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The relationship between TEC and Earth's magnetic field during quiet and disturbed days over Istanbul, Turkey

ADVANCES IN SPACE RESEARCH 2020年第9期65卷 2167-2171页

作者： Ozcan, O. Sagir, S. Atici, R. Firat Univ Fac Sci Dept Phys TR-23000 Elazig Turkey Mus Alparslan Univ Dept Elect & Automat TR-49250 Mus Turkey Mus Alparslan Univ Fac Educ TR-49250 Mus Turkey

In this study, the impact of Earth's magnetic field on total electron content (TEC) was studied by using statistical multiple linear regression model and co-integration method. TEC values were measured over the Turkey-Istanbul (ista) station using date of global positioning system (GPS), and the magnetic field components of the Earth were measured from Bogazici University, Kandilli Observatory and Earthquake Research Institute, Geomagnetic observatory Istanbul (ISK) station. This examination has been carried out during the dates of March 14-19, 2015 covering the dates of March 17-18, 2015 known in the literature as St. Patrick's Day geomagnetic storm. The three days before the storm (March 14-16) were named as quiet days, whereas the other days (March 17-19) were named as disturbed days after which the two periods were examined separately. It was observed as a result of the examination that the x-component (south-north direction) of the magnetic field had a negative impact on TEC on quiet days, whereas the impact was positive on disturbed days. However, the y-component (east-west direction) of the magnetic field had an inverse relationship of the x-component on the quiet and disturbed days. In addition, it was deduced that the impact coefficient of the x and y-component of the magnetic field was higher on disturbed days in comparison with those on quiet days. The correlation coefficient between the TEC and the components of the Earth's magnetic field was 0.11 on quiet days and 0.95 on disturbed days. Therefore, it can be stated that the relationship of the TEC values with the geomagnetic field are higher on disturbed days. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Earth's magnetic held Ionospheric TEC multiple linear regression model

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Sorption Capacity of Pesticides on Soil in a Predominant Apple Cultivation Area

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SOIL & SEDIMENT CONTAMINATION 2020年第1期29卷 107-119页

作者： Kumari, Dev John, Siby Punjab Engn Coll Civil Engn Dept Sect 12 Chandigarh India

Laboratory-based batch sorption experiments were performed using six pesticides and 12 soil samples collected from the predominant apple cultivation areas of Kinnaur district, Himachal Pradesh (India). Freundlich isotherm model was used to determine the sorption coefficients. Soil properties such as texture, pH, organic matter content (OM), cation exchange capacity (CEC), total dissolved solids (TDS), and clay content were measured for different soil samples. The principal component analysis showed that the sorption of pesticides was considerably affected by organic matter in the soil. multiple linear regression models were proposed correlating the sorption coefficients and soil properties. The models and maps developed in this study could be used to estimate the mobility risk of pesticides and to evolve pesticide pollution management plans.

关键词： Pesticides sorption soil properties GIS mapping multiple linear regression model

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A comparison of forecasting models for the resource usage of MapReduce applications

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NEUROCOMPUTING 2020年 418卷 36-55页

作者： Li, Yang Yuan Tien Van Do Nguyen, Hai T. Xian SiYuan Univ Xian Peoples R China Baoji Univ Arts & Sci Baoji Peoples R China Budapest Univ Technol & Econ Dept Networked Syst & Serv Magyar Tudosok Korutja 2 H-1117 Budapest Hungary Budapest Univ Technol & Econ Balatonfured Student Res Grp Budapest Hungary

In this paper, we construct forecasting models (multivariate long short-term memory recurrent neural networks and multiple linear regression) for the resource usage prediction of four MapReduce applica-tions and applications executed within the Spark framework. We have evaluated the impact of a sample size to prediction accuracy. Also, we propose a phase modelling approach for read/write-intensive applications. Our results show that models based on long short-term memory recurrent neural networks exhibit a higher accuracy than multiple linear regression models and the intensive characteristics of a resource are closely related to the prediction accuracy of forecasting models. We investigated the hyper parameter tuning of such models and showed that a randomly initialised, shallow, well-tuned network may outperform deeper models that use stacked autoencoder initialisation. Furthermore, multivariate long short-term memory recurrent neural network models are more sensitive to sample size than multiple linear regression models. We show that an LSTM model trained in a specific machine may be used to predict the resource usage in another machine. (C) 2020 Elsevier B.V. All rights reserved.

关键词： MapReduce application Resource usage parameters LSTM model multiple linear regression model

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The Behavior of Cadmium Leaching from Contaminated Soil by Nitrilotriacetic Acid: Implication for Cd-Contaminated Soil Remediation

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WATER AIR AND SOIL POLLUTION 2020年第4期231卷 1-12页

作者： Xie, Xianjun Yang, Sen Liu, Hongxing Pi, Kunfu Wang, Yanxin China Univ Geosci State Key Lab Biogeol & Environm Geol Wuhan 430074 Peoples R China China Univ Geosci Sch Environm Studies Wuhan 430074 Peoples R China Univ Waterloo Dept Earth & Environm Sci Ecohydrol Res Grp Waterloo ON N2L 3G1 Canada

To determine the leaching behavior and mechanism of cadmium (Cd) from contaminated soil by nitrilotriacetic acid (NTA), batch leaching experiments and chemical extraction in combination with Fourier transform infrared spectroscopy (FTIR) analyses were conducted to investigate the changes of Cd species, the organic matter content, the functional groups of soil particles in the course of leaching. The batch experiment was conducted using one cadmium level (12.49 mg/kg), different levels of NTA (1, 5, 10, 100, 150, 200, 300, 400, and 500 mmol/L), different pH levels (6-11), and different contact times (10, 30, 60, 120, 240, 480, 1440 min). Moreover, based on the experiment results, factors like the input NTA concentration, the eluent pH values, as well as the leaching time for the amount of leached Cd were evaluated via a multiple linear regression simulation. The results showed that the leached Cd from contaminated soils increased with increasing input NTA concentration and absorbed NTA in soils. At the beginning of the leaching experiment, the functional groups of the soil particles changed from hydroxyl to ester, resulting in the release of Cd from soil particles. At the same time, the esterification reaction caused an increase of eluent pH. However, after Cd released into the liquid phase, the complexation reaction between NTA and Cd continually consumed NTA, and this resulted in a minor decrease in eluent pH. As the result, some amounts of Cd could be precipitated as Cd(OH)(2) and immobilized again under high pH condition. multiple linear regression results showed that the input NTA concentration is the main factor that influences the removal efficiency of Cd from contaminated soils. The study results provide vital support for the practice of Cd-contaminated soil remediation using NTA as chemical eluent.

关键词： Cd-contaminated soil Chemical leaching Remediation multiple linear regression model

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Empirical study on the influencing factors of housing price –––Based on cross-section data of 31 provinces and cities in China

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Procedia Computer Science 2022年 199卷 1498-1504页

作者： Yuxi Jiang Lilan Qiu School of Public Administration Southwest Minzu University Chengdu 610041 China Business School of Southwest Minzu University Chengdu 610041 China

Using Eviews software, this paper makes an empirical analysis on the two kinds of factors that affect the housing price of 31 key cities in China: internal direct factors and external indirect factors, in order to reveal the reasons for the high housing price. A multivariate linear regression model was established in which the explanatory variables were land purchase price and residents’ disposable income, and the explanatory variables were the average selling price of residential commercial housing. The research shows that the land purchase price has a greater positive impact on the average selling price of residential commercial housing, compared with residents’ disposable income although also a positive impact but less impact.

关键词： housing price Land purchase unit price Household disposable income multiple linear regression model

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Carbonate Parameter Estimation and Its Application in Revealing Temporal and Spatial Variation in the South and Mid-Atlantic Bight, USA

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JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 2022年第7期127卷 e2022JC018811-e2022JC018811页

作者： Li, Xinyu Xu, Yuan-Yuan Kirchman, David L. Cai, Wei-Jun Univ Delaware Sch Marine Sci & Policy Newark DE 19716 USA Univ Miami Rosenstiel Sch Marine & Atmospher Sci Cooperat Inst Marine & Atmosphere Studies 4600 Rickenbacker Causeway Miami FL 33149 USA

To overcome the limitations due to sporadic carbonate parameter data, this study developed and evaluated empirical multiple linear regression (MLR) models for dissolved inorganic carbon (DIC), pH in total scale (pH(T)), and aragonite carbonate saturation state (ohm(Ar)) using hydrographic data (temperature, salinity, and oxygen) measured during 2007-2018 in the South Atlantic Bight (SAB) and Mid-Atlantic Bight (MAB) along the U.S. East Coast. We first reviewed the assumptions and routines of MLR models and then generated MLR models for each cruise for all three carbonate parameters in each region and assessed model performance. models derived from measured spectrophotometric pH have smaller uncertainties than pH(T) models based on pH calculated from total alkalinity (TA) and DIC. The regional differences of carbonate parameters between MAB and SAB are reflected in the coefficients of the empirical models. The MLR model temporal consistency indicates that the effect of the atmospheric CO2 increase on seawater carbonate parameters cannot be unequivocally resolved for the period of this study in the regions. Therefore, we combined different cruises to build composite models for each region. The composite models can capture the key features in the SAB and MAB. To further assess the model applicability, we applied our models to Biogeochemical-Argo data to reconstruct carbonate parameters. The algorithm in this study helps to reconstruct seawater carbonate chemistry using proxy data of high spatial and temporal resolution, which will enhance our understanding of physical and biological processes on carbon cycle and the long-term anthropogenic carbon inputs in coastal oceans.

关键词： mid-Atlantic bight South Atlantic bight multiple linear regression model carbonate parameters coastal carbon system

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Impact of soil and water conservation measures and precipitation on streamflow in the middle and lower reaches of the Hulu River Basin, China

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CATENA 2020年 195卷 104792-104792页

作者： Han, Hao Hou, Jingming Huang, Miansong Li, Zhanbin Xu, Keyan Zhang, Dawei Bai, Ganggang Wang, Chen Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Shaanxi Peoples R China Beijing Capital Co Ltd Beijing 100028 Peoples R China YRCC Hydrol Bur Zhengzhou 450004 Henan Peoples R China Minist Water Resources China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct Beijing 100038 Peoples R China James Hutton Inst Aberdeen AB15 8QH Scotland

Precipitation variation and soil and water conservation (SWC) measures mostly determine the dynamics of streamflow in the Hulu River Basin. In particular, SWC measures play an essential role in controlling streamflow reduction and soil loss in the Hulu River Basin. The objective of this study is to quantitatively explore the effect of SWC measures and precipitation on streamflow changes in the middle and lower reaches of the Hulu River Basin. In this study, a long-term measured annual streamflow data from two major hydrological stations was used to analyze variations in streamflow in this basin. The Mann-Kendall trend test and the change-point analysis method were applied to analyze the trend of streamflow datasets. The results indicated that the annual streamflow in the middle and lower basin of the Hulu River (1975-2016) had a similar decreasing trend to streamflow change of the entire basin (1960-2016), with the abrupt change point of annual streamflow occurred in the year 1986. In addition, the streamflow exhibited a slightly increasing trend due to the more precipitation increase in the basin throughout 1999-2016. From 1999 to 2016, the impact of precipitation and SWC measures on streamflow is quantitatively assessed by using a multiple linear regression model. The result showed that SWC measures could effectively delay and reduce streamflow. The analysis of the regression coefficients suggested that SWC measures had a positive ecological effect on streamflow decrease, whereas precipitation had a significantly positive effect on streamflow increase. The precipitation had a higher contribution to streamflow changes than SWC measures. For the change of annual streamflow, precipitation contributed 79.6%, the Grain-for-Green program and check dams contributed 13.4% and 7%, respectively. A comparison of the impact of SWC measures on streamflow showed that the Grain-for-Green program measure had a more significant impact on streamflow reduction in the middle and lowe

关键词： Soil and water conservation measures Precipitation Annual streamflow Trend change multiple linear regression model Hulu River Basin

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A novel approach for prediction of mass yield and higher calorific value of hydrothermal carbonization by a robust multilinear model and regression trees

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JOURNAL OF THE ENERGY INSTITUTE 2020年第4期93卷 1755-1762页

作者： Vallejo, Fidel Diaz-Robles, Luis A. Vega, Ricardo Cubillos, Francisco Univ Santiago Chile Dept Ingn Quim Santiago Chile Programa Ctr Valorizac Residuos & Econ Circular Santiago Chile

