Journal articles on the topic 'Multi-parameter regression (MPR)'
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Moskal, Michał, Piotr Migas, and Mirosław Karbowniczek. "Multi-Parameter Characteristics of Electric Arc Furnace Melting." Materials 15, no. 4 (February 21, 2022): 1601. http://dx.doi.org/10.3390/ma15041601.
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The article presents the results of analyses of numerical modelling of selected factors in electric arc furnace melts. The aim of the study was to optimise the melting process in an electric arc furnace using statistical-thermodynamic modelling based on, among other things, multiple linear regression (MLR). The article presents tools and methods which make it possible to identify the most significant indicators of the process carried out on the analysed unit from the point of view of improvement. The article presents the characteristics of the process and creation of the MLR model and, by applying its numerical analyses and results of calculations and simulations for selected variables and indicator, identifying the operation of a selected furnace. Developed model to demand of electric energy identification was used for calculations of energy balances, the distribution of the energy used in the furnace was presented.
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Tomar, Prashant Singh. "Rainfall Simulation using ANN based Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) Technique for Bhopal, Madhya Pradesh." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1069–76. http://dx.doi.org/10.22214/ijraset.2021.36470.
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Rainfall forecasting represents a tremendously significant matter in field of hydrology. In this study, was undertaken to develop and evaluate the applicability of Multilayer Perceptron (MLP) and Multi Linear Regression (MLR) techniques. The performance of the developed models, on the basis of training and testing, was judged on the basis of four statistical measures such as Root Mean Squared Error (MSE), Coefficient of Efficiency (CE), Correlation Coefficient (r) and Coefficient of Determination(R2) during monsoon period (June to September) for Bhopal, Madhya Pradesh, India. The daily data of minimum temperature, maximum temperature, wind speed and relative humidity were used for rainfall prediction. The appropriate parameter combination of input variables for MLP was used to predict rainfall. The Neuro Solution 5.0 software and Microsoft Excel were used in analysis and the performance evaluation of developed models, respectively. The input pairs in the training data set were applied to the network of a selected architecture and training was performed using back propagation algorithm for MLP models was designed with Gaussian membership function, Takagi- Sugeno- Kang fuzzy model, hyperbolic tangent activation function and Delta-Bar-Delta learning algorithm. Ten MLP models and MLR were selected based on the performance evaluation indices during testing period. MLP models were found to be much closer to the observed values of rainfall as compared to MLR.
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Cai, C. Z., X. J. Zhu, J. F. Pei, and G. L. Wang. "Study on the Process Optimization of Synthesizing Co3O4 Nanoparticles by Homogeneous Precipitation Based on Support Vector Regression." Materials Science Forum 689 (June 2011): 211–19. http://dx.doi.org/10.4028/www.scientific.net/msf.689.211.
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The Co3O4 is the major raw material for fabricating lithium cobalt oxide electrode of lithium ion battery. According to the experimental dataset on grain diameter of Co3O4 nanoparticles synthesized by homogeneous precipitation under four main process parameters including the concentration of Co(NO3)2•6H2O solution, mole ratio of reactants, reaction temperature and reaction time, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, is introduced to establish a model for estimating grain diameter of Co3O4 nanoparticles. The comparison of prediction results strongly support the prediction and generalization abilities of SVR are superior to those of multivariable gradual regression (MGR). Meanwhile, the index of grain diameter of Co3O4 nanoparticles under an independent combination of process parameters predicted by SVR model is more accurate than that by MGR model. The multi-factors analysis results based on SVR model are consistent with that of the literatures. This study suggests that SVR is a theoretical significance and potential practical value in development of smaller grain diameter of Co3O4 nanoparticles via guiding experiment.
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Yasmirullah, S. D. P., B. W. Otok, J. D. T. Purnomo, and D. D. Prastyo. "Parameter Estimation of Multivariate Adaptive Regression Spline (MARS) with Stepwise Approach to Multi Drug-Resistant Tuberculosis (MDR-TB) Modeling in Lamongan Regency." Journal of Physics: Conference Series 1752, no. 1 (February 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1752/1/012017.
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Zhou, Wengang. "Aero-engine exhaust gas temperature prediction based on adaptive disturbance quantum-behaved particle swarm optimization." Advances in Mechanical Engineering 14, no. 8 (August 2022): 168781322211190. http://dx.doi.org/10.1177/16878132221119044.
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Aircraft engine EGT (exhaust gas temperature) is uncertain. In order to reduce the EGT influence to the health of the engine, it is important to carry out the prediction. A novel EGT prediction method based on the combination method is proposed. Firstly, MIV (Mean Impact Value) was used to reduce the dimension of the input numbers. Second, the EGT was predicted by some single models such as BP (back propagation) neural network model, SVR (support vector regression) model, PLS (partial least square) model, GM(1,N) (multi-parameter input gray prediction model), MLR (multiple linear regression) model. Then absolute mean error was used to evaluate the predictive results of single models and the best predictive results of four single methods were selected to establish combination model with PSO (particle swarm optimization). Finally, the combination model was used in predicting the EGT of V2500 aero-engine. Experiment results show that the combination method is more reliable and suitable than the single models for aero-engine EGT prediction.
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Candra Damis Widiawaty, Ahmad Indra Siswantara, Gun Gun R Gunadi, Mohamad Arif Andira, Budiarso, Muhammad Arif Budiyanto, M. Hilman Gumelar Syafei, and Dendy Adanta. "Optimization of inverse-Prandtl of Dissipation in standard k-ε Turbulence Model for Predicting Flow Field of Crossflow Turbine." CFD Letters 14, no. 1 (January 11, 2022): 112–27. http://dx.doi.org/10.37934/cfdl.14.1.112127.
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Despite the successful use of the Standard model in simulating turbulent flow for many industrially relevant flows, the model is still less accurate for a range of important problems, such as unconfined flows, curved boundary layers, rotating flows, and recirculating flows. As part of the authors’ effort to extend the model applicability and reliability, this paper aims to study the effects of diffusivity parameter called the turbulent Prandtl number of dissipation rate () on the Standard model performance for predicting recirculating flow in a crossflow water turbine. The value of this parameter was varied from 0.5 to 1.5 in the CFD simulations, and the results were compared to the more sophisticated model, namely the RNG , which has been first qualitatively validated by an experimental result. In addition, the parameter value was also adjusted using the Multi-Linear Regression (MLR) method ranging from 0.42 to 1.5 to complement the CFD simulations. It was observed that reducing the value is effective in minimizing the average deviation of the turbulence properties concerning the RNG model. However, the adjusted model still faces difficulty in accurately predicting the pressure and velocity field. Based on this result, adjusting the constant in the Standard turbulence model has the potential to improve the model performance for modelling recirculating flow in terms of the turbulence properties, but still needs further investigation for the flow properties.
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Eric, Slavica, Marko Kalinic, Aleksandar Popovic, Halid Makic, Elvisa Civic, and Merja Bektasevic. "The importance of the accuracy of the experimental data for the prediction of solubility." Journal of the Serbian Chemical Society 75, no. 4 (2010): 483–95. http://dx.doi.org/10.2298/jsc090809022e.
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Aqueous solubility is an important factor influencing several aspects of the pharmacokinetic profile of a drug. Numerous publications present different methodologies for the development of reliable computational models for the prediction of solubility from structure. The quality of such models can be significantly affected by the accuracy of the employed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for model training was investigated. Three data sets were used as training sets - Data Set 1 containing solubility data collected from various literature sources using a few criteria (n = 319), Data Set 2 created by substituting 28 values from Data set 1 with uniformly determined experimental data from one laboratory (n = 319) and Data Set 3 created by including 56 additional components, for which the solubility was also determined under uniform conditions in the same laboratory, in the Data Set 2 (n = 375). The selection of the most significant descriptors was performed by the heuristic method, using one-parameter and multi-parameter analysis. The correlations between the most significant descriptors and solubility were established using multi-linear regression analysis (MLR) for all three investigated data sets. Notable differences were observed between the equations corresponding to different data sets, suggesting that models updated with new experimental data need to be additionally optimized. It was successfully shown that the inclusion of uniform experimental data consistently leads to an improvement in the correlation coefficients. These findings contribute to an emerging consensus that improving the reliability of solubility prediction requires the inclusion of many diverse compounds for which solubility was measured under standardized conditions in the data set.
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Pandey, S., N. R. Patel, A. Danodia, and R. Singh. "DISCRIMINATION OF SUGARCANE CROP AND CANE YIELD ESTIMATION USING LANDSAT AND IRS RESOURCESAT SATELLITE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 229–33. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-229-2019.
