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multiple linear regression model for the prediction of Changma onset date in Korea

INTERNATIONAL JOURNAL OF CLIMATOLOGY 2014年第4期34卷 1000-1010页

作者： Choi, Ki-Seon Kang, Sung-Dae Kim, Hae-Dong Korea Meteorol Adm Natl Typhoon Ctr Cheju South Korea Keimyung Univ Dept Environm Conservat Taegu South Korea

In this research, the multiple linear regression models for the prediction of the Changma onset in Korea have been developed. The predictors are based on correlation analysis between Changma onset dates over 30years (1973-2002), and the winds and geopotential heights at high, middle, and low levels of the troposphere during the preceding 6months (December to May). The clearest correlation was observed at 850 hPa geopotential height field in the preceding April among 6months. The fundamental meaning of this model indicates that Changma in Korea comes earlier when the Mascarene high becomes languished and the tropical storms around India and western North Pacific subtropical high (WNPSH) become strengthened. The reliability of this model has been tested and proved by statistical cross-validation analysis, and additionally, has been analysed for differences in the 850 hPa stream flows for those years that Changma comes earlier and later. From the preceding April onwards, the Mascarene high tends to become weakened so that the cross-equatorial flow is strengthened along the African east coast. The cross-equatorial flow fortifies the tropical storms around Indian as it moves to the east. In the long run, the flow is influenced so that the WNPSH is more developed to the north, influencing Changma rain band to move north faster. To determine why Changma comes later in Korea, the water equivalent of accumulated snow depth (WEASD) difference between the late and early onset phases in the preceding April has been analysed. The result indicated that WEASD had a positive value for most East Asian regions. Since the snow reflects solar radiation by the Albedo effect, it decreases the difference in temperature between continents and oceans by decreasing the surface air temperature. The result has been confirmed by analysing the sensible heat net flux (SHTFL) difference between the two phases. In this way, small temperature gaps between continents and oceans weaken the WNPSH allow

关键词： Changma multiple linear regression model Mascarene high

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multiple linear regression model on Compression Strength of Corrugated Boxes

Multiple Linear Regression Model on Compression Strength of ...

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18th IAPRI World Conference on Packaging

作者： Zhou, Yinghong Zhong, Bangqi Guo, Renhong Guangdong Inspect & Quarantine Technol Ctr Guangzhou 510623 Guangdong Peoples R China Guangdong Entry Exit Inspect & Quarantine Bur Guangzhou 510623 Guangdong Peoples R China

ISBN: (纸本)9781605950846

Using a multiple linear regression model, we analyze test results based on a sample of thousands of batches of corrugated boxes to establish the relation between compression strength and a series of variables such as bursting strength, edgewise crush strength, puncture resistance and ply adhesive strength. Based on the non-linear regression model results, we reveal that the relation between compression strength and other independent variables is more akin to that of a power function. We undertake a logarithmic transformation on the best fitting non-linear regression model to establish a formula for predicting compression strength. To test how well our formula predicts compression strength, we compare real test results with values predicted both by our formula and by traditional formulas such as those of Kellicutt, Makee and Wolf. We find the values predicted by our model are closer to those derived in real tests than are those predicted by the other three formulas considered.

关键词： Corrugated Boxes Compression Strength Cardboard Parameter Tests multiple linear regression model

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multiple linear regression model on Compression Strength of Corrugated Boxes

Multiple Linear Regression Model on Compression Strength of ...

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International Conference on Packaging Technology and Science (ICPTS 2012)

ISBN: (纸本)9783037854785

Using a multiple linear regression model, we analyze test results based on a sample of thousands of batches of corrugated boxes to establish the relation between compression strength and a series of variables such as bursting strength, edgewise crush strength, puncture resistance and ply adhesive strength. Based on the nonlinear regression model results, we reveal that the relation between compression strength and other independent variables is more akin to that of a power function. A logarithmic transformation on the best fitting nonlinear regression model is undertaken to establish a formula for predicting compression strength. To test how well our formula predicts compression strength, we compare real test results with values predicted both by our formula and by traditional formulas such as those of Kellicutt, Makee and Wolf It finds the values predicted by our model are closer to those derived in real tests than are those predicted by the other three formulas considered.

