The accurate knowledge of global solar radiation is of vital requirement for surveys in agronomy, hydrology, ecology, sizing of the photovoltaic or thermal solar systems, solar architecture, molten ...salt power plant and supplying energy to natural processes like photosynthesis and estimates of their performances. However, measurement of global solar radiation is not available in most locations across Africa. During the past 36 years in order to estimate global solar radiation on the horizontal surface on both daily and monthly mean daily basis, numerous empirical models have been developed for several locations in Africa. As a result various input parameters have been utilized and different functional forms used. In this study aim at classifying and reviewing the empirical models employed for estimating global solar radiation in Africa. The empirical models so far utilized were classified into six main categories and presented based on the input parameters employed. The models were further reclassified into several main sub-classes (groups) and finally represented according to their developing year. On the whole, 732 empirical models and 65 functional forms were recorded in literature for estimating global solar radiation in Africa in this review. Thus, this review would provide solar energy researchers in terms of identifying the input parameters and functional forms widely employed up till now as well as recognizing their importance for estimating global solar radiation in several locations in Africa.
Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall‐to‐wall information of canopy height with high spatial resolution and accuracy is not yet ...available on large scales. For the globally consistent TanDEM‐X data, simplifications are necessary to estimate canopy height with semi‐empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR).
We trained the semi‐empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM‐X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation.
Our results indicated that the empirical parameters substantially improved the estimation from a root‐mean‐square‐error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m 40.4% vs. 6.9 m 37.5%). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land‐use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals.
We demonstrate the potential of the combined exploitation of the TanDEM‐X and GEDI missions for a wall‐to‐wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation canopy height in tropical landscapes and on large scales with spaceborne laser and SAR data.
•Solubility of Glibenclamide in SCCO2 was measured for the first time.•Experiments were accomplished on temperatures (308–338 K) and pressures (12–30 MPa).•Solubility data were correlated by six ...density-based models and PR.•Model's accuracy was investigated by some statistical criteria of (AARD%) and (Radj).•Thermodynamic enthalpies of Glibenclamide were estimated.
Optimized production of micro and nanoparticles using the supercritical fluid technique requires information on the solubility of pharmaceutical materials in the supercritical fluid. In this article, Glibenclamide solubility in the SCCO2 was investigated for the first time at the temperature range 0f 308–338 K and pressure range of 12–30 MPa. Under this condition, the mole fraction of Glibenclamide was determined in the range 0.8 × 10−6 to 8.03 × 10−5. Furthermore, the six empirical models (i.e. Chrastil, Bartle et al., Mendez-Santiago and Teja (MST), Sparks et al., Bian et al., Sodeifian et al.) and PR-EoS with vdW2 mixing rule were utilized to correlate the solubility. The results confirmed that Sodeifian et al. and MST models exhibited the highest accuracy with respective Radj of 0.996 and 0.9859. Finally, the total (41.65 kJ mol-1), vaporization (65.5 kJ mol-1), and solvation. (-23.85 kJ mol-1) enthalpies were estimated by Chrastil's model and Bartle et al.
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This technical note sought to examine the ability of near-infrared reflectance spectroscopy (NIRS) to predict the chemical content and organic matter digestibility (OMD) of whole plants and the ...morphological components of forage sunflower. Empirical models for the prediction of OMD values from chemical components were developed, and their predictive ability vs. NIRS models was assessed. The total set of samples (n=147) was composed of whole plants (n=14) and morphological components (n=133) from different experiments performed at Galicia (Spain) and were scanned using a Foss NIR System 6500 instrument. The reference values of OMD corresponded to in vitro determinations (n=112 samples) from laboratory incubation tests using rumen fluid. The predictive capacity of the NIRS models was assessed by the coefficient of determination value in external validation (r2 ), showing good to excellent quality prediction of OMD and chemical components with values of r2 ≥0.88. However, the estimation of lignin did not show predictive utility (r2 =0.40). Using the NIRS models to predict the OMD of whole plants and morphological components of forage sunflower led to a decrease in the standard error in external validation, in contrast to the best empirical equation through the chemical components of samples (from ±8.25 to ±3.23%). This technical note showed that NIRS is a suitable technology, providing a rapid assessment of forage sunflower. However, these results should be considered preliminary, as they are based on a limited number of samples, and it is desirable to improve the performance of NIRS equations by increasing the dataset in future works.
Global solar radiation is a core component of scientific research and engineering application across a broad spectrum. However, its measurement is limited by a small number of stations due to the ...technical and financial restricts. Estimating solar radiation with the meteorological variables using empirical models is of benefit to obtain solar radiation data at global scale. Yet, there are various options of available empirical models to select the most suitable one. This study conducted a most comprehensive collection and review of empirical models employing the commonly measured meteorological variables and geographic factors. A total of 294 different types of empirical models were collected and classified into 37 groups according to input attributes. Such collection built an empirical model library providing an overall overview of the developed empirical models in literatures. Furthermore, the collected models were calibrated and evaluated at three meteorological stations in the Three Gorges Reservoir Area in China. This study suggests that these model-comparing processes can assist the governments, scientists and engineers in tailoring the most fitted model for specific applications and in particular areas.
•A most comprehensive collection and review on empirical model was conducted.•An empirical model library providing an overall overview of the models was build.•The study can guide the selection of the most appropriate model for specific applications.
•The thermal conductivity of a fine-grained sand has been investigated.•The measurements were carried out using a steady state divided bar method.•The degree of saturation has a significant effect on ...the sand thermal conductivity.•A selected prediction models were validated against the experimental results.•A new empirical model based on water content and porosity has been proposed.
The thermal properties of soils are of great importance in many thermo-active ground structures such as energy piles and borehole heat exchangers. In this paper the effect of the porosity and degree of saturation on the thermal conductivity of a sandy soil that has not been previously thermally tested is investigated using steady state experimental tests. The steady state apparatus used in these tests was designed to provide high performance in controlling all boundary conditions. Twenty thermal conductivity experimental tests have been carried out at different porosity and saturation values. The performance of selected prediction methods have been validated against the experimental results. The validation shows that none of the selected models can be used effectively in predicting the thermal conductivity of Tripoli sand at all porosity and saturation values. However, some can provide good agreement at dry or nearly dry condition while others perform well at high saturations. The performance of most of the selected models also increases as the soil approaches a two phase state where conduction plays the dominant role in controlling heat transfer. An empirical equation of thermal conductivity expressed as a function of water content and porosity has been developed based on the experimental results obtained.
Calibration of models has been performed for estimating global solar radiation with input as daily temperature extremes. Measured data for 22 locations were obtained from the Indian Meteorological ...Department (India) for 15 years and ten global solar radiation model forms were selected. Data were then bifurcated, where sixteen locations were used for training and the remaining six for validation. Models were compared using the statistical tools-based error analysis. The ranking of models was introduced using a Global Performance Indicator. Period-based testing of models declared Chen et al. model as the superior one. Location-based validation of the models was further performed which indicated the best suitability of the Bristow and Campbell model for semi-arid climate, Hargreaves et al. model for Arid climate and montane climate, Samani model for Tropical wet climate, Goodin et al. model for Humid subtropical climate and wet-dry climate, with GPI values of 5.3965, 0.5325, 0.5174, 0.5182, 0.5333 and 0.5862, respectively.
The discovery of physical laws consistent with empirical observations is at the heart of (applied) science and engineering. These laws typically take the form of nonlinear differential equations ...depending on parameters; dynamical systems theory provides, through the appropriate normal forms, an “intrinsic” prototypical characterization of the types of dynamical regimes accessible to a given model. Using an implementation of data-informed geometry learning, we directly reconstruct the relevant “normal forms”: a quantitative mapping from empirical observations to prototypical realizations of the underlying dynamics. Interestingly, the state variables and the parameters of these realizations are inferred from the empirical observations; without prior knowledge or understanding, they parametrize the dynamics intrinsically without explicit reference to fundamental physical quantities.
Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models ...with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 < 0.5), ecosystem respiration (R2 > 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.
This paper aims to review past literature on hotel location models and evaluate the state of the art, as well as set out future directions. This study divides hotel location models into three major ...categories: theoretical models, empirical models, and operational models. Four theoretical hotel location models are reviewed and discussed, including the tourist-historic city model, the mono-centric model, the agglomeration model, and the multi-dimensional model. Based on previous literature, six empirical models and three operational models of hotel location are elaborated. Furthermore, some challenges related to hotel location studies are discussed, and future research directions are provided. In particular, we advocate the development of more sophisticated hotel location models and the use of Geographic Information System (GIS) in hotel location analysis.
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