This study shows a mathematical and statistical analysis to generate models based on multiple linear regression (MLR) and regression trees (RT) that allow a reliable prediction of the Mass Yield (MY) and the Higher Heating Value (HHV) of the final solid product obtained by Hydrothermal Carbonization, called hydrochar. MLR models were obtained for lignocellulosic and non-lignocellulosic biomass using a set of experimental data with more than 500 points collected from the literature. A new approach based on dimensionless groups of variables that describe the composition of biomass and operational conditions was used. The analysis for each equation indicated that the MY depends on the process conditions and the biomass composition, which is proportional to the Polarity Index (IP) and Reactive Index (IR) values. On the other hand, the severity factor (log Ro) and the initial calorific value (HHVo) were the main factors for the HHV, but also the raw biomass composition (IP and H/C ratio) had an opposite and equal significant effect. For these equations, the results indicated an adjusted R-2(R-a(2)) of about 0.90 and an average RMSE of 6% and 1.7 MJ/kg for MY and HHV, respectively. Besides, explanatory variables were analyzed by their Relative Importance for the RT models. The severity factor (65%) and the IR (18%) were the most decisive variable in the MY prediction. The R-2 and RMSE were 0.73 and 2%, respectively. For HHV, the variables with the most significant impact were the HHVo (33%), the log Ro (24%), and the IP (22%). In this case, the R-2 and RMSE were 0.87 and 0.68 MJ/kg, respectively. Therefore, the model equations obtained are a powerful tool to predict the mass yield and the energetic value of the hydrochar before developing an experimental study. (C) 2020 Energy Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrothermal carbonization multiple linear regression model regression tree

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Robust Optimal Design When Missing Data Happen at Random

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2023年第3期17卷 43-43页

作者： Hu, Rui Bica, Ion Zhai, Zhichun MacEwan Univ Dept Math & Stat Edmonton AB T5J 2P2 Canada

In this article, we investigate the robust optimal design problem for the prediction of response when the fitted regression models are only approximately specified, and observations might be missing completely at random. The intuitive idea is as follows: We assume that data are missing at random, and the complete case analysis is applied. To account for the occurrence of missing data, the design criterion we choose is the mean, for the missing indicator, of the averaged (over the design space) mean squared errors of the predictions. To describe the uncertainty in the specification of the real underlying model, we impose a neighborhood structure on the deterministic part of the regression response and maximize, analytically, the Mean of the averaged Mean squared Prediction Errors (MMPE), over the entire neighborhood. The maximized MMPE is the "worst" loss in the neighborhood of the fitted regression model. Minimizing the maximum MMPE over the class of designs, we obtain robust "minimax" designs. The robust designs constructed afford protection from increases in prediction errors resulting from model misspecifications.

关键词： Optimal designs Minimax Missing completely at random Missing observations model robustness multiple linear regression model Nonlinear regression model

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Assessment of ground-level ozone pollution with monitoring and modelling approaches in Makkah, Saudi Arabia

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ARABIAN JOURNAL OF GEOSCIENCES 2020年第21期13卷 1164-1164页

作者： Habeebullah, Turki M. A. Umm Al Qura Univ Custodian Two Holy Mosques Inst Hajj & Umrah Res Mecca Saudi Arabia

Long-term exposure to the high levels of ground-level ozone (O-3) adversely affects human health, vegetation, and building materials. Atmospheric concentrations of O-3 are dependent on the levels of its precursors and meteorological parameters. In this paper, the data of several air pollutants as nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and sulfur dioxide (SO2) and meteorological parameters such as temperature, relative humidity, wind speed and direction, and atmospheric pressure are used to model O-3 at five (5) air quality monitoring stations in the Holy City of Makkah, the Kingdom of Saudi Arabia, for the year 2019. The performance of the multiple linear regression model (MLRM) and the generalized additive model (GAM) was compared for modelling the concentrations of O-3. It was found that NO, NO2, and relative humidity had a negative association, whereas other covariates had a positive association with O-3. To quantify the association between modelled and observed O-3 concentrations, several metrics including correlation coefficients and root mean square error (RMSE) were calculated. GAM outperformed MLRM at all five sites and was found to successfully capture nonlinearity in the association between O-3 and predictor variables. Correlation coefficients for the cross-validated model using hold-out testing dataset for GAM were 0.82, 0.78, 0.86, 0.79, and 0.76 and for MLRM were 0.69, 0.61, 0.79, 0.64, and 0.63 for Haram, Aziziah, Umrah, Atebiah, and Shawqiah sites, respectively. To conclude, GAM being an advanced nonlinear model performed better than MLRM and therefore is preferred for modelling O-3 in Makkah and other similar arid regions.

关键词： Generalized additive model multiple linear regression model Ground-level ozone Makkah Air pollution

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