关键词： Corrugated Boxes Compression Strength Cardboard Parameter Tests multiple linear regression model

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A Study of Spatial Soil Moisture Estimation Using a multiple linear regression model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

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REMOTE SENSING 2017年第8期9卷 870-870页

作者： Jung, Chunggil Lee, Yonggwan Cho, Younghyun Kim, Seongjoon Konkuk Univ Dept Civil Environm & Plant Engneering 120 Neungdong Ro Seoul 05029 South Korea Korea Water Resources Corp Hydrometeorol Cooperat Ctr 11 Gyoyookwon Ro Gwacheon Si 13841 Gyeonggi Do South Korea

This study attempts to estimate spatial soil moisture in South Korea (99,000 km(2)) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R-2) of the original LST and observed LST was 0.71, and the R-2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R-2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R-2 values were between 0.28 and 0.67. The reason for R-2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R-2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the v

关键词： spatial soil moisture MODIS LST MODIS NDVI conditional merging multiple linear regression model

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Development of a multiple linear regression model for Meteorological Drought Index Estimation Based on Landsat Satellite Imagery

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WATER 2020年第12期12卷 3393页

作者： Kim, Seon Woo Jung, Donghwi Choung, Yun-Jae Keimyung Univ Dept Civil Engn 1095 Dalgubeol Daero Daegu 42601 South Korea Korea Univ Sch Civil Environm & Architectural Engn 145 Anam Ro Seoul 02841 South Korea GEO C&I Co Ltd Geospatial Res Ctr 435 Hwarang Ro Daegu 41165 South Korea

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R-2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R-2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

关键词： Landsat remote sensing data drought index SPI multiple linear regression model Boryeong

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Spatial distribution of soil moisture estimates using a multiple linear regression model and Korean geostationary satellite (COMS) data

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AGRICULTURAL WATER MANAGEMENT 2019年 213卷 580-593页

作者： Lee, Yonggwan Jung, Chunggil Kim, Seongjoon Konkuk Univ Dept Civil Environm & Plant Engn 1 Hwayang Dong Seoul 05029 South Korea

This study attempts to estimate the spatial distribution of the soil moisture (SM) in South Korea (99,260 km(2)) from January 2013 to May 2015 using a multiple linear regression (MLR) model with data from the Korean multipurpose geostationary Communication, Ocean and Meteorological Satellite (COMS), and land surface temperature (LST) data. The normalized distribution vegetation index (NDVI) from the moderate-resolution imaging spectroradiometer (MODIS) on board the Terra satellite was used to reflect the vegetation variations. Observed precipitation data measured by automatic weather stations (AWSs) operated by the Korea Meteorological Administration (KMA) were collected, and SM data were collected from 38 stations operated by various institutions. The regression coefficients of the MLR model were estimated seasonally considering five days of preceding precipitation. The p-values of all regression coefficients were below 0.05, and all coefficients of determination (R-2) ranged from 0.17 to 0.63. Specifically, the R-2 of loam in the summer was the highest at 0.63, and most of the R-2 values were 0.4 or higher. The results of the SM regression showed that the overall R-2 was higher than 0.4 and that the root mean square error (RMSE) was less than 5% at all but a few stations. A time series analysis of the simulated SM revealed that the observed SM data ranged from 0 to 20% for sand, 10 to 30% for loam, 20 to 40% for clay and 30 to 50% for silt. The simulated SM followed the volatility of the observed data in most of the soils. The spatial distribution of the simulated SM showed the same trends with the observed data on the monthly spatial precipitation map. Consequently, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of drought and the variability in the SM.

关键词： COMS MODIS NDVI multiple linear regression model Spatial distribution of soil moisture Remote sensing

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Economic modeling of mechanized and semi-mechanized rainfed wheat production systems using multiple linear regression model

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Information Processing in Agriculture 2020年第1期7卷 30-40页

作者： Mobin Amoozad-Khalili Reza Rostamian Mahdi Esmaeilpour-Troujeni Armaghan Kosari-Moghaddam Department of Agricultural Economic Qaemshahr BranchIslamic Azad UniversityIran Department of Biosystems Engineering Ferdowsi University of MashhadMashhadIran Department of Biosystems Engineering University of TabrizTabrizIran

Mathematical modeling of economic indices is a challenging topic in crop production *** present study aimed to model the economic indices of mechanized and semimechanized rainfed wheat production systems using various multiple linear regression *** study area was Behshahr County located in the east of Mazandaran Province,Northern *** statistical population included all wheat producers in Behshahr County in 2016/17 crop *** input variables were human labor,machinery,diesel fuel,chemical(chemical fertilizers and chemical pesticides)costs,and the income was considered to be the *** results showed that the cost of wheat production in the semimechanized system was higher than that of the mechanized *** both systems,the highest cost was related to agricultural machinery ***,seed cost was lower in the mechanized system than that of the semi-mechanized *** net return indicator was 993.68$ha1 and 626.71$ha1 for the mechanized and semi-mechanized systems,*** average benefit to cost ratio was 3.46 and 2.40 for the mechanized and semi-mechanized systems,respectively,demonstrating the greater profitability of the mechanized *** results of the evaluation of five types of regression models including the Cobb-Douglas,linear,2FI,quadratic and pure-quadratic for the mechanized and semi-mechanized production systems indicated that in the developed Cobb-Douglas model,the R2-value was higher than that of the quadratic model while RMSE and MAPE of the quadratic model were determined to be smaller than that of the Cobb-Douglas ***,the best model to investigate the relationship between input costs and the income of wheat production in both mechanized and semi-mechanized systems was the quadratic model.

