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Prediction of nitrate concentrations using multiple linear regression and radial basis function neural network in the Cheliff River basin, Algeria

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JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH 2024年第1期12卷 77-89页

作者： Mehdaoui, Ibrahim Boudibi, Samir Latif, Sarmad Dashti Sakaa, Bachir Chaffai, Hicham Hani, Azzedine Badji Mokhtar Univ Fac Earth Sci Lab Water Resources & Sustainable Dev Annaba Algeria Campus Mohamed Khider Univ Sci & Tech Res Ctr Arid Reg CRSTRA Biskra Algeria Komar Univ Sci & Technol Coll Engn Civil Engn Dept Sulaimany Iraq

In this paper, multiple linear regression (MLR) and radial basis function neural network (RBF-NN) are applied to predict nitrate (NO3-) concentration with and without reservoir volume (WV) as predictor using monthly data for ten years in three water reservoirs located in the upper Cheliff basin (NW of Algeria). The datasets were divided into training (80%) and testing (20%) sets and two different scenarios were compared. The results revealed that RBF-NN was more efficient (MAE = 0.192 and SI = 0.061) compared with the MLR model to predict NO3- in all reservoirs. RBF-NN provided the best accuracy in the testing period with a high R-2 of 0.957 in reservoir II, and low MSE and PBias of 0.048 mg/l and 2.98% in the training period in reservoir III, respectively. Overall, the best results were generated by M(iii) in scenario B.

关键词： Nitrate concentration radial basis function neural network model multiple linear regression model water reservoir Upper-Cheliff River basin

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regression models in Lubricant Oil Quality Assessment 1

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24th International Conference on Computational Science and Its Applications (ICCSA)

作者： Aleixo, Sandra M. Enguica, Ricardo Sedas, Francisco Ferreira, Paulo Silva, Andre ISEL Inst Super Engn Lisboa IPL Inst Politecn Lisboa Dept Math Rua Conselheiro EmIdio Navarro 1 P-1959007 Lisbon Portugal Univ Lisbon Fac Ciencias CEAUL Ctr Estat & Aplicac Bloco C6Piso 4 P-1749016 Lisbon Portugal Inst Super Tecn CEMAT Ciencias Ctr Matemat Computac & Estocast Ave Rovisco Pais P-1049001 Lisbon Portugal Galp Qual & Tech Prod Care Ave India 8 P-1349065 Lisbon Portugal

ISBN: (数字)9783031651540

ISBN: (纸本)9783031651533;9783031651540

After a suitable data analysis, we adjust two types of regression models to support decision-making when monitoring lubricants in an industrial environment. The target variable is the remaining useful life of a lubricant, as it is an essential process in preventing damage to associated equipment. The main objective of this work is to predict at an early stage how long will an oil load still perform correctly. The first model is a multiple linear regression, adjusting coefficients using cross-validation on several subgroups of the data set. Though the performance of this model is satisfactory, the corresponding adjustment is compromised by the fact that the observations are not independent, as they are grouped taking into account the oil loads to which they belonged. For this reason, a second type of regression was adjusted: a linear regression model with mixed effects. After adjusting the models, a Python algorithm was created to determine an optimal interval regarding the oil service hours after which the lubricant should be replaced.

关键词： Predictive maintenance Lubricants Predictor selection Correlation multiple linear regression model Mixed effects linear regression Stepwise selection methods Optimal range

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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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Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: multiple linear regression and Non-linear regression models

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ENERGIES 2022年第19期15卷 7407页

作者： Tangwe, Stephen Mukumba, Patrick Makaka, Golden Univ Ft Hare Dept Phys Renewable Energy Res Grp Private Bag X1314 ZA-5700 Alice South Africa

Biogas technology is rapidly gaining market penetration, and the type of digesters employed in the harnessing of the biogas from biodegradable waste is crucial in enhancing the total viable bacteria counts. This study focused on the exploration of input parameter (number of days, daily slurry temperature, and pH) and target (total viable bacteria counts) datasets from anaerobic balloon digester charged with cow manure using data acquisition system and standard methods. The predictors were ranked according to their weights of importance to the desired targets using the reliefF test. The complete dataset was randomly partitioned into testing and validated samples at a ratio of 60% and 40%, respectively. The developed non-linear regression model applied on the testing samples was capable of predicting the yield of the total viable bacteria counts with better accuracy as the determination coefficient, mean absolute error, and p-value were 0.959, 0.180, and 0.602, respectively, as opposed to the prediction with the multiple linear regression model that yielded 0.920, 0.206, and 0.514, respectively. The 2D multi-contour surface plots derived from the developed models were used to simulate the variation in the desired targets to each predictor while the others were held constant.

关键词： balloon digester multiple linear regression model non-linear regression model 2D multi-contour surface plots predictors total viable bacteria counts

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Dew amount and its long-term variation in the Kunes River Valley,Northwest China

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Journal of Arid Land 2022年第7期14卷 753-770页

作者： FENG Ting HUANG Farong ZHU Shuzhen BU Lingjie QI Zhiming LI Lanhai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Tianshan Station for Snowcover and Avalanche Research Chinese Academy of SciencesXinyuan 835800China University of Chinese Academy of Sciences Beijing 100049China Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone Urumqi 830011China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China Department of Bioresource Engineering McGill UniversityMontreal H3A0G4Canada

Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid *** estimating the dew amount and quantifying its long-term variation are *** this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),*** key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) *** then significantly decreased for about a decade,after it increased slightly from 2013 to *** the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and *** in the dew amount in the Kunes River Valley was mainly driven by relative *** study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.

关键词： dew amount long-term variation meteorological variables random forest model multiple linear regression model Kunes River Valley

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Effects of Regional Thermodynamic Parameters on Lightning Flash Density as an Indicator of Convective Activity Over Southwest Iran

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PURE AND APPLIED GEOPHYSICS 2022年第5期179卷 2011-2025页

作者： Rafati, Somayeh Fattahi, Ebrahim Sayyed Jamaleddin Asadabadi Univ Dept Geog Asadabad Iran Atmospher Sci & Meteorol Res Ctr ASMERC Tehran *** Iran

This study investigated the correlation of thermodynamic parameters with the regional averaged lightning flash density in southwest Iran based on OTD/LIS observations and reanalysis datasets from 1996 to 2014. In addition, the most important parameters and their domains were identified for explaining the lightning density variance using a multiple linear regression model. The results showed that surface sensible heat flux over Syria and Jordan, CAPE over southwest Iran, sea surface temperature over the Arabian Sea, and latent heat flux over Northeastern Europe are the best parameters to predict the lightning density in winter. While in spring, CAPE over southwest Iran, the surface sensible heat flux over northeast Iran and the Arabian Sea, and air surface temperature over the Saudi Arabian Peninsula are the main factors for predicting lightning density. The regression models explain almost 90% and 93% of the lightning variability in winter and spring, respectively. The local effect of CAPE and low-level wind shear on lightning activity is more important in spring than in winter, indicating that the contribution of local lightning-generating convective activity is higher in spring. The correlation coefficient between humidity indices and lightning density showed that western moisture sources have a more important role in providing moisture to convection and lightning in winter than in spring. It is worth noting that the wind speed of jet streams at 300 hPa is positively correlated with lightning activity in winter, although their contribution to the multiple linear regression model is negligible.

关键词： Lightning activity CAPE heat flux surface temperature multiple linear regression model

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Value-added Benefits of Green Space: A Systematic Analysis of the Relationship between Urban Green Space and Commercial House Prices in Beijing Based on Remote Sensing Technology

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SENSORS AND MATERIALS 2022年第12期34卷 4247-4262页

作者： Zhang, Yutong Wang, Simin Ma, Yiwen Peng, Cheng Qu, Manping Xu, Ao Xing, Xiaojuan Yin, Weida Beijing Forestry Univ Sch Landscape Architecture Beijing 100083 Peoples R China Beijing Inst Surveying & Mapping Beijing Key Lab Urban Spatial Informat Engn Beijing 100038 Peoples R China

Urban green space is an important consideration when people purchase housing, and remote sensing technology makes it possible to acquire and analyze urban data on a large scale. The purpose of this article is, using remote sensing data on the Web, to quantify the relationship between urban green space and urban commercial house prices to reflect the increased importance that contemporary urban residents attach to urban green space and the potential economic benefits to real estate that green space enhancement may bring. We establish a model of the impact of urban green space on house prices for three districts in Beijing within the Fifth Ring Road using the hedonic price method, GIS spatial operation, and regression analysis. Then through questionnaire clustering, we summarize the preferences of people of different regions and social attributes in terms of green space accessibility, visibility, and availability indicators. We find that, holding other variables constant, the price of each residence decreases by 0.403 million yuan/m² for each 1 km increase in the distance from the house to the nearest park. Holding other variables constant, each unit increase in the rating of the nearest park increases the price of each residence by 0.144 million yuan/m². Respondents attach more importance to urban green space accessibility than to visibility and availability. Combining these two findings, we propose that the design of green space should take accessibility as the main consideration, that the construction of green space should enhance the coordination between green space and residences, and that the layout of green space should also be differentiated to improve the science of green space system planning. Through this study, we can explore ways to improve the social benefits of public space such as urban green space and help carry out more scientific and reasonable planning of urban green space, so as to promote the sustainable development of environmental and ecologica

关键词： urban green space hedonic price model house price multiple linear regression model Beijing City

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Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula

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ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES 2022年第4期58卷 549-561页

作者： Jeong, Dajeong Yoo, Changhyun Yeh, Sang-Wook Yoon, Jin-Ho Lee, Daegyun Lee, Jae-Bum Choi, Jin-Young Ewha Womans Univ Dept Climate & Energy Syst Engn 52 Ewhayeodae Gil Seoul South Korea Hanyang Univ ERICA Dept Marine Sci & Convergent Technol Ansan South Korea Gwangju Inst Sci & Technol Sch Earth Sci & Environm Engn Gwangju South Korea Natl Inst Environm Res Air Qual Forecasting Ctr Incheon South Korea

Concentrations of fine particulate matter smaller than 2.5 mu m in diameter (PM2.5) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM2.5 concentrations at 1-3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government's observations, constructing a long-term dataset covering the 2005-2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM2.5 concentrations. For the wintertime (December-January-February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March-April-May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM2.5 concentration and an extreme metric, i.e., seasonal number of high PM2.5 concentration days.

关键词： Seasonal prediction PM2.5 concentrations multiple linear regression model

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An improved method of fluorescein diacetate determination for assessing the effects of pollutants on microbial activity in urban river sediments

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JOURNAL OF SOILS AND SEDIMENTS 2022年第10期22卷 2792-2801页

作者： Zhang, Chao Li, Meng Zhang, Shiwei Qi, Zixuan Huang, Jianjun Sun, Jingmei Tianjin Univ Sch Environm Sci & Engn Tianjin 300350 Peoples R China North China Municipal Engn Design & Res Inst Co L Tianjin 300074 Peoples R China

Purpose The fluorescein diacetate (FDA) method has been widely used to quantify microbial activity rapidly and sensitively. However, due to high silt-clay ratios and high organic matter contents in urban river sediments, the current FDA methods for soil/coastal sediments may produce errors when applied to urban river sediments. Here, the FDA method was optimized to assess the effects of pollutants on microbial activity. Materials and methods Based on their microbial activities, five urban river sediments with different contamination levels were selected for method optimization. The effects of pollutants on microbial activity were assessed using multiple linear regression analysis based on the optimized method. Results Reducing the method's variation coefficient from 1.9%similar to 3.8% to 0.7%similar to 3.3% by increasing the amount of terminator was critical for improving the method's precision and reproducibility. The fluorescein adsorbed to the sediment was released by ultrasonication in an ice-water bath for 20 min, improving measured microbial activity values. Using the optimized method, we found significant positive correlations between microbial activity and physicochemical indicators. According to multiple linear regression analysis, the influence of different variables on microbial activity diminished in the following order: organic matter > total nitrogen > acidified volatile sulfides > moisture content. Conclusions The optimized method reduced the coefficient of variation and improved the measured microbial activity values. Based on the optimized method, multiple linear regression analysis will be helpful for the prediction of pollutant concentrations in urban river sediments.

关键词： Urban river sediments Microbial activity Correlation analysis multiple linear regression model model validation

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