Mean Bias Error Research Articles

Measuring porous media velocity fields and grain bed architecture with a quantitative PLIF-based technique

Porous media flows are common in both natural and anthropogenic systems. Mapping these flows in a laboratory setting is challenging and often requires non-intrusive measurement techniques, such as particle image velocimetry (PIV) coupled with refractive index matching (RIM). RIM-coupled PIV allows the mapping of velocity fields around transparent solids by analyzing the movement of neutