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International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Chen, Jing Wang, Jianbo Meteorol Technol & Equipment Ctr Hunan Prov Changsha 410007 Hunan Peoples R China

ISBN: (纸本)9781728144634

With the frequent occurrence of haze weather, the concentration prediction of PM2.5, the main pollutant in haze weather, has gradually become a hot topic. Based on the analysis of historical data of PM2.5 and related weather information in Changsha City, this paper establishes a multivariate linear regression model to predict the concentration of PM2.5. The validity of the model is verified by comparing the predicted value with the observed value. The model has a good application value for the prediction of PM2.5 concentration.

关键词： PM2.5 multiple linear regression model

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Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model

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ATMOSPHERIC ENVIRONMENT 2011年第3期45卷 634-640页

作者： Shu, Yugin Lam, Nina S. N. S China Normal Univ Sch Geog Sci Guangzhou 510631 Guangdong Peoples R China Louisiana State Univ Dept Environm Sci Baton Rouge LA 70820 USA

Detailed estimates of carbon dioxide emissions at fine spatial scales are critical to both modelers and decision makers dealing with global warming and climate change. Globally, traffic-related emissions of carbon dioxide are growing rapidly. This paper presents a new method based on a multiple linear regression model to disaggregate traffic-related CO2 emission estimates from the parish-level scale to a 1 x 1 km grid scale. Considering the allocation factors (population density, urban area, income, road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions, and developed the best-fit model. The method was applied to downscale the traffic-related CO2 emission values by parish (i.e. county) for the State of Louisiana into 1-km(2) grid cells. In the four highest parishes in traffic-related CO2 emissions, the biggest area that has above average CO2 emissions is found in East Baton Rouge, and the smallest area with no CO2 emissions is also in East Baton Rouge, but Orleans has the most CO2 emissions per unit area. The result reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. The proposed method can be used to identify the emission "hot spots" at fine scale and is considered more accurate and less time-consuming than the previous methods. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： multiple linear regression model Carbon dioxide emissions Spatial disaggregation

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An Empirical Analysis of Poverty Reduction Factors in National-Level Poverty-Stricken Counties Taking Sichuan Province as an Example

An Empirical Analysis of Poverty Reduction Factors in Nation...

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作者： Li Lei Chenxi Liu Zhenyi Ji Zhixin Shu Sichuan Agricultural University

Poverty has always been the development priority in China. Sichuan, a key target of national poverty alleviation and reduction, is vital for the overall poverty alleviation across China. Based on the data of poverty-stricken counties in Sichuan from 2013 to 2017, this paper employs multiple linear regression model to analyze the factors affecting residents' saving deposit balance. Results show that the proportion of working population in the secondary industry and the tertiary industry, per capita oil production, the proportion of fixed-line telephone significantly sway the saving deposit of residents. Relevant policy suggestions are also proposed based on the results.

关键词： national-level poverty-stricken counties rural poverty factors multiple linear regression model

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Forecasting of Currency Exchange Rate Using Artificial Neural Network: A Case Study of Solomon Island Dollar 16th

Forecasting of Currency Exchange Rate Using Artificial Neura...

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16th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Kimata, James D. Khan, M. G. M. Sharma, Anuraganand Rashid, Mahmood A. Tekabu, Tokaua Univ South Pacific Suva Fiji Victoria Univ Melbourne Werribee Vic 3030 Australia

ISBN: (纸本)9783030298944;9783030298937

The use of neural network models for currency exchange rate forecasting has received much attention in recent time. In this paper, we propose an exchange rate forecasting model based on artificial neural network. We tested our model on forecasting the exchange rate of Solomon Islands Dollar against some major trading currencies of the country such as, Australian Dollar, Great Britain Pound, Japanese yen, and Euro. We compared the performance of our model with that of the single exponential smoothing model;the double exponential smoothing with trend model;and Holt-Winter multiplicative and additive seasonal and multiple linear regression model. The performance of the models was measured using the error function, root mean square error (RMSE). The empirical result reveals that the proposed model is more efficient and accurate in forecasting currency exchange rate in comparison to the regression and time series models.

关键词： Forecasting exchange rate Neural network model multiple linear regression model Time series models Naive method

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Temperature Compensation of Laser Triangular Displacement Sensor

Temperature Compensation of Laser Triangular Displacement Se...

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Chinese Automation Congress (CAC)

作者： Li, Zhi Chen, Xiao Liu, Yuanzhi Tao, Wei Zhao, Hui Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781728140940

The measurement precision of the laser triangulation displacement sensor will be decreased with temperature, its application areas have been restricted for a long time. Three main factors affecting measurement accuracy were analyzed and two algorithms were proposed in this paper to compensate for the temperature error. Experiments of the three aspects such as measurement distance, chip temperature and ambient temperature are shown. In addition, a temperature compensation model based on multiple linear regression was proposed. Considering that various influencing factors are not simple coupling, a neural network technology that can effectively solve nonlinear problems was used and a three-layer neural network model was also built, which can generate temperature compensation values. At the same time, the results of the two algorithms were compared. Results show that the neural network model reduces the displacement temperature drift from the original -80 mu m similar to+50 mu m to within +/- 20 mu m in the range of 0 similar to 40 degrees C, which significantly improves the temperature adaptability of the laser displacement sensor and is successfully applied in the aerospace field.

关键词： Laser triangle Temperature compensation multiple linear regression model Neural network

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Soil salinization mapping across different sandy land-cover types in the Shiyang River Basin: A remote sensing and multiple linear regression approach

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2022年 28卷

作者： Ngabire, Maurice Wang, Tao Xue, Xian Liao, Jie Sahbeni, Ghada Huang, Cuihua Duan, Hanchen Song, Xiang Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Desert & Desertificat Lanzhou Peoples R China Eotvos Lorand Univ Dept Geophys & Space Sci Pazmany Peter Stny 1-A H-1117 Budapest Hungary

Soil salinization has a critical impact on land in arid and semi-arid regions. Consequently, mapping and monitoring saline soils have been the subject of growing attention for many scientists due to the irregular spectral reflectivity related to geographic locations and landscape characteristics, which are challenging for policy-makers to improve and sustain the ecological balance. This study applied geoinformation techniques combined with multiple linear regression modeling to map soil salinity over different land-cover types in the Shiyang River Basin. Based on field knowledge and area accessibility, eighty samples were collected, then Kennard-Stone (K-S) algorithm was used for sample partition, 70% for training, and 30% for validation. Variance Inflation Factor (VIF) was applied to identify multicollinearity and adjust the model covariates to ensure proper specification and functioning. Mobile sand, water channels, built-up, and mountainous areas were masked. The results revealed the model's high performance with a coefficient of determination R2 of 0.898, a probability level of 95%, a Root Mean Square Error (RMSE) of 1.653, a Ratio of the Performance to the Interquartile range (RPIQ) of 4.182, and standard error of Electric conductivity (EC) laboratory measurements to the standard error of the predicted EC (SE Lab/SE Pred) of 1.048. Overall, an area of 13834.59 km2, accounting for 33.54% of the entire study region, is under salinization threat. The basin's lower reach is the most affected, with 9576.25 km2, accounting for 57.64% of the eco-region, and 4258.34 km2, around 32.12% of land in the middle reach, suffering from soil salinization. 80.46% of farmland in the lower reach and 35.99% in the middle reach are saline soils. Around 98.9% of Gobi in the lower reach and 91.55% in the middle reach suffer from soil salinization, 96.62% of rangeland in the lower reach and 44.62% in the middle are under salinization risk. Generally, the highest salinity values were re

关键词： Desertification multiple linear regression model Remote sensing Soil salinization The shiyang river basin

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A regression Analysis on the Effects of Factors on Plastic Waste Production

A Regression Analysis on the Effects of Factors on Plastic W...

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作者： Junnan Yang Lingchang Zhang Weitao Wang Zheyong Qiu School of Science Hangzhou Dianzi University School of Automation Hangzhou Dianzi University

Plastic is a multi-purpose material produced in large ***,the nondegradability of plastic waste also brings many negative *** order to solve the problem of plastic production waste,we first analyze which factors have a greater impact on the production of plastic waste,and for this reason,a multiple linear regression has been *** quantify abstract influencing *** that,the data is put into Statistical Product and Service Solutions(SPSS) for processing,and the influence of different factors on plastic waste is *** find that the production of plastics has the greatest impact on the increase of plastic waste,while mainland policies have the greatest impact on the reduction of plastic ***,we have a sensitivity analysis on this model to analyze the impact of various factors on the output of plastic waste.

