## What is/are Predict Surface?

Predict Surface - We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.^{[1]}A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation.

^{[2]}Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters.

^{[3]}We predict surface overlaps using an estimated OctoMap.

^{[4]}Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas.

^{[5]}5, which is acceptable to predict surface albedo.

^{[6]}In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture.

^{[7]}The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.

^{[8]}These optical methods could help predict surface drying state, thereby improving the accuracy of dust emissions risk assessment protocols that support mining industries intervention and mitigation strategies.

^{[9]}Regression modeling was able to predict surface resistivity using porosity data and thus may be a valuable concrete mixture screening tool.

^{[10]}Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality.

^{[11]}Therefore, simplified models for effective stress ground analysis should be used with caution by practicing engineers to predict surface spectra but can be used confidently to assess the potential for liquefaction triggering.

^{[12]}Stage 1 includes thermomechanical SLM simulation to predict surface geometry and is applied to model four 50-μm layers of a 316L part having a 4 mm × 4 mm footprint.

^{[13]}Our study shows that the surface diffusion coefficient can be used to quantitatively predict surface crystallization rates in a chemically diverse range of materials.

^{[14]}As an alternative to the traditional methods, this study developed a fast and computationally economical machine learning-based model to predict surface-averaged external pressure coefficients of a building with an unconventional configuration using three tree-based regressors: Adaboost, Extra Tree, and Random Forest.

^{[15]}In the present study, a mathematical model based on volume-averaged porous media equations is employed to predict surface operation time.

^{[16]}The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit.

^{[17]}On the other hand, for future experimental measurements of phase equilibria, our results could serve as an initial approximation of equilibrium, and the correlations obtained for the binary parameters of the linear gradient theory and parachor method can be used to predict surface tension at other temperatures outside the range 288.

^{[18]}Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity.

^{[19]}We ultimately conclude that despite these challenges, using dynamic vegetation models to predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.

^{[20]}To predict surface evolution, one should ideally consider the tool as a set of grains and take into account the cutting action of each grain.

^{[21]}Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance.

^{[22]}Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.

^{[23]}In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman.

^{[24]}These models latter can be used to predict surface roughness according to technological variables.

^{[25]}Based on theories of Gaussian pulse beam and picosecond laser ablation material, machined surface morphology model was developed to predict surface roughness Sa and removal depth of CVDD.

^{[26]}Based on those observations a new parameter involving these three geometrical factors was developed to predict surface crack initiation sites.

^{[27]}Hydrological simulation models predict surface and artificial subsurface flow at different scales.

^{[28]}Finally, the model is extended to predict surface tension for mixture solutions, considering both independent and dependent adsorptions of different solute species to the liquid-vapor interface.

^{[29]}The model output shows the importance of slope and ground heating index (GHI) – an estimation of the amount of energy transferred to the ground, to predict surface displacements independently from the type of considered processes.

^{[30]}In this present study, an experimental investigation has been done to predict surface roughness by taking into consideration of the following parameters: cutting speed, rate of feed, cutting depth and nose radius in the hard turning of Duplex 2205 (ASTM A276) round bar material utilizing carbide tip tool material.

^{[31]}In addition, a linear model generated to predict surface roughness performed best at moderate to medium level of processing (fluences in the region of 0.

^{[32]}The accumulative change value for surface subsidence is subject to logarithmic law, and our results can be used to quantitatively guide the collaborative relationship between tunnel excavation and support, dynamically adjust TBM construction parameters, and predict surface subsidence and surrounding rock deformation, thereby providing guidance and reference for similar projects.

^{[33]}We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles.

^{[34]}Finite element method (FEM) was used to study elastic deformation effects, explain the observed friction difference, and predict surface material influence.

^{[35]}In this paper, a radial basis neural network is proposed to predict surface roughness.

^{[36]}ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.

^{[37]}Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures.

^{[38]}This article investigates the influence of LP on surface roughness and wettability of AISI 316L SS produced by DED and proposes equations that predict surface roughness and remelted layer thickness (RLT) as a function of laser power (P).

^{[39]}The developed mathematical models for ceramic and coated carbide inserts are capable to predict surface roughness with 97.

^{[40]}This method to predict surface temperature is then combined into an optimization model for real-time layer time control.

^{[41]}This model can be used to predict surface behaviour when cavitation induced by e.

^{[42]}An effective and efficient methodology is proposed to predict surface roughness by online monitoring of surface quality using accelerometer signals.

^{[43]}We use these simulations to predict surface brightnesses in Hα, which we show to have a characteristic ring-shaped morphology for haloes in a narrow mass range between ≃ 109.

^{[44]}The best way to predict surface movement processes is exploring sensitive areas of surface movement.

^{[45]}To predict the correct time for rehabilitation or routine maintenance, it is necessary to have functions or performance models to predict surface deterioration.

^{[46]}However, it is difficult to guarantee shape accuracy and predict surface roughness evolution.

^{[47]}A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.

^{[48]}We also predict surface second harmonic conversion efficiencies of order 0.

^{[49]}An evaluation of the model setup is performed, which exhibited the model’s ability to predict surface meteorological and chemical variables well compared with observations, and consistent with other studies.

^{[50]}

## Accurately Predict Surface

The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.^{[1]}Therefore, further work is required to accurately extract Tolman lengths and rigidities of alkanols because DFT with PCP-SAFT does not accurately predict surface tensions of these fluids.

^{[2]}It is shown that the Delany–Bazley and Miki models can accurately predict surface impedance of multi-component polyester nonwovens, but the Komatsu model is less accurate, especially at the low-frequency range.

