Multivariate Linear Regression Method Research Articles

Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors.

This paper aims to monitor the ambient level of particulate matter less than 2.5 m (PM) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 g/m with a correlation coefficient of 0.6281, by referring to the ground truth of PM time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 g/m with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM estimates is 15.6391 g/m with the correlation coefficient of 0.8701.

Exploring multivariate linear regression methods for the prediction of total phenolic content in standard American lager beers using synchronous fluorescence spectroscopy fused data

This work aims to explore chemometric methods for determining the total phenolic content in Brazilian standard American lager beers using synchronous fluorescence spectroscopy fused data. Two hundred and sixteen (216) beer samples were diluted at a ratio of 1:5 (v/v), sonicated for 15 ​min in a water bath, and buffered using an acetic acid/acetate buffer of pH 4.5. The synchronous fluorescence spectra were obtained from 200 to 600 ​nm with a resolution of 1 ​nm. However, different offsets were tested for synchronous fluorescence (Δλ ​= ​20, 50, and 80). All measurements were carried out in triplicate, and the Folin-Ciocalteu method was used as the reference method for the total phenolic content. All chemometric methods were explored using R software and its graphic interface, RStudio. First, the spectral data with different offsets were normalized and fused. After this, the fused data were smoothed with the Savitzky-Golay algorithm with a window of length 11 and a quadratic polynomial, followed by data mean centering. Robust principal component analysis (ROBPCA) was used to identify anomalous data. The Kennard-Stone algorithm was used to split the dataset into 75% for the training set and 25% for the test set. The prediction models used were partial least squares (PLS), orthogonal partial least squares (O-PLS), and elastic net regressions with cross-validation (CV). Ensemble PLS and ensemble sparse PLS with bootstrap resampling were also used. Other PLS regression methods based on the selection of variables were tested, such as significance multivariate regression (sMC – PLS), uninformative variable elimination (UVE – PLS), and interval PLS (iPLS) global and optimized models with cross-validation. All the regression models were built with the training set. The validation was carried out with the application of the regression models to the test set. Within this context, two multivariate regression were highlighted, the CV elastic net and iPLS optimized models, with the root mean square error of prediction (RMSEP) equaling 1.0 and 1.1 ​mg GAE L−1, respectively.

Impact of Service Quality, Customer Satisfaction and Switching Costs on Customer Loyalty

This study examines the relationships and the impacts of service quality, customer satisfaction and switching costs on customer loyalty of e-banking in commercial banks. This study uses questionnaires with 7-point Likert scale to collect data from 227 e-banking users, who are mainly students and paid employees in Hanoi City, Vietnam. The collected data are analyzed by using multivariate linear regression method. The results show that all five factors of service quality in e-banking – reliability, responsiveness, service capacity, empathy and tangibility – have positive correlations with customer satisfaction. Among these determinants, service capacity and tangibility have the strongest impact. It means that, by increasing the quality of their services through five above factors, especially through improving capacity and tangibility, commercial banks can satisfy their customer better. In addition, customer satisfaction is strongly and positively correlated to customer loyalty. It means that, if customers are satisfied with e-banking services of one bank, they not only continue to do more businesses with this bank, but also recommend this bank to other people. Lastly, customer loyalty also has a strong and positive correlation with switching costs, which means customers tend to be loyal to one bank if the barriers to switching banks increase.

Robust and Rapid Detection of Mixed Volatile Organic Compounds in Flow Through Air by a Low Cost Electronic Nose

This work aims to detect volatile organic compounds (VOC), i.e., acetone, ethanol and isopropyl alcohol (IPA) and their binary and ternary mixtures in a simulated indoor ventilation system. Four metal-oxide-semiconductor (MOS) gas sensors were chosen to form an electronic nose and it was used in a flow-through system. To speed up the detection process, transient signals were used to extracted features, as opposed to commonly used steady-state signals, which would require long time stabilization of testing parameters. Five parameters were extracted including three in phase space and two in time space. Classifier and regression models based on backpropagation neural network (BPNN) were used for the qualitative and quantitative detection of VOC mixtures. The VOCs were mixed at different ratios; ethanol and isopropyl alcohol had similar physical and chemical properties, both being challenging in terms of obtaining quantitative results. To estimate the amounts of VOC in the mixtures, the Levenberg–Marquardt algorithm was chosen in network training. When compared with the multivariate linear regression method, the BPNN-based model offered better performance on differentiating ethanol and IPA. The test accuracy of the classification was 82.6%. The concept used in this work could be readily translated for detecting closely related chemicals.

Application of a physics-based lumped parameter model to evaluate reservoir parameters during CO2 storage

Evaluating reservoir performance could be challenging, especially when available data are only limited to pressures and rates from oil field production and/or injection wells. Numerical simulation is a typical approach to estimate reservoir properties using the history match process by reconciling field observations and model predictions. Performing numerical simulations can be computationally expensive by considering a large number of grids required to capture the spatial variation in geological properties, detailed structural complexity of the reservoir, and numerical time steps to cover different periods of oil recovery. In this work, a simplified physics-based model is used to estimate specific reservoir parameters during CO2 storage into a depleted oil reservoir. The governing equation is based on the integrated capacitance resistance model algorithm. A multivariate linear regression method is used for estimating reservoir parameters (injectivity index and compressibility). Synthetic scenarios were generated using a multiphase flow numerical simulator. Then, the results of the simplified physics-based model in terms of the estimated fluid compressibility were compared against the simulation results. CO2 injection data including bottom hole pressure and injection rate were also gathered from a depleted oil reef in Michigan Basin. A field application of the simplified physics-based model was presented to estimate above-mentioned parameters for the case of CO2 storage in a depleted oil reservoir in Michigan Basin. The results of this work show that this simple lumped parameter model can be used for a quick estimation of the specific reservoir parameters and its changes over the CO2 injection period.

Multivariate recursive Bayesian linear regression and its applications to output-only identification of time-varying mechanical systems

This article focuses on the output-only recursive identification of time-varying systems by using parametric time-domain methods. A novel multivariate recursive Bayesian linear regression method is proposed based on the vector time-dependent autoregressive moving average model. The standard setup of univariate batch Bayesian linear regression is first extended to the multivariate case for multiple response signal modeling and further extended to the recursive case to meet the output-only recursive identification requirement of practical systems. A sliding window mechanism is finally applied to deemphasize data from the remote past and fix the computational complexity for each consecutive update, allowing the proposed method to be capable of tracking the time-varying dynamics online. The proposed multivariate recursive Bayesian linear regression method is first validated by a simple numerical system and subsequently applied to identify two mechanical systems with typical time-varying dynamics. Comparative identification results via Monte Carlo tests numerically and experimentally demonstrate the superior achievable accuracy and time-varying tracking capability of the proposed method.

A rigorous model for prediction of viscosity of oil-based hybrid nanofluids

Oil-based hybrid nanofluids play an important role in heat transfer in cooling systems and lubrication. Therefore, various experimental investigations are conducted to estimate their viscosity. However, such measurements can be carried out on limited types of oil-based hybrid nanofluids and often are time consuming and expensive. The main objective of this paper is to develop a rigorous data-driven method based on an advanced genetic programming (GP) called multigene genetic programming (MGGP) to predict the viscosity of Newtonian oil-based hybrid nanofluids which has not previously been used in this area. A comparative analysis was performed using the gene expression programming (GEP), multi-variate linear regression (MLR) methods and various correlations. 679 experimental data points with different nanoparticles and oil-based fluids were collected from literature to develop the Artificial Intelligent (AI) models. The new approach showed superior performance in estimating of the relative viscosity of oil-based hybrid nanofluids in comparison with all correlations methods. Furthermore, the MGGP results for the test dataset (R=0.991, RMSE=0.05, PI=0.643) were more accurate than those obtained from the GEP (R=0.975, RMSE=0.083, PI=0.696) and MLR (R=0.912, RMSE =0.153, PI=1), respectively. The sensitivity analysis was also performed demonstrating that the volume fraction (PIs=0.849, DV1=10.079%), temperature (PIs=0.463, DV2=9.966%) and nanoparticles size (PIs=0.420, DV3=6.092%) are the most significant factors in assessing relative viscosity, respectively.

A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network

The short-term load forecasting is crucial in the power system operation and control. However, due to its nonstationary and complicated random features, an accurate forecast of the load behavior is challenging. An improved short-term load forecasting method is proposed in this article. At first, the load is decomposed into different frequency components varying from the low to high levels realized by the ensemble empirical-mode decomposition algorithm. Then, the smooth and periodic low-frequency components are predicted by the multivariable linear regression method while maintaining the efficient computation capacity, while the high-frequency components with strong randomness are forecasted by the long short-term memory neural network algorithms. Thus, the actual load behavior is obtained by combining these two methods. Finally, the proposed method is validated by experiments, in which the tested data from the west area of China, Uzbekistan, and PJM Interconnection (USA) are used. The prediction of the load behavior is accurate globally along with the local details, as presented in the experiments, which verify the effectiveness of the proposed method.

Investigation of Academic Staff’s Self-Efficacy Using the Educational Internet

The Covid-19 pandemic has had negative effects throughout the whole world, including education systems. To overcome this negativity, all educational institutions have turned to internet-based education. However, the educator’s self-efficacy is of more importance in this system. This study aimed to reveal the connection between the University academic staff’s genders, ages, titles (doctorate/non-doctorate), and work year characteristics and their self-efficacy beliefs about their educational Internet usage. The sample consists of 100 [51% (n = 51) female and 49% (n = 49) male] academic staff, who were selected according to convenience sampling in the Faculty of Education and Faculty of Sport Sciences at Uludag University. In this study, the “Educational Internet Usage Self-Efficacy Beliefs” scale, developed by Şahin (2009), was used to collect data. Descriptive statistics refer to number and percentage for qualitative variables; quantitative variables are summarized using mean, median, standard deviation, and minimum and maximum statistics. Univariate analyses used binary group comparisons with the Student’s t-test and relationships between numerical variables and Spearman correlation coefficients. Multiple regression analysis was used, in conjunction with the backward method, for the multivariate linear regression method. Analysis results alpha (Type I error) value was evaluated at the level of 0.05 significance. The mean level of self-efficacy belief of academic staff is 109,42. Since the highest score that can be obtained from the scale is 140, the relationships between scale score and age and duration of service variables are significant according to the univariate analysis, while differences in gender and PhD and non-PhD groups are not significant. When multiple linear regression analysis is applied with the backward stepwise method, age and academic title variables are significant in the model. Additionally, the mean scale scores of PhD academicians are higher than others.

Prioritizing the perceived equity of the residents to construct an equitable health care system: evidence from a national cross-sectional study in China

BackgroundBuilding an equitable health care system involves both the promotion of social justice in health and people’s subjective perception of the promotion. This study aimed to analyze the overall status and associated factors of the perceived equity of the Chinese health care system, and then to offer policy recommendations for health care reform.MethodsInformation on the perceived equity score (scale 0 to 10) of 10,243 valid cases in total were derived from the data set of Chinese Social Survey 2015. Univariate analysis methods were applied to present respondents’ overall perceived equity of the Chinese health care system. Multivariate linear regression method was used to explore the associated factors of the perceived equity and examine their independent effect.ResultsThe respondents gave positive but relatively low marks (6.7 ± 2.6, 95% CI: = 6.64~6.74) of the equity of the Chinese health care system. Younger respondents reported a higher score of perceived equity than their elder counterparts (β = − 0.132, 95% CI: − 0.203~ − 0.062, P < 0.001). Respondents with lower education level were significantly more likely to consider the Chinese health care system equitable (β = − 0.104, 95% CI: − 0.153~ − 0.056, P < 0.001). Respondents satisfied with the Social Health Insurance reimbursement ratio tended to score the system higher in the survey (β = 0.044, 95% CI: 0.024~0.063, P < 0.001). Respondents residing in eastern China and rural areas were significantly more likely to consider the Chinese health care system equitable (β = − 0.268, 95% CI: − 0.338~ − 0.199, P < 0.001). Meanwhile, rural respondents reported higher scores of the perceived equity than urban respondents did (β = 0.348, 95% CI: 0.237~0.458, P < 0.001). Respondents from regions with adequate GPs scored the system higher in this survey (β = 0.087, 95% CI: 0.008~0.165, P < 0.001). The present study found no influence of gender, economic status, Social Health Insurance coverage, or satisfaction with the latest treatment on perceived equity.ConclusionsEliminating the sense of inequity among a range of populations should be prioritized in health care reform. A national-level investigation system to rate residents’ perceived equity was necessary for global health care reform.

Effect of free swelling index on improving estimation of the soil moisture retention curve by different methods

Several variables have been used to estimate the soil moisture retention curve (SMRC) so far, but one of the main points in using pedotransfer functions (PTFs) is the selection of readily available soil properties as inputs. In this study, 147 disturbed and undisturbed soil samples were taken from five provinces of Iran. Some soil properties such as particle size distribution (PSD), bulk density (BD), free swelling index (FSI) and SMRC were measured. A double exponential model was fitted to the SMRC data, and PTFs were developed to predict the model parameters, by multivariate linear regression and artificial neural network (ANN) methods. Using FSI with the soil basic properties (silt to sand ratio, clay and BD) as estimators improved the estimation of SMRC significantly for the full data set by ANN method (by 10.9 and 9.0% in the training and testing steps, respectively), and in the textural groups of “clay loam, silty clay loam”, “clay”, “loam”, “sandy loam” and “sand”, by the linear regression method in the range of 1.0 to 14.9%. Grouping the soil samples significantly improved the accuracy of the estimates, especially in the sandy soil textural group. In estimating SMRC using total data, ANN method had a greater accuracy compared with the linear regression method. In general, FSI significantly improved the accuracy of parametric estimation of the SMRC and grouping of data had a positive impact on improving estimates in some textural groups.

MOA-based linear and nonlinear QSAR models for predicting the toxicity of organic chemicals to Vibrio fischeri.

Risk assessment of pollutants to humans and ecosystems requires much toxicological data. However, experimental testing of compounds expends a large number of animals and is criticized for ethical reasons. The in silico method is playing an important role in filling the data gap. In this paper, the acute toxicity data of 1221 chemicals to Vibrio fischeri were collected. The global models obtained showed that there was a poor relationship between the toxicity data and the descriptors calculated based on linear and nonlinear regression analysis. This is due to the fact that the studied compounds contain not only non-reactive compounds but also reactive and specifically acting compounds with different modes of action (MOAs). MOAs are fundamental for the development of mechanistically based QSAR models and toxicity prediction. To investigate MOAs and develop MOA-based prediction models, the compounds were classified into baseline, less inert, reactive, and specifically acting compounds based on the modified Verhaar's classification scheme. Satisfactory models were established by multivariate linear regression (MLR) and support vector machine (SVM) analysis not only for baseline and less inert chemicals, but also for reactive and specifically acting compounds. Compared with linear models obtained by the MLR method, the nonlinear models obtained by the SVM method had better performance. The cross validation proved that all of the models were robust except for those for reactive chemicals with nN (number of nitrogen atoms) = 0 and n(C=O) (number of carbonyl groups) > 0 (Q2ext < 0.5). The application domains and outliers are discussed for those MOA-based models. The models developed in this paper are significantly helpful not only because the application domains for baseline and less inert compounds have been expended, but also the toxicity of reactive and specifically acting compounds can be successfully predicted. This work will promote understanding of toxic mechanisms and toxicity prediction for the chemicals with structural diversity, especially for reactive and specifically acting compounds.

Traffic Congestion and Duration Prediction Model Based on Regression Analysis and Survival Analysis

With the current situation of traffic congestion becoming more and more serious, how to accurately predict the time of traffic congestion has been widely concerned. In this article, we will build two models to better predict traffic congestion time. First, we use methods to collect the data we need, and through the preliminary cleaning, processing, deletion of missing data, combined calculation of data according to indicators and other steps to screen and integrate the data we need. Then, the multivariate linear regression method is used to construct the traffic prediction congestion model for the existing data, and the actual situation of traffic congestion is obtained. Secondly, the non-parametric method Kaplan-Meier model in the survival analysis method is used to obtain the survival function of traffic congestion duration, and the traffic congestion duration model is constructed. The software programming is solved by MATLAB, Stata, SPSS, etc., and the congestion prediction is obtained. The fitting degree between the predicted value and the actual value of the model is above 0.96, which can better quantify the conclusion that the road traffic operation congestion degree and congestion duration model can identify the characteristics of congestion distribution and duration. Finally, the paper evaluates the advantages and disadvantages of the model objectively, and considers the aspects that can be promoted and applied. I hope that this model can contribute to the prediction research of traffic congestion time!

Monitoring drought using composite drought indices based on remote sensing

Drought is one of the most frequent disasters occurring in North China and has a great influence on agriculture, ecology and economy. To monitor drought of typical dry areas in North China, Shandong Province, this paper proposed composite drought indices using multivariable linear regression (MCDIs) to integrate Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Global Land Data Assimilation System Version 2.1 (GLDAS-2.1) derived soil moisture, Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2017 (March to September). Pearson correlation analyses were performed between single remote sensing drought indices and in-situ drought indices, standardized precipitation evapotranspiration index (SPEI), in different time scales to assess the capability of single indices over Shandong Province. The multivariable linear regression method was used to established MCDIs, and mediator and moderator variables were introduced to optimize the model. The correlation coefficients (r) between MCDIs and SPEIs was higher than that between each single index and SPEIs. Additionally, when we investigate the correlations of different MCDIs with both standardized precipitation index (SPI) and moisture index (MI), the highest r values with both 1-month SPI and MI were acquired by the MCDI based on 1-month SPEI (MCDI-1). This suggested MCDI-1 was suitable to monitor meteorological drought. Also, the comparison between MCDI based on 9-month SPEI (MCDI-9) and soil moisture showed MCDI-9 was a good indicator for agricultural drought. Therefore, multivariable linear regression and MCDIs were recommended to be an effective method and indices for monitoring drought across Shandong Province and similar areas.

Rapid analysis of phosphate slurries and pressed pellets using laser-induced breakdown spectroscopy

This paper demonstrates the capabilities of laser-induced breakdown spectroscopy (LIBS) to perform quantitative multi-elemental analysis in phosphate slurries and pressed pellets. Fourteen phosphate powder samples were used in this study. 30 ml (12–13 g) of each phosphate powder sample were pelletized using an automated hydraulic press, and eleven slurries having a solid density of 30% (w/v) were prepared by mixing an appropriate quantity of powder and water. Samples were then analyzed by the Dual-LIBS instrument. A calibration model was generated for several elements contained in the samples, namely phosphorus, aluminum, iron, carbon, silicon, and magnesium, using a multivariate linear regression (MLR) method. The accuracy and reproducibility of the multivariate model were evaluated using independent validation samples sets. Correlation with R-squared of ~0.99 was found between the calculated and reference concentrations, which indicates that LIBS is a promising technique for the in situ rapid determination of the major elements present in solid and slurry phosphate samples.

Influence of the built environment of Nanjing\u2019s Urban Community on the leisure physical activity of the elderly: an empirical study

BackgroundUrbanization and aging are global phenomena that offer unique challenges in different countries. A supportive environment plays an important role in addressing the issues of health behavioral change and health promotion (e.g., prevent chronic illnesses, promote mental health) among older adults. With the development of the socio-ecological theoretical model, studies on the impact of supportive environments on physical activity have become popular in the public health field in the EU and US. Meanwhile, very few Chinese studies have examined the relationship between built environment features and older adults’ physical activity at the ecological level. The purpose of the study is to investigate how the factors part of the built environment of Nanjing’s communities also influence leisure time physical activity among the elderly.MethodsUsing a socio-ecological model as a theoretical framework, we conducted a cross-sectional study of 399 elderly people from 19 communities in Nanjing, China, using a one-on-one questionnaire to collect data, including participants’ perceived built environment and self-reported physical activity. A multivariate linear regression method was used to analyze the factors influencing their recreational physical activity.ResultsThis study found that compared to older people with low average monthly income, the recreational physical activity of the elderly with average monthly incomes between 1001 and 2000 ¥ (β = 23.31, p < 0.001) and 2001 ¥ or more (β = 21.15, p < 0.001) are significantly higher. After controlling for individual covariates, street connectivity (β = 7.34, p = 0.030) and street pavement slope (β = − 7.72, p = 0.020), we found that two out of ten built environment factors indicators influence their physical activity. The importance of each influencing factor ranked from highest to lowest are monthly average income, street pavement slope, and street connectivity. Other factors were not significantly related to recreational physical activity by the elderly.ConclusionsOlder adults with a high income were more likely to participate in recreational physical activity than those with a low income. In order to positively impact physical activity in older adults and ultimately improve health, policymakers and urban planners need to ensure that street connectivity and street pavement slope are factored into the design and development of the urban environment.

An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze

There are a few studies working on the vertical distribution of TSM, however, understanding the underwater profile of TSM is of great benefit to the study of biogeochemical processes in the water column that still require further research. In this study, three data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2016 and 2018. Based on the in situ optical and biological data, a multivariate linear stepwise regression method was applied for retrieval of the surface horizontal distribution of TSM (TSM0.2) using GOCI (Geostationary Ocean Color Imager) data. Then, the estimation model of vertical structure of underwater TSM was constructed using layer-by-layer recursion. This study drew several crucial findings: (1) the approach proposed in this paper generated very high goodness of fit results, with determination coefficients (R2) of 0.83 (p < 0.001, N = 54), and with smaller prediction errors (the mean absolute percentage error is determined to be 16.34%, the root mean square error is 9.01 mg l−1, and the mean ratio is 1.00, N = 26). (2) The monthly surface TSM and the column mass of suspended matter (CMSM) are affected by both wind speed and precipitation in HZL. In addition, the hourly variation of surface TSM and CMSM are driven by local wind, most especially in spring and winter. (3) Compared with the non-uniform hypothesis, the CMSM derived by conventional vertical uniformity hypothesis was underestimated by almost 10% in HZL during 2016. This should warrant the attention of lake managers and lake environmental evolution researchers.

Health literacy among Iranian adults: findings from a nationwide population-based survey in 2015.

Health literacy is one of the most important determinants of noncommunicable diseases prevention. Health literacy is associated with elevated risks for poorer access to care, adverse health outcomes, and increased hospitalization and health costs. This study aimed to determine the level of health literacy among the general adult population in the Islamic Republic of Iran. Using a cross-sectional study during 2014-2015 with a multistage cluster sampling approach, we administered a pilot-tested standardized questionnaire to assess different domains of health literacy (i.e., reading, comprehension, communication/decision-making and Interpretation/judgment skills, individual and social empowerment, health information access and health information use) among 8439 (3935 males) individuals aged 18-60 years. Data were collected through face-to-face interviews. Descriptive statistics and multivariable linear regression method using SPSS (20) were applied to identify the factors associated with health literacy among Iranian adults. The mean health literacy level was 10.2±3.8 (out of 20). Only 18% (95% confidence interval [CI]: 17.15-18.78) of the participants had adequate health literacy, while 45.7% (95% CI: 44.64-46.78) had inadequate, and the 36.3% (95% CI: 35.21-37.33) had moderate health literacy. In the adjusted linear regression model, education level (the smallest β = 4.35, P < 0.001), age (β = 0.01, P = 0.002), female sex (β = 0.45, P < 0.001), residency in rural areas (β = 0.26, P < 0.001) and having permanent job (β = 1.03, P < 0.001) were significantly associated with more health literacy. Our findings highlighted that the Iranian adult population has an insufficient level of health literacy, which calls for comprehensive education planning to improve the levels, with special attention to certain subpopulations (e.g. illiterate populations) and domains (e.g. individual empowerment).

Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests

Developing an accurate model for estimating the forest structural parameters of planted forests is crucial for forest productivity predictions and can provide a better understanding of the carbon cycle under climate change. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represents a promising active remote sensing technology that has the potential to be used for forest inventories. In addition, the process-based model, physiological principles predicting growth (3-PG), which is based on physiological principles and environmental factors, has been applied to estimate the growth of even-aged, mono-specific forests under the effect of different management levels, site conditions, and climate change. In this study, the performance of UAV-LiDAR metrics was assessed and applied to estimate forest structural parameters using a multivariate linear regression (MLR) method. The 3-PG was parameterized and used to simulate the diameter at breast height, stem density, volume and above-ground biomass of a planted ginkgo forest in eastern China. In addition, a sensitivity analysis was conducted on the 3-PG model's input parameters. The results demonstrated that both the MLR based on UAV-LiDAR data and a progress model of the 3-PG have a promising potential for estimating forest structural parameters ( R 2 > 0.70, relative root squared error >20%). A sensitivity analysis of the 3-PG parameters also confirmed that the parameter “age at canopy cover” (fullCanAge) is vital for the 3-PG model, and positively correlation with the simulated results. The method presented here represents an improvement on traditional methods for estimating forest structural parameters because it more explicitly accounts for climatic effects included in the 3-PG model.

Bias Correction of Tropical Cyclone Parameters in the ECMWF Ensemble Prediction System in Australia

Abstract Global ensemble prediction systems have considerable ability to predict tropical cyclone (TC) formation and subsequent evolution. However, because of their relatively coarse resolution, their predictions of intensity and structure are biased. The biases arise mainly from underestimated intensities and enlarged radii, in particular the radius of maximum winds. This paper describes a method to reduce this limitation by bias correcting TCs in the ECMWF Ensemble Prediction System (ECMWF-EPS) for a region northwest of Australia. A bias-corrected TC system will provide more accurate forecasts of TC-generated wind and waves to the oil and gas industry, which operates a large number of offshore facilities in the region. It will also enable improvements in response decisions for weather sensitive operations that affect downtime and safety risks. The bias-correction technique uses a multivariate linear regression method to bias correct storm intensity and structure. Special strategies are used to maintain ensemble spread after bias correction and to predict the radius of maximum winds using a climatological relationship based on wind intensity and storm latitude. The system was trained on the Australian best track TC data and the ECMWF-EPS TC data from two cyclone seasons. The system inserts corrected vortices into the original surface wind and pressure fields, which are then used to estimate wind exceedance probabilities, and to drive a wave model. The bias-corrected system has shown an overall skill improvement over the uncorrected ECMWF-EPS for all TC intensity and structure parameters with the most significant gains for the maximum wind speed prediction. The system has been operational at the Australian Bureau of Meteorology since November 2016.