关键词： Plastic production waste multiple linear regression model Statistical Product and Service Solutions

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Comparison of public perception and risk management decisions of aircraft noise near Taoyuan and Kaohsiung International Airports

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2020年 85卷 101797-101797页

作者： Yang, Ya Ling Chang Jung Christian Univ Dept Aviat & Maritime Transportat Management Tainan 71101 Taiwan

This study uses multiple linear regression to identify factors contributing to perceived risk among residents near Taoyuan and Kaohsiung International Airports, the effect of perceived risk on their willingness to reduce risk, and consumption preferences that can reduce risk. Results indicated that residents' risk perception near Taoyuan Airport is lower than that near Kaohsiung Airport. Noise pollution experience, perceived probability of environmental contamination and negative effects, and perceived severity of catastrophic consequences significantly increase residents' perceived risks. Residents are willing to recognize and participate in mitigating the risks of aircraft noise pollution. The more risk residents perceive, the more willing they are to participate in disaster reduction and investigate means of improving the risk environment.

关键词： Aircraft noise pollution Risk perception Taoyuan international airport Kaohsiung international airport multiple linear regression model

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User Usable Experience: A three-dimensional approach on usability in tourism websites and a model for its evaluation

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TOURISM MANAGEMENT PERSPECTIVES 2020年 33卷

作者： Martinez-Sala, Alba-Maria Monserrat-Gauchi, Juan Alemany-Martinez, Dolores Univ Alicante Sch Econ & Business Sci Dept Commun & Social Psychol San Vicente Raspeig Rd Alicante 03690 Spain

A standardized website evaluation model is needed in the tourism sector. This research article aims at revising previous models and updating them to contribute with a unified evaluation model for the analysis of web quality that incorporates a three-dimensional approach on usability, since usability is closely related to graphic design and navigability. This perspective has not been stated before. To test this correlation, a model to evaluate User Usable Experience (UUX) which integrates this three-dimensional approach on usability is proposed and a set of indicators that have been devised from a close bibliographic revision of previous web analysis models is shown. Its application to a purposive sampling verifies the positive correlation among the three above mentioned parameters by means of a multiple linear regression model. The results confirm the need to analyse UUX from a three-dimensional perspective on usability.

关键词： Graphic design multiple linear regression model Navigability Tourism Official tourism websites Usability Website evaluation

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Comparison of hyperspectral transformation accuracies of multispectral Landsat TM, ETM plus , OLI and EO-1 ALI images for detecting minerals in a geothermal prospect area

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年第Mar.期137卷 15-28页

作者： Nguyen Tien Hoang Koike, Katsuaki Kyoto Univ Grad Sch Engn Dept Urban Management Lab Environm Geosphere Engn Katsura C1-2-215 Kyoto 6158540 Japan Hue Univ Coll Sci Dept Environm Sci 77 Nguyen Hue Hue Vietnam

Hyperspectral remote sensing generally provides more detailed spectral information and greater accuracy than multispectral remote sensing for identification of surface materials. However, there have been no hyperspectral imagers that cover the entire Earth surface. This lack points to a need for producing pseudo-hyperspectral imagery by hyperspectral transformation from multispectral images. We have recently developed such a method, a Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), which transforms Landsat 7 ETM+ images into pseudo-EO-1 Hyperion images using multiple linear regression models of ETM+ and Hyperion band reflectance data. This study extends the PHITA to transform TM, OLI, and EO-1 ALI sensor images into pseudo-Hyperion images. By choosing a part of the Fish Lake Valley geothermal prospect area in the western United States for study, the pseudo-Hyperion images produced from the TM, ETM+, OLI, and ALI images by PHITA were confirmed to be applicable to mineral mapping. Using a reference map as the truth, three main minerals (muscovite and chlorite mixture, opal, and calcite) were identified with high overall accuracies from the pseudo-images (>95% and >42% for excluding and including unclassified pixels, respectively). The highest accuracy was obtained from the ALI image, followed by ETM+, TM, and OLI images in descending order. The TM, OLI, and ALI images can be alternatives to ETM+ imagery for the hyperspectral transformation that aids the production of pseudo-Hyperion images for areas without high-quality ETM+ images because of scan line corrector failure, and for long-term global monitoring of land surfaces. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Hyperion image Hyperspectral transformation Reflectance spectrum multiple linear regression model Hydrothermal alteration minerals Mineral mapping

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