

The vibration of tunnel boring machine (TBM) is very difficult to monitor on sites, and related research on prediction methods is rare. Based on the field tunnelling test of a TBM in the Xinjiang Ehe project, the vibration information of the main beam of the TBM under different surrounding rock conditions is collected. The relationships among the tunnelling parameters, surrounding rock parameters and vibration parameters were studied. The results show that the penetration, cutter head speed, torque and thrust are important parameters affecting TBM vibration. In addition, the field penetration index and cutter head driving power index are significantly related to the root mean square of acceleration. Based on this, a multiple regression prediction model of TBM vibration is established. The model was verified and analysed via field projects, and the relative prediction error was less than 12%. This method can be used to predict the vibration of a TBM in real time through characteristic parameters without the use of a traditional monitoring system. This approach is highly important for determining the status of TBM equipment in real time.

Exploration of multiple regression analysis and prediction methods for power grid carbon emission factors

Abstract Carbon emissions from the power sector account for about one-third of China’s total carbon emissions, and carbon emission reduction in the power sector is crucial to the realization of the “double carbon” goal. This paper proposes a prediction model for grid carbon emission factor based on the combination of multiple linear regression models and the GM(1,1) model. The grid carbon measurement model is built using the theory of carbon emission flow, and the grid carbon emission accounting model is built using the consumption side. The average grid carbon emission factor is calculated by dividing the grid carbon emission factor into three different dimensions. The multiple linear regression model is used to study the correlation between the independent variables and the dependent variables, and the specific values of each regression factor are predicted by combining with the GM(1,1) model, and it is judged whether the prediction model is reasonable or not. Among the contributions of the increase in carbon emissions from the power grid, the contribution of electricity consumption increased from 2010 to 2014 and reached a peak of 2.9824 million tons in 2014, and the carbon emission factor value of the power grid gradually decreased from 0.719kg CO2/(kW·h) in 2010 to 0.593kg CO2/(kW·h) in 2022. The MR-GM(1,1) model is applied to the prediction of carbon emissions from power grids, and the absolute error of the results is within 15,000 tons, and the maximum relative error is only 2.42%. The calculation and prediction of carbon emission factors of the power grid can help power grid enterprises to clarify the trend of carbon emission, which is conducive to the realization of the low-carbon goal of “carbon neutral” and “carbon peak”.

Research on energy-saving strategies in the architectural design of the Eastern Railway in the era of artificial intelligence

Abstract This paper takes the air conditioning energy consumption in railroad building design as the main object of analysis and uses an orthogonal experimental design method to study the influencing factors and effects of energy consumption in the Middle East railroad construction design. Stochastic working condition simulation is carried out for the typical models of passenger stations in five climate zones to obtain the data set of the passenger station energy consumption model, and the multiple linear regression prediction models of air conditioning energy consumption in different regions are established. Combined with the hour-by-hour average outdoor temperature, hour-by-hour passenger flow and lighting and related equipment data in the waiting hall of a city in July, the air-conditioning cold load of railroad passenger stations is simulated and analyzed. Explore the implementation effect of energy-saving strategies for railroad passenger station air-conditioning system, and for the traditional PID control strategy, put forward the control strategy based on model prediction, analyze the change of centralized air-conditioning system cold load and the change of the actual performance coefficient of the chiller unit under different control strategies. The data show that compared with the traditional PID control, the model prediction control reduces the total energy consumption by 0.2063, which realizes the energy-saving optimization of the air conditioning system for railroad building design.

Evaluation of the Phytoremediation Potential of the Sinapis alba Plant Using Extractable Metal Concentrations.