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<p><strong>Abstract.</strong> The objective of this research work aims at crop acreage estimation at mill catchment level, derivation of sugarcane phenology and yield estimation at field level. The study was carried out in Kisan Sahkari Chini Mill catchment, Nanauta, Saharanpur, Uttar Pradesh. Extensive and systematic field sampling was carried out for ground-truth observations, biophysical measurements (LAI and above/below canopy PAR) and mill-able cane yield through crop cutting experiments. Major emphasis were laid on sugarcane crop discrimination, biophysical parameter estimation, generation of phenological metrics and yield model development for sugarcane crop at mill catchment level. Sugarcane crop discrimination and its acreage estimation was done using multi-sensor satellite data. The sugarcane classification accuracies were &gt;&thinsp;92% for LISS-IV, &gt;&thinsp;86% for Landsat-8 and &gt;&thinsp;83% for LISS-III classified image. The sugarcane phenological matrices at field level derived using time-series of NDVI for a period of 2015&ndash;2016 through TIMESAT software. To retrieve the biophysical parameters particularly leaf area index, best predictive function developed with vegetation indices (EVI, NDVI, SAVI) through correlation and regression analysis along this cane yield estimation attempted with multi-date (eight-day) NDVI from Landsat OLI. Yield models developed for ratoon cane and planted cane explained variance in yield significantly with coefficient of determination (R<sup>2</sup>) values equal to 0.83 and 0.69, respectively. Similar predictive functions were also established with monthly composite dataset for village-level yield estimates with step wise regression (R<sup>2</sup>&thinsp;=&thinsp;0.83) (P&thinsp;=&thinsp;0.00001), Multi linear regression (MLR) (R<sup>2</sup>&thinsp;=&thinsp;0.792) (P&thinsp;=&thinsp;0.00081) and Random forest regression (R<sup>2</sup>&thinsp;=&thinsp;0.466) (P&thinsp;=&thinsp;0.038).</p>
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Zhu, Hongxin, Kun Zou, and Wenlan Bao. "Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning." Journal of Engineered Fibers and Fabrics 16 (January 2021): 155892502110548. http://dx.doi.org/10.1177/15589250211054833.
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In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.
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Park, Donggeun, and Jeung Sang Go. "Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD." Processes 8, no. 11 (November 23, 2020): 1521. http://dx.doi.org/10.3390/pr8111521.
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In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design.
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Zhang, Yi, Yizhe Yang, Qinwei Zhang, Runqing Duan, Junqi Liu, Yuchu Qin, and Xianzhi Wang. "Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation." Remote Sensing 15, no. 1 (December 20, 2022): 7. http://dx.doi.org/10.3390/rs15010007.
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Leaf Area Index (LAI) is an important parameter which can be used for crop growth monitoring and yield estimation. Many studies have been carried out to estimate LAI with remote sensing data obtained by sensors mounted on Unmanned Aerial Vehicles (UAVs) in major crops; however, most of the studies used only a single type of sensor, and the comparative study of different sensors and sensor combinations in the model construction of LAI was rarely reported, especially in soybean. In this study, three types of sensors, i.e., hyperspectral, multispectral, and LiDAR, were used to collect remote sensing data at three growth stages in soybean. Six typical machine learning algorithms, including Unary Linear Regression (ULR), Multiple Linear Regression (MLR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Back Propagation (BP), were used to construct prediction models of LAI. The results indicated that the hyperspectral and LiDAR data did not significantly improve the prediction accuracy of LAI. Comparison of different sensors and sensor combinations showed that the fusion of the hyperspectral and multispectral data could significantly improve the predictive ability of the models, and among all the prediction models constructed by different algorithms, the prediction model built by XGBoost based on multimodal data showed the best performance. Comparison of the models for different growth stages showed that the XGBoost-LAI model for the flowering stage and the universal models of the XGBoost-LAI and RF-LAI for three growth stages showed the best performances. The results of this study might provide some ideas for the accurate estimation of LAI, and also provide novel insights toward high-throughput phenotyping of soybean with multi-modal remote sensing data.