关键词： Rainfed wheat Economic modeling multiple linear regression model Production costs

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THE CONVERGENCE-RATES OF EMPIRICAL BAYES ESTIMATION IN A multiple linear-regression model

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 1995年第1期47卷 81-97页

作者： WEI, LS ZHANG, SP HANGZHOU NORMAL COLL DEPT MATHZHEJIANG 310036PEOPLES R CHINA

Empirical Bayes (EB) estimation of the parameter Vector theta = (beta', sigma(2))' in a multiple linear regression model Y = X beta + epsilon is considered, where beta is the vector of regression coefficient, epsilon similar to N(0, sigma(2)I) and sigma(2) is unknown. In this paper, we have constructed the EB estimators of theta by using the kernel estimation of multivariate density function and its partial derivatives. Under suitable conditions it is shown that the convergence fates of the EB estimators are O(n(-(lambda k-1)(k-2)/k(2k+p+1))), where the natural number k greater than or equal to 3, 1/3 < lambda < 1, and p is the dimension of vector beta.

关键词： EMPIRICAL BAYES ESTIMATION multiple linear regression model CONVERGENCE RATES

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The prediction of carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第4-5期45卷 349-365页

作者： Niu, Lede Lin, Jingzhi Zhou, Lifang Li, Anlin Zhou, Yan Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China Yunnan Normal Univ Sch Tourism & Geog Sci Kunming Peoples R China Yunnan Normal Univ Coll Tourism & Geog Sci Kunming Peoples R China

In order to clarify the quantitative relationship between construction land changes and carbon emissions, a prediction method for carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model was proposed. Taking the central Yunnan urban agglomeration area as the study area, based on the data of construction land from 2011 to 2020, the carbon emission of construction land was predicted by using the multiple linear regression model. There is a positive correlation between the carbon emissions of the central Yunnan urban agglomeration area and the level of construction land use. From 2011 to 2020, average annual growth rate of construction land area was 8.56%, and the average annual growth rate of carbon emissions was 5.75%. The annual growth rate of carbon emissions from 2021 to 2030 is 0.97%, indicating that the government's carbon emission control measures have achieved good results.

关键词： multiple linear regression model STIRPAT model central Yunnan urban agglomeration area construction land carbon emission prediction

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Recovering defective Landsat 7 Enhanced Thematic Mapper Plus images via multiple linear regression model

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IET COMPUTER VISION 2016年第8期10卷 788-797页

作者： Sadiq, Asmaa Sulong, Ghazali Edwar, Loay Univ Technol Malaysia Fac Comp MaGIC X UTM IRDA Digital Media Ctr Utm Skudia Johor Takzin 81310 Malaysia Univ Al Mustansiriyah Coll Sci Dept Comp Baghdad Iraq Univ Baghdad Coll Sci Dept Comp Baghdad Iraq

Since 2003, the scan line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor has failed permanently, inhibiting the retrieval or scanning of 22% of the pixels in each Landsat 7 SLC-off image. This utter failure has seriously limited the scientific applications and usability of ETM+ data. Precise and complete recovery of the missing pixels for the Landsat 7 SLC-off images is a challenging issue and developing an efficient gap-fill algorithm with improved ETM+ data usability has been ever-demanding. In this study, a new gap filling method has been introduced to reconstruct the SLC-off images via multi-temporal SLC-off auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two auxiliary fill images in parallel using the multiple linear regressions model. Both simulated and actual defective Landsat 7 images were tested to assess the performance of the proposed model by comparing with two multi-temporal data based methods, the local linear histogram matching method and Neighbourhood Similar Pixel Interpolator method. The quantitative evaluations indicate that the proposed method makes an accurate estimate of the missing values even for more temporally distant fill images.

关键词： remote sensing geophysical image processing image resolution image enhancement regression analysis infrared imaging image sensors image reconstruction artificial satellites defective Landsat 7 enhanced thematic mapper plus images recovery multiple linear regression model scan line corrector Landsat 7 SLC-off image Landsat 7 enhanced thematic mapper plus sensor ETM+ data usability gap filling method missing value estimation

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