Testing the feasibility of soil phytoremediation requires the development of models applicable on a large scale. Phytoremediation mechanisms include advanced rhizosphere biodegradation, phytoaccumulation, phytodegradation, and phytostabilization. The aim of this study was to evaluate the phytoremediation potential of the Sinapis alba. Identification of the factors influencing the extraction process of metals from contaminated soils in a laboratory system suitable for evaluating the phytoavailability of these metals in three solutions (M1-CaCl2, M2-DTPA, and M3-EDTA) included the following: distribution of metals in solution (Kd), soil properties and mobile fractions (SOC, CEC, pH), response surface methodology (RSM), and principal component analysis (PCA). The evaluation of the phytoremediation potential of the Sinapis alba plant was assessed using bioaccumulation coefficients (BACs). The accumulation of heavy metals in plants corresponds to the concentrations and soluble fractions of metals in the soil. Understanding the extractable metal fractions and the availability of metals in the soil is important for soil management. Extractable soluble fractions may be more advantageous in total metal content as a predictor of bioconcentrations of metals in plants. In this study, the amount of metal available in the most suitable extractors was used to predict the absorption of metals in the Sinapis alba plant. Multiple regression prediction models have been developed for estimating the amounts of As and Cd in plant organs. The performance of the predictive models generated based on the experimental data was evaluated by the adjusted coefficient of determination (aR2), model efficiency (RMSE), Durbin-Watson (DW) test, and Shapiro-Wilk (SW) test. The accumulation of the analyzed metals followed the pattern Root > Pods > Leaves > Seeds, stems > Flowers for As and Leaves > Root > Stem > Pods > Seeds > Flowers for Cd in soil contaminated with different metal concentrations. The obtained results showed a phytoremediation potential of the Sinapis alba plant.

Diagenesis and Diagenetic Mineral Control on Reservoir Quality of Tight Sandstones in the Permian He8 Member, Southern Ningwu Basin

The sandstone reservoirs of the He8 member within the Lower Permian Shihezi Formation are important targets for oil and gas exploration in the southern Ningwu Basin. This study utilized thin-section identification, scanning electron microscopy, and X-ray diffraction analysis to examine the petrological features and reservoir characteristics, and evaluate the impact of the mineral composition and diagenesis type on the porosity of the sandstone reservoir. Additionally, a multiple linear regression prediction model was developed to predict the distribution of promising sandstone reservoirs in the study area. The results of the analysis revealed that the sandstone of the He8 member is mainly composed of feldspathic lithic sandstone, followed by lithic sandstone. The main reservoir type is characterized by secondarily dissolved pores and micropores within kaolinite aggregates. The low porosity (ranging from 0.2% to 10.7%) and permeability indicate that the He8 member is a tight sandstone reservoir. This reservoir has undergone compaction, cementation, and dissolution diagenesis, and is presently in the stage of mesodiagenesis B. The rigid framework of quartz, the dissolution of feldspar grains, and the intergranular pores of kaolinite are significant contributors to reservoir quality and the main drivers of porosity. In this study, a multivariate linear regression model was developed based on the mineral content of quartz, feldspar, carbonate minerals, kaolinite, smectite, and rock fragments, which accurately predicts the porosity of the studied reservoirs. Based on this model, it was predicted that the north of the Jingle South sub-depression contains a favorable reservoir space in the tight sandstone reservoir of the He8 member. The findings of this study hold significant reference value in the quantitative characterization of tight sandstone reservoirs with similar depositional and diagenetic characteristics, and improving the prediction effect of favorable reservoirs.

Identifying Postural Instability in Children with Cerebral Palsy Using a Predictive Model: A Longitudinal Multicenter Study.

Insufficient postural control and trunk instability are serious concerns in children with cerebral palsy (CP). We implemented a predictive model to identify factors associated with postural impairments such as spastic or hypotonic truncal tone (TT) in children with CP. We conducted a longitudinal, double-blinded, multicenter, descriptive study of 102 teenagers with CP with cognitive impairment and severe motor disorders with and without truncal tone impairments treated in two specialized hospitals (60 inpatients and 42 outpatients; 60 males, mean age 16.5 ± 1.2 years, range 12 to 18 yrs). Clinical and functional data were collected between 2006 and 2021. TT-PredictMed, a multiple logistic regression prediction model, was developed to identify factors associated with hypotonic or spastic TT following the guidelines of "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis". Predictors of hypotonic TT were hip dysplasia (p = 0.01), type of etiology (postnatal > perinatal > prenatal causes; p = 0.05), male gender, and poor manual (p = 0.01) and gross motor function (p = 0.05). Predictors of spastic TT were neuromuscular scoliosis (p = 0.03), type of etiology (prenatal > perinatal > postnatal causes; p < 0.001), spasticity (quadri/triplegia > diplegia > hemiplegia; p = 0.05), presence of dystonia (p = 0.001), and epilepsy (refractory > controlled, p = 0.009). The predictive model's average accuracy, sensitivity, and specificity reached 82%. The model's accuracy aligns with recent studies on applying machine learning models in the clinical field.