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Ahmed, Hemn U., Azad A. Mohammed, and Ahmed Mohammed. "Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete." PLOS ONE 17, no. 5 (May 25, 2022): e0265846. http://dx.doi.org/10.1371/journal.pone.0265846.
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A variety of ashes used as the binder in geopolymer concrete such as fly ash (FA), ground granulated blast furnace slag (GGBS), rice husk ash (RHA), metakaolin (MK), palm oil fuel ash (POFA), and so on, among of them the FA was commonly used to produce geopolymer concrete. However, one of the drawbacks of using FA as a main binder in geopolymer concrete is that it needs heat curing to cure the concrete specimens, which lead to restriction of using geopolymer concrete in site projects; therefore, GGBS was used as a replacement for FA with different percentages to tackle this problem. In this study, Artificial Neural Network (ANN), M5P-Tree (M5P), Linear Regression (LR), and Multi-logistic regression (MLR) models were used to develop the predictive models for predicting the compressive strength of blended ground granulated blast furnace slag and fly ash based-geopolymer concrete (GGBS/FA-GPC). A comprehensive dataset consists of 220 samples collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, eleven effective variable parameters on the compressive strength of the GGBS/FA-GPC, including the Activated alkaline solution to binder ratio (l/b), FA content, SiO2/Al2O3 (Si/Al) of FA, GGBS content, SiO2/CaO (Si/Ca) of GGBS, fine (F) and coarse (C) aggregate content, sodium hydroxide (SH) content, sodium silicate (SS) content, (SS/SH) and molarity (M) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the ANN model better predicted the compressive strength of GGBS/FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the alkaline liquid to binder ratio, fly ash content, molarity, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the GGBS/FA-GPC.
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Dybkær, Karen, Hanne Due, Rasmus Froberg Brøndum, Ken H. Young, and Martin Bøgsted. "Addition of Drug-Response Specific Micro-RNAs to the International Prognostic Index Improves Prognostic Stratification of GCB-DLBCL Patients Treated with R-CHOP." Blood 134, Supplement_1 (November 13, 2019): 1623. http://dx.doi.org/10.1182/blood-2019-122351.
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Background: Patients with Diffuse large B-cell lymphoma (DLBCL) in approximately 40% of cases suffer from primary refractory disease and treatment induced immuno-chemotherapy resistance demonstrating that standard provided treatment regimens are not sufficient to cure all patients. Early detection of resistance is of great importance and defining microRNA (miRNA) involvement in resistance could be useful to guide treatment selection and help monitor treatment administration while sparing patients for inefficient, but still toxic therapy. Concept and Aims: With information on drug-response specific miRNAs, we hypothesized that multi-miRNA panels can improve robustness of individual clinical markers and serve as a prognostic classifier predicting disease progression in DLBCL patients. Methods: Fifteen DLBCL cell lines were tested for sensitivity towards rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). Cell line specific seeding concentrations was used to ensure exponential growth and each cell line was subjected to 16 concentrations in serial 2-fold dilutions and number of metabolic active cells was evaluated after 48 hours of drug exposure using MTS assay. For each drug, we ranked the cell lines according to their sensitivity and categorized them as sensitive, intermediate responsive, or resistant. Differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs to be associated with response to compounds of the R-CHOP regimen, by selecting probes with a log fold change larger than 2. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis identified 43 miRNAs associated to response to R-CHOP. Using the Affymetrix HG-U133+2 platform, expression levels of the miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers were build using multiple Cox regression or random survival forest. Results: Generated prognostic miRNA-panel classifiers were tested for predictive accuracies and were subsequently evaluated by Brier scores and time varying area under the ROC curves (tAUC). Progression-free survival (PFS) was chosen as the outcome, since it is a treatment evaluation parameter as closely as possible to the time of drug exposure and the tested miRNAs were all associated directly to drug specific response. Furthermore, overall survival (OS) was used for verification of findings. Comparison of analyses conducted for the respective cohorts (All DLBCL, ABC, and GCB patients) showed the lowest prediction errors for all models within the GCB subclass with a multivariate Cox miRNA-panel model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the miRNA-panel and international prognostic index (IPI) substantially increased prognostic performance in GCB classified patients, indicating a prognostic signal from the response-specific miRNAs independent of IPI. In conclusion: We found as proof of concept that adding gene expression data detecting drug-response specific miRNAs to the clinically established IPI improved the prognostic stratification of GCB-DLBCL patients treated with R-CHOP. Disclosures No relevant conflicts of interest to declare.