^{[3]}

## Could Predict Surface

ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.^{[1]}A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx.

^{[2]}

## predict surface roughnes

The unexpected surface roughness and the error between the experimental and predicted values show that the obtained models are, in most cases, not adequate to predict surface roughness when the power is greater than a given limit.^{[1]}Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.

^{[2]}These models latter can be used to predict surface roughness according to technological variables.

^{[3]}Based on theories of Gaussian pulse beam and picosecond laser ablation material, machined surface morphology model was developed to predict surface roughness Sa and removal depth of CVDD.

^{[4]}In this present study, an experimental investigation has been done to predict surface roughness by taking into consideration of the following parameters: cutting speed, rate of feed, cutting depth and nose radius in the hard turning of Duplex 2205 (ASTM A276) round bar material utilizing carbide tip tool material.

^{[5]}In addition, a linear model generated to predict surface roughness performed best at moderate to medium level of processing (fluences in the region of 0.

^{[6]}In this paper, a radial basis neural network is proposed to predict surface roughness.

^{[7]}ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs.

^{[8]}This article investigates the influence of LP on surface roughness and wettability of AISI 316L SS produced by DED and proposes equations that predict surface roughness and remelted layer thickness (RLT) as a function of laser power (P).

^{[9]}The developed mathematical models for ceramic and coated carbide inserts are capable to predict surface roughness with 97.

^{[10]}An effective and efficient methodology is proposed to predict surface roughness by online monitoring of surface quality using accelerometer signals.

^{[11]}However, it is difficult to guarantee shape accuracy and predict surface roughness evolution.

^{[12]}On this basis, a new approach was developed to measure the amount of grinding wheel wear (GWW) and predict surface roughness (SR) in accordance with the compressed air measuring head and hybrid algorithms fuzzy neural networks (ANFIS)–Gaussian regression function (GPR) and Taguchi empirical analysis.

^{[13]}The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis.

^{[14]}A two-latent variable Partial Least Squares Regression model developed on Raman spectral data could predict surface roughness with a coefficient of determination (R2) of approx.

^{[15]}The aim of the model is to minimise the contribution of uncertainty errors due to the stochastic distribution of the phases present within the material microstructure, to better predict surface roughness under different cutting conditions.

^{[16]}Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling.

^{[17]}On the basis of the analysis, a model that can predict surface roughness is established that provides a means of optimizing micro-textured cutter design and machining inclination angles when milling titanium alloy.

^{[18]}While approximate analytical electromagnetic (EM) models paired with surface gravity wave spectra have been used to predict surface roughness emissivity enhancement, these methods do not reveal details of the response to foam, breaking wave, and WC geometry.

^{[19]}The proposed model can predict surface roughness values with an error rate of 0.

^{[20]}Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm.

^{[21]}A MATLAB TM interface was developed to predict surface roughness and geometric defects (circularity, cylindricity, and localization).

^{[22]}

## predict surface strain

A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.^{[1]}A finite element model was developed using the tendon geometry and inhomogeneous material properties to predict surface strains for loading conditions mimicking experimental loading conditions.

^{[2]}The two-step framework uses a multibody musculoskeletal model to predict tibiofemoral kinematics which are then imposed on a deformable model to predict surface strains.

^{[3]}

## predict surface water

Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas.^{[1]}The results indicate that the FNN based soft-sensor can predict surface water quality simultaneously with suitable prediction accuracy.

^{[2]}The Pesticide Water Calculator (PWC), a model used for the regulation of pesticides in the US, was used to predict surface water and sediment pore water pesticide concentrations in vernal pool habitats.

^{[3]}

## predict surface tension

On the other hand, for future experimental measurements of phase equilibria, our results could serve as an initial approximation of equilibrium, and the correlations obtained for the binary parameters of the linear gradient theory and parachor method can be used to predict surface tension at other temperatures outside the range 288.^{[1]}Finally, the model is extended to predict surface tension for mixture solutions, considering both independent and dependent adsorptions of different solute species to the liquid-vapor interface.

^{[2]}Therefore, further work is required to accurately extract Tolman lengths and rigidities of alkanols because DFT with PCP-SAFT does not accurately predict surface tensions of these fluids.

^{[3]}

## predict surface subsidence

The accumulative change value for surface subsidence is subject to logarithmic law, and our results can be used to quantitatively guide the collaborative relationship between tunnel excavation and support, dynamically adjust TBM construction parameters, and predict surface subsidence and surrounding rock deformation, thereby providing guidance and reference for similar projects.^{[1]}This paper introduced the fundamental principle of the SWBM technique and then presented a model based on the theory of equivalent mining height (EMH) to predict surface subsidence.

^{[2]}

## predict surface integrity

Current modeling approaches struggle to predict surface integrity, and typically neglect the effects of progressive tool-wear, resulting in inefficient ‘static’ process parameters.^{[1]}The obtained result revealed that the model of the quadratic polynomial is fitted to predict surface integrity.

^{[2]}

## predict surface temperature

This method to predict surface temperature is then combined into an optimization model for real-time layer time control.^{[1]}By using 3D models, we can self-consistently predict surface temperatures and the location of water vapor, clouds, and surface ice in the climate system.

^{[2]}

## predict surface quality

We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.^{[1]}Numerical modeling of machining parameters is performed using Multi-Layer Perceptron Artificial Neural Network (MLP ANN) and Multiple Regression Model (MR) to predict surface quality.

^{[2]}

## predict surface soil

In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture.^{[1]}The most efficient satellite features, which accurately predict surface soil salinity by its higher spectral reflectance, were derived from the Sentinel-2 data to be used as explanatory variables in the analysis.

^{[2]}