New anergy tide control strategy based on Eviews econometric model

Abstract Along with the limited conventional energy sources and the increasingly prominent environmental problems, new energy sources characterized by “environmental protection” and “renewable” are gradually gaining the attention of many countries in the world. In order to accelerate the development of new energy, countries have introduced control strategies for the new energy industry, but the effect is that the seeds are widely planted and thinly sown. This paper constructs a control strategy for new energy trend based on Eviews econometric model through sliding average model calculation, multiple linear regression prediction, statistics of total new energy, new energy usage and new energy market share calculation. The strategy was also experimented, and the experiments analyzed and predicted the development of new energy in the world and China under the new energy trend. The results show that under the Eviews econometric model, the share of world new energy consumption in the total energy consumption market increases from 25% to 80%, while in China the new energy represented by natural gas grows from 698 million cubic meters to 9,671 million cubic meters, a full 70.76%, and with the development of the economy new energy consumption will rise from 5 billion to 11 billion.

Integrated Model for Predicting Supply Chain Risk Through Machine Learning Algorithms

The machine learning model has become a critical consideration in the supply chain. Most of the companies have experienced vari-ous supply chain risks over the past three years. Earlier risk prediction has been performed by supply chain risk management. In this study, an integrated supply chain operations reference (ISCOR) model has been used to evaluate the organization's supply chain risk. Machine learning (ML) has become a hot topic in research and industry in the last few years. With this motivation, we have moved in the direction of a machine learning-based pathway to predict the supply chain risk. The great attraction of this research is that suppliers will understand the associated risk in the activity. This research includes data pre-processing, feature extraction, data transformation, and missing value replacement. The proposed integrated model involves the support vector machine (SVM), k near-est neighbor (k-NN), random forest (RF), decision tree (DT), multiple linear regression (MLR) algorithms, measured performance, and prediction of supply chain risk. Also, these algorithms have performed a comparative analysis under different aspects. Among the other algorithms, the random forest algorithm achieves an accuracy of 99% and has accomplished superior results with a maxi-mum precision of 0.99, recall of 0.99, and F-score of 0.99 with 1% error rate. The model’s prediction indicates that it can be used to find the supply chain risk. Finally, the limitation and the challenges discussed also provide an outlook for future research direction to perform effective management to mitigate the risk.

Study on the Drying Process and the Influencing Factors of Desiccation Cracking of Cohesive Soda Saline-Alkali Soil in the Songnen Plain, China

The surface of cohesive soda saline-alkali soil in the Songnen Plain of China exhibits obvious desiccation cracking phenomenon during water evaporation. Quantitative research on the process and influencing factors of desiccation cracking are significant for determining the mechanical properties of salt-affected soil, improving the salinization status and the local ecological environment. This study aims to conduct laboratory-controlled cracking experiments on soda saline-alkali soils, and to regularly measure the soil moisture and the cracking status of 40 soil samples. After processing the crack images uniformly, crack characteristics including crack length, crack area, contrast texture feature, and box-counting fractal dimension are extracted. The results indicate that the decrease in soil moisture can be divided into three stages such as fast evaporation stage, slow evaporation stage and stable evaporation stage, and that the evaporation rate shows a linear negative correlation with soil salinity. The measurements also indicate that crack length rapidly increases and reaches stability with a decrease in water content compared with the crack area. The high correlation between different crack characteristics and the soil total salinity (correlation coefficient from 0.44 to 0.95) shows that salt content is the dominant factor affecting the desiccation cracking of soda saline-alkali soil. A multiple linear regression prediction model was finally established based on crack characteristics for different salinity parameters with high prediction accuracy for Na+, EC and total salinity (R2 &gt; 0.91 and RPD &gt; 2.5) and also certain prediction accuracy for Cl− and CO32− (R2 &gt; 0.66 and RPD &gt; 1.5).

