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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

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Geo-Spatial Information Science 2022年第3期25卷 353-364页

作者： Abdelkrim Bouasria Khalid Ibno Namr Abdelmejid Rahimi El Mostafa Ettachfini Badr Rerhou Faculty of Sciences Chouaib Doukkali UniversityEl JadidaMorocco Agricultural Development Department Doukkala Regional Agricultural Development OfficeEl JadidaMorocco Soil Science Department Hassan II Agronomic and Veterinary InstituteRabatMorocco

In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using multiple linear regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.

关键词： Digital soil mapping soil organic matter remote sensing multiple linear regression artificial neural networks irrigated area Doukkala Morocco

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multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

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Frontiers of Structural and Civil Engineering 2017年第1期11卷 90-99页

作者： Faezehossadat KHADEMI Mahmoud AKBARI Sayed Mohammadmehdi JAMAL Mehdi NIKOO Department of Civil Architectural and Environmental Engineering Illinois Institute of Technology Chicago IL 60616 USA Civil Engineering Department University of Kashan Kashan *** Iran Department of Civil Engineering University of Hormozgan Bandar Abbas 3995 IRAN Ahvaz Branch Islamic Azad University Ahvaz *** IRAN

Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model （MLR）, artificial neural network （ANN）, and adaptive neuro-fuzzy inference system （ANFIS） are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis （SA） for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete 28 days compressive strength multiple linear regression artificial neural network ANFIS sensitivity analysis （SA）

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multiple linear regression Application on the Inter-Network Settlement of Internet

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The Journal of China Universities of Posts and Telecommunications 2006年第2期13卷 103-107页

作者： YANG Qing-feng ZHANG Qi-xiang Lǖ Ting-jie School of Management and Economics Beijing University of Posts and Telecommunications Beijing 100876 P.R. China

This paper develops an analytical framework to explain the lnternet interconnection settlement issues. The paper shows that multiple linear regression can be used in assessing the network value of lnternet Backbone Providers （ IBPs）. By using the exchange rate of each network, we can define a rate of network value, which reflects the contribution of each network to interconnection and the interconnected network resource usage by each of the network.

关键词： multiple linear regression network value internet settlement

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

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Journal of Systems Engineering and Electronics 2011年第4期22卷 615-620页

作者： Pingping Xiong Yaoguo Dang Xianghua wu Xuemei Li College of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing 210016 P. R. China College of Mathematics and Physics Nanjing University of Information Science and Technology Nanjing 210044 P. R. China

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.

关键词： multi-variable grey model （MGM（1,m）） backgroundvalue optimization multiple linear regression combined predic-tion model.

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

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Acta Oceanologica Sinica 2012年第6期31卷 19-31页

作者： DUAN Rui YANG Kunde MA Yuanliang HU Tao College of Marine Northwestern Polytechnical UniversityXi'an 710072China Key Laboratory of Underwater Acoustic Environment Institute of AcousticsChinese Academy of SciencesBeijing 100190China

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.

关键词： mixed layer multiple linear regression South China Sea vertical mixing model

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Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by multiple linear regression and Artificial Neural Networks

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Computational Chemistry 2022年第4期10卷 186-202页

作者： Yafigui Traoré Jean Missa Ehouman Mamadou Guy-Richard Koné Donourou Diabaté Nahossé Ziao Laboratory of Thermodynamics and Environmental Physico-Chemistry Nangui Abrogoua University Abidjan Ivory Coast Training and Research Unit Biological Sciences University Peleforo Gon Coulibaly Korhogo Ivory Coast Ivorian Research Group in Disease Modeling (GIR2M) Abidjan Ivory Coast

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.

关键词： Anti-Inflammatory Activity multiple linear regression Artificial Neural Network QSAR

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Research on the Impact of Employment on GDP Based on multiple linear regression Model

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经济管理学刊（中英文版） 2022年第1期11卷 1-8页

作者： Wei Zheng Yao Xu Jun Yang Shuhuan Yang School of mathematics and computer science Chuxiong Normal UniversityChuxiong675000China

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.

关键词： GDP Employees in Various Industries multiple linear regression

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The Influence of Internet Use on Women’s Depression and Its Countermeasures—Empirical Analysis Based on Data from CFPS

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International Journal of Mental Health Promotion 2024年第3期26卷 229-238页

作者： Dengke Xu Linlin Shen Fangzhong Xu School of Economics Hangzhou Dianzi UniversityHangzhou310018China School of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China

Based on China Family Panel Studies(CFPS)2018 data,the multiple linear regression model is used to analyze the effects of Internet use on women’s depression,and to test the robustness of the regression results.At the same time,the effects of Internet use on mental health of women with different residence,age,marital status and physical health status are analyzed.Then,we can obtain that Internet use has a significant promoting effect on women’s mental health,while the degree of Internet use has a significant inhibitory effect on women’s mental health.In addition,the study found that women’s age,education,place of residence,marital status,length of sleep,working status and physical health status are the main factors affecting the mental health of Chinese women.In the heterogeneity investigation of residence,age,marital status and physical health status,Internet use has a greater negative impact on the Center for Epidemiological Studies Depression Scale(CES-D8)scores of women in rural areas,has a significant positive impact on the mental health of middle-aged and elderly women or women with spouses,and has a positive impact on the mental health of physically unhealthy women.Therefore,in view of women’s mental health needs and the problems existing in the use of the Internet,this paper puts forward some suggestions to further improve the overall mental health level of women.

关键词： Internet use depression multiple linear regression heterogeneity

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Construction of multiple linear regression Prediction Model of PRETCO-A Scores and Its Positive Backwash Effect on Teaching

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国际计算机前沿大会会议论文集 2021年第2期 491-502页

作者： Haiyun Han Yuanhui Li Sanya Aviation&Tourism College Sanya Hainan 572000China

PRETCO-A is a standardized English proficiency test set up to evaluate the English application ability of the students in higher vocational college.In order to improve the passing rate,a multiple linear regression prediction model is constructed in this paper.A significance test was first performed on the regression model and the regression coefficient to verify a high correlation among the variables.The confirmed model was then put into application to predict the students’scores and identify the students who may fail the exam,leading to targeted tutoring assistance given to those students in advance.Finally,60 students with predicted scores lower than 60 points were selected as research samples,and randomly divided into the control group and the experimental group,30 students in each group.Finally,the experimental group students were given 40 teaching hours of precision assistance and targeted training,while the control group did not engage in any teaching intervention.The experimental results indicate that the pass rate of experimental group is 20%higher than the control group,which means the backwash effect of the test prediction is positive.The prediction model is proved to be scientific and reliable for teaching.

关键词： PRETCO-A multiple linear regression Prediction model Significance test Backwash effect Targeted teaching assistance

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Using multiple linear regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

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Open Journal of Medicinal Chemistry 2013年第1期3卷 7-15页

作者： Rokaya Mouhibi Mohamed Zahouily Khalid El Akri Naima Hanafi Laboratoire de Catalyse Chimiométrie et EnvironnementFacultédes Sciences et Techniques Mohammedia(URAC24)UniversitéHassan Ⅱ Mohammedia-CasablancaCasablancaMaroc Laboratoire de Chimie Organique Catalyse et EnvironnementFacultédes Sciences Ben M’sikUniversitéHassan Ⅱ Mohammedia-CasablancaCasablancaMaroc

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.

关键词： Artificial Neural Network Descriptors CCR5 multiple linear regression Structure-Activity Relationship

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