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Ahmed, Hemn Unis, Ahmed Salih Mohammed, Azad A. Mohammed, and Rabar H. Faraj. "Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes." PLOS ONE 16, no. 6 (June 14, 2021): e0253006. http://dx.doi.org/10.1371/journal.pone.0253006.
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Geopolymer concrete is an inorganic concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to the geopolymer concrete being an eco-efficient and environmentally friendly construction material. A variety of ashes used as the binder in geopolymer concrete such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash, fly ash was commonly consumed to prepare geopolymer concrete composites. The most important mechanical property for all types of concrete composites, including geopolymer concrete, is the compressive strength. However, in the structural design and construction field, the compressive strength of the concrete at 28 days is essential. Therefore, achieving an authoritative model for predicting the compressive strength of geopolymer concrete is necessary regarding saving time, energy, and cost-effectiveness. It gives guidance regarding scheduling the construction process and removal of formworks. In this study, Linear (LR), Non-Linear (NLR), and Multi-logistic (MLR) regression models were used to develop the predictive models for estimating the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In this regard, a comprehensive dataset consists of 510 samples were collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, twelve effective variable parameters on the compressive strength of the FA-GPC, including SiO2/Al2O3 (Si/Al) of fly ash binder, alkaline liquid to binder ratio (l/b), fly ash (FA) content, fine aggregate (F) content, coarse aggregate (C) content, sodium hydroxide (SH)content, sodium silicate (SS) content, (SS/SH), molarity (M), curing temperature (T), curing duration inside ovens (CD) and specimen ages (A) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the NLR model performed better for predicting the compressive strength of FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the curing temperature, alkaline liquid to binder ratio, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the FA-GPC.
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Jaouimaa, Fatima-Zahra, Il Do Ha, and Kevin Burke. "Multi-parameter regression survival modelling with random effects." Statistical Modelling, September 7, 2022, 1471082X2211173. http://dx.doi.org/10.1177/1471082x221117377.
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We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters (i.e., scale and shape). This is in contrast with the standard convention of having a single covariate-dependent parameter, typically the scale. Taking what is referred to as a multi-parameter regression (MPR) approach to modelling has been shown to produce flexible and robust models with relatively low model complexity cost. However, it is very common to have clustered data arising from survival analysis studies, and this is something that is under developed in the MPR context. The purpose of this article is to extend MPR models to handle multivariate survival data by introducing random effects in both the scale and the shape regression components. We consider a variety of possible dependence structures for these random effects (independent, shared and correlated), and estimation proceeds using a h-likelihood approach. The performance of our estimation procedure is investigated by a way of an extensive simulation study, and the merits of our modelling approach are illustrated through applications to two real data examples, a lung cancer dataset and a bladder cancer dataset.
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MacKenzie, Gilbert, Miliça Blagojevic-Bucknall, Yasin Al-tawarah, and Defen Peng. "The XGTDL family of survival distributions." Japanese Journal of Statistics and Data Science, June 26, 2021. http://dx.doi.org/10.1007/s42081-021-00129-9.
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AbstractNon-PH parametric survival modelling is developed within the framework of the multiple logistic function. The family considered comprises three basic models: (a) a PH model, (b) an accelerated life model and (c) a model which is non-proportional hazards and non-accelerated life. The last model, the generalised time-dependent logistic model was described first by the author in 1996 and this model gives its name to the entire family. The family is generalised by means of a Gamma frailty extension which is shown to accommodate crossing hazards data. A further generalisation is the inclusion of a dispersion model. These extensions lead naturally to the concept of a multi-parameter regression model described by Burke and MacKenzie in which the scale and shape parameters are modelled simultaneously as functions of covariates. Where possible, we include the MPR extension in the XGTDL family. Following a simulation study, the new models are used to analyse two sets survival data and the methods are discussed.
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Vogel, Sebastian, Eric Bönecke, Charlotte Kling, Eckart Kramer, Katrin Lück, Golo Philipp, Jörg Rühlmann, Ingmar Schröter, and Robin Gebbers. "Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping." Precision Agriculture, July 26, 2021. http://dx.doi.org/10.1007/s11119-021-09830-x.
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AbstractLiming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil’s base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value.