Using Baidu Index Data to Improve Chickenpox Surveillance in Yunnan, China: Infodemiology Study.

Chickenpox is an old but easily neglected infectious disease. Although chickenpox is preventable by vaccines, vaccine breakthroughs often occur, and the chickenpox epidemic is on the rise. Chickenpox is not included in the list of regulated communicable diseases that must be reported and controlled by public and health departments; therefore, it is crucial to rapidly identify and report varicella outbreaks during the early stages. The Baidu index (BDI) can supplement the traditional surveillance system for infectious diseases, such as brucellosis and dengue, in China. The number of reported chickenpox cases and internet search data also showed a similar trend. BDI can be a useful tool to display the outbreak of infectious diseases. This study aimed to develop an efficient disease surveillance method that uses BDI to assist in traditional surveillance. Chickenpox incidence data (weekly from January 2017 to June 2021) reported by the Yunnan Province Center for Disease Control and Prevention were obtained to evaluate the relationship between the incidence of chickenpox and BDI. We applied a support vector machine regression (SVR) model and a multiple regression prediction model with BDI to predict the incidence of chickenpox. In addition, we used the SVR model to predict the number of chickenpox cases from June 2021 to the first week of April 2022. The analysis showed that there was a close correlation between the weekly number of newly diagnosed cases and the BDI. In the search terms we collected, the highest Spearman correlation coefficient was 0.747. Most BDI search terms, such as "chickenpox," "chickenpox treatment," "treatment of chickenpox," "chickenpox symptoms," and "chickenpox virus," trend consistently. Some BDI search terms, such as "chickenpox pictures," "symptoms of chickenpox," "chickenpox vaccine," and "is chickenpox vaccine necessary," appeared earlier than the trend of "chickenpox virus." The 2 models were compared, the SVR model performed better in all the applied measurements: fitting effect, R2=0.9108, root mean square error (RMSE)=96.2995, and mean absolute error (MAE)=73.3988; and prediction effect, R2=0.548, RMSE=189.1807, and MAE=147.5412. In addition, we applied the SVR model to predict the number of reported cases weekly in Yunnan from June 2021 to April 2022 using the same period of the BDI. The results showed that the fluctuation of the time series from July 2021 to April 2022 was similar to that of the last year and a half with no change in the level of prevention and control. These findings indicated that the BDI in Yunnan Province can predict the incidence of chickenpox in the same period. Thus, the BDI is a useful tool for monitoring the chickenpox epidemic and for complementing traditional monitoring systems.

Geochemical Characteristics of Cd in Different Parent Soils in Karst Area and Prediction of Cd Content in Maize

The naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil physicochemical properties, soil Cd content, and bioavailability of different parent materials in the karst area, there are certain limitations in using the total soil Cd content to classify the environmental quality of cultivated land. In this study, surface soil and maize samples of eluvium and alluvial parent material in typical karst areas were collected systematically; the contents of maize Cd, soil Cd, pH, and oxides were analyzed, the Cd geochemical characteristics of different parent soils and the influencing factors of their bioavailability were revealed, and scientific and effective arable land use zoning suggestions based on the prediction model were suggested. The results showed that the physicochemical properties of different parent material soils in the karst area were obviously different. The alluvial parent material soil had low Cd content but high bioavailability, and the maize Cd exceeding rate was high. The maize Cd bioaccumulation factor was significantly negatively correlated with soil CaO, pH, Mn, and TC, and the correlation coefficients were -0.385, -0.620, -0.484, and -0.384, respectively. Compared with the multiple linear regression prediction model, using the random forest model to predict the maize Cd enrichment coefficient had higher accuracy and precision. Furthermore, a new scheme for the safe utilization of cultivated land at the plot scale based on soil Cd and predicted crop Cd content was proposed in this study, making full use of arable land resources to ensure crop safety.

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere.The purpose of this study was to create the best O3model for forecasting O3concentrations in the industrial area and to determine the variables that affect O3concentrations.Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3concentration were developed.MLR3had the highest correlation coefficient of 0.792 during development as compared to models MLR1and MLR2. MLR2was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models.The establishment of an O3prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions.