Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network(ANN) and multiple linear regressions(MLR)

Many properties of fruit are influenced by plant nutrition. Fruit firmness is one of the most important fruit characteristics and determines post-harvest life of the fruit, in recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. In the present study, the predictive capabilities of multiple linear regressions （MLR） and artificial neural networks （ANNs） are evaluated to estimate fruit firmness in six months, including each of nutrients concentrations （nitrogen （N）, potassium （K）, calcium （Ca） and magnesium （Mg）） alone （P1）, com- bination of nutrients concentrations （P2）, nutrient concentration ratios alone （P3）, and combination of nutrient concentrations and nutrient concentration ratios （P4）. The results showed that MLR model estimated fruit firmness more accuracy than ANN model in three datasets （P1, P2 and P4）. However, the application of P3 （N/Ca ratio） as the input dataset in ANN model improved the prediction of fruit firmness than the MLR model. Correlation coefficient and root mean squared error （RMSE） were 0.850 and 0.539 between the measured and the estimated data by the ANN model, respectively. Generally, the ANN model showed greater potential in determining the relationship between 6-mon-fruit firmness and nutrients concentration.

基于MLR–ANN算法的地应力场反演与裂缝预测

中国页岩气储层埋藏深,受构造运动影响,地应力分布规律复杂,传统方法很难准确反演区域地应力大小和方向。提出多元线性回归和人工神经网络的耦合算法,对川南长宁—建武区块的页岩气储层及周边地应力场进行反演,并采用综合破裂系数法,对储层裂缝进行预测,划分裂缝发育区域。研究表明,基于多元回归和神经网络的耦合算法能准确反演区域的地应力场分布规律。研究区的地应力以挤压应力为主,方向在NE115°左右。受构造运动产生的断层周边应力较为集中,易发育剪切裂缝,裂缝以发育和较发育程度为主。研究区在邻近龙马溪组底部的五峰组上段和构造大断层部位裂缝发育程度较高。研究成果对该区块完善页岩气开采的井网布置、压裂优化设计和套管损坏防治等有一定的参考价值。

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

A study of the mixed layer of the South China Sea based on the multiple linear regression

Multiple linear regression （MLR） method was applied to quantify the effects of the net heat flux （NHF）, the net freshwater flux （NFF） and the wind stress on the mixed layer depth （MLD） of the South China Sea （SCS） based on the simple ocean data assimilation （SODA） dataset. The spatio-temporal distributions of the MLD, the buoyancy flux （combining the NHF and the NFF） and the wind stress of the SCS were presented. Then using an oceanic vertical mixing model, the MLD after a certain time under the same initial conditions but various pairs of boundary conditions （the three factors） was simulated. Applying the MLR method to the results, regression equations which modeling the relationship between the simulated MLD and the three factors were calculated. The equations indicate that when the NHF was negative, it was the primary driver of the mixed layer deepening; and when the NHF was positive, the wind stress played a more important role than that of the NHF while the NFF had the least effect. When the NHF was positive, the relative quantitative effects of the wind stress, the NHF, and the NFF were about i0, 6 and 2. The above conclusions were applied to explaining the spatio-temporal distributions of the MLD in the SCS and thus proved to be valid.

基于RF和MLR的土壤重金属影响因素分析及生物有效性预测

为探究影响土壤中重金属累积和生物有效性的因素,以桂北地区某铅锌矿流域为研究对象,综合运用单因子指数法、风险评价编码法(RAC)、多元线性回归模型(MLR)和随机森林模型(RF)进行土壤重金属(Pb、Zn、Cu和Cr)累积影响因素分析及生物有效性预测。结果表明:研究区Cr含量无超标且空间分布相对均匀(变异系数为0.51);Cu、Pb和Zn的含量均值(分别为52.58、280.31 mg·kg^(-1)和654.71 mg·kg^(-1))均大于广西西江流域土壤重金属背景值,在思的河山前和地下河入口处全量和生物有效性均较大,对土壤生态环境具有一定风险;对于重金属全量分布和生物有效态的影响因素,阳离子交换量(CEC)、黏粒(Clay)、土壤有机质(SOM)和铁铝氧化物对Cr影响较大,SOM、Clay、pH和铁铝氧化物对Cu影响较大,pH、电导率(EC)和Clay对Pb影响较大,CEC、pH、土壤质地和铁铝氧化物对Zn影响较大;生物有效性预测结果显示RF和MLR均可较好地预测土壤重金属的全量与次生相,其中RF预测的R2区间为0.44~0.93,MLR预测的R2区间为0.30~0.72,RF预测结果表现更为准确。

基于APCS-MLR模型的煤矿开采对地下水的影响定量识别

中国煤炭与水资源储量呈逆向分布,煤炭基地水资源相对短缺,生态环境脆弱。随着煤炭资源的大规模和高强度开发,区域地下水环境问题越发凸显。为定量识别煤矿开采对地下水的影响程度,以蒙东能源基地某矿区为例,通过采集矿区周边地下水化学样品进行测试,系统分析了研究区地下水水化学特征,采用相关性分析、PCA等多元统计方法确定了地下水的影响因子,据此建立了基于绝对因子得分-多元线性回归法(APCS-MLR)的定量识别模型,对研究区地下水受煤矿开采的影响贡献进行了计算分析。结果显示:研究区浅层地下水pH值为6.52~7.86,平均7.27,TDS为126.14~2240.34mg/L,平均为638.18 mg/L。主要阳离子平均含量Na^(+)>Ca^(2+)>Mg^(2+)>K^(+),主要阴离子平均含量HCO_(3)^(-)>Cl^(-)>SO_(4)^(2-)>NO_(3)^(-)。其中Cl^(-)和SO_(4)^(2-)的含量分别为4.25~779.77 mg/L和0~483.20 mg/L,其变异系数均大于100%。SO_(4)^(2-)与Na^(+)、Ca^(+)、Mg^(2+)、Cl^(-)存在显著正相关关系(r>0.72,P<0.01),TDS与SO_(4)^(2-)、Na^(+)、Ca^(+)、Mg^(2+)、Cl^(-)存在显著正相关关系。多项指标显示研究区地下水水质已经受到了煤矿开采的影响。主成分分析(PCA)解析了4个地下水影响因子,分别为煤炭开采影响因子、自然因素的硅酸盐溶解因子、自然因素的反硝化作用和农业活动的化肥使用,其占总荷载的37.061%、16.067%、14.807%和8.775%。以SO_(4)^(2-)作为煤矿开采对地下水影响的表征因子,构建了SO_(4)^(2-)来源计算分析的APCS-MLR定量识别模型。通过最小二乘法计算得到模型的各项参数,确定SO_(4)^(2-)的实际浓度和预测浓度拟合曲线为y=0.9716x+2.9702(R^(2)=0.9759),说明构建的回归方程符合实际,效果良好。据此计算了4个地下水影响因子的贡献比分别为79.3%、6.06%、2.00%和9.96%,其他未识别的因子占比2.67%。分析了煤矿开采影响地下水水质的主要方式为形成降落漏斗影响周边水化学场以及外排含有特殊组分的矿井水进而影响地下水水质。因此需要采取合理措施控制煤矿开采产生的降落漏斗范围继续扩大,必要时对已经产生的漏斗进行恢复治理,同时加强对高盐、高SO_(4)^(2-)矿井水的处理和排放管理,研究成果可为煤炭绿色开发和环境高质量发展提供技术支持。

Application of Multiple Linear Regression and Manova to Evaluate Health Impacts Due to Changing River Water Quality

Rivers are important systems which provide water to fulfill human needs. However, excessive human uses over the years have led to deterioration in quality of river causing, causing health problems from contaminated water. This study focuses on the application of statistical techniques, Multiple Linear Regression model and MANOVA to assess health impacts due to pollution in Cauvery river stretch in Srirangapatna. In this study, using Multiple Linear Regression, it is found that health impact level is 60.8% dependent on water quality parameters of BOD, COD, TDS, TC and FC. The t-statistics and their associated 2-tailed p-values indicate that COD and TDS produces health impacts compared to BOD, TC and FC, when their effects are put together across all the six sampling stations in Srirangapatna. Further Pearson correlation Matrix shows highly significant positive correlation amongst parameters across all stations indicating possibility of common sources of origin that might be anthropogenic. Also graphs are plotted for individual parameters across all stations and it reveals that COD and TDS values are significant across all sampling stations, though their values are higher in impact stations, causing health impacts.

Statistical analysis of nitrogen use efficiency in Northeast China using multiple linear regression and Random Forest

Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression

Foam drilling is increasingly used to develop low pressure reservoirs or highly depleted mature reservoirs because of minimizing the formation damage and potential hazardous drilling problems. Prediction of the cuttings concentration in the wellbore annulus as a function of operational drilling parameters such as wellbore geometry, pumping rate, drilling fluid rheology and density and maximum drilling rate is very important for optimizing these parameters. This paper describes a simple and more reliable artificial neural network (ANN) method and multiple linear regression (MLR) to predict cuttings concentration during foam drilling operation. This model is applicable for various borehole conditions using some critical parameters associated with foam velocity, foam quality, hole geometry, subsurface condition (pressure and temperature) and pipe rotation. The average absolute percent relative error (AAPE) between the experimental cuttings concentration and ANN model is less than 6%, and using MLR, AAPE is less than 9%. A comparison of the ANN and mechanistic model was done. The AAPE values for all datasets in this study were 3.2%, 8.5% and 10.3% for ANN model, MLR model and mechanistic model respectively. The results show high ability of ANN in prediction with respect to statistical methods.

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.

Analysis of the Invariance and Generalizability of Multiple Linear Regression Model Results Obtained from Maslach Burnout Scale through Jackknife Method

The purpose of this study was to examine the burnout levels of research assistants in Ondokuz Mayis University and to examine the results of multiple linear regression model based on the results obtained from Maslach Burnout Scale with Jackknife Method in terms of validity and generalizability. To do this, a questionnaire was given to 11 research assistants working at Ondokuz Mayis University and the burnout scores of this questionnaire were taken as the dependent variable of the multiple linear regression model. The variable of burnout was explained with the variables of age, weekly hours of classes taught, monthly average credit card debt, numbers of published articles and reports, gender, marital status, number of children and the departments of the research assistants. Dummy variables were assigned to the variables of gender, marital status, number of children and the departments of the research assistants and thus, they were made quantitative. The significance of the model as a result of multiple linear regressions was examined through backward elimination method. After this, for the five explanatory variables which influenced the variable of burnout, standardized model coefficients and coefficients of determination, and 95% confidence intervals of these values were estimated through Jackknife Method and the generalizability of the parameter estimation results of these variables on population was researched.

Predicting urbanization level by main element analysis and multiple linear regression---taking Xiantao district in Hubei Province as an example

In this paper we firstly select main factors relating to urbanization level of Xiantao District in Hubei Province by main element, then, make model of urbanization level by analysis of multiple liner regression, and lastly predict its urbanization level

Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks

Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μD, the energy of the highest occupied molecular orbital EHOMO, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R2=91.28%, R2aj=89.11%, RMCE = 0.2831, R2ext=86.50% and R2=98.22%, R2aj=97.75%, RMCE = 0.1131, R2ext=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.

基坑开挖诱发侧方隧道变形的PCA-MLR预测方法

研究基坑开挖对侧方隧道变形的影响是岩土工程领域的热点与难点问题。为预测基坑开挖诱发临近侧方隧道变形,首先通过主成分分析方法(PCA)对侧方隧道变形指标影响因素进行降维处理并获取其主成分指标,然后采用多元线性回归方法(MLR)建立隧道变形指标与主成分指标间的关系模型,最后基于工程实例验证提出的PCA-MLR预测模型的正确性。研究结果表明:PCA-MLR模型考虑了基坑开挖诱发侧方隧道变形的高维度影响因素耦合作用,且实现了低维度主成分指标与变形指标间的关系模型建立。采用PCA-MLR模型预测的隧道最大水平及竖向位移与实测值的相对误差均小于10%,验证了提出模型的正确性与适用性。

Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu

Efficiency and precision in prediction of Chlorophyll-a using this model is still a pandemic among researchers, due to the natural conditions in ocean water systems itself, which involved chemical, biological and physical processes and interaction among them may affect the model performance drastically. Thus, to overcome this problem as well as to improve the strength of MLR, we proposed a hybrid approach, i.e., an Artificial Neural Network to the MLR coins as Artificial Neural Network-Multiple Linear Regression (ANN-MLR). To investigate the performance of the proposed model, we compared Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and proposed hybrid Artificial Neural Network and Multiple Linear Regression (ANN-MLR) in the prediction of chlorophyll-a (chl-a) concentration by statistical measurement which are MSE and MAE. Achieving our objectives of study, we used 4 parameters, i.e. temperature (°C), pH, salinity (ppt), DO (ppm) at the Offshore Kuala Terengganu, Terengganu, Malaysia. The results showed that our proposed model can improve the performance of the model as compared to ANN and MLR due to small errors generated, error reduced, and increased the correlation coefficient for all parameters in both MSE and MAE, respectively. Thus, this result indicated that our proposed model is efficient, precise and almost perfect correlation as compared to ANN and MLR.

Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Research on the Impact of Employment on GDP Based on Multiple Linear Regression Model

In order to study the impact of employed persons in various industries on regional GDP,based on the data of GDP in various regions and employed persons divided by industries in various regions in 2019,the employed persons are divided into seven categories,and the multiple linear regression model of GDP in various regions of China on employed persons in various industries is established by using the methods of multiple linear regression analysis and cluster analysis,It also analyzes the impact of employees in various industries on the GDP of various regions.

基于CNN-GRU-MLR的多频组合短期电力负荷预测

负荷预测对于电力企业制定未来调度计划十分重要。为了进一步提高预测精度,充分挖掘负荷数据中时序特征的联系,提出一种卷积神经网络(Convolutional Neural Networks,CNN)、门控循环单元(Gate Recurrent Unit,GRU)和多元线性回归(Multiple Linear Regression,MLR)混合的多频组合电力负荷预测模型。该模型先对时间序列的负荷数据进行集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD),并将其重构为高低两种频率;同时在高频中引入影响因子较大的气象因素,使用CNN-GRU模型预测,低频部分使用多元线性回归进行预测;最后将各个模型得出的预测结果叠加,得到最终预测结果。仿真结果表明,相对于其它网络模型,提出的混合模型具有更高的预测精度,是一种有效的短期负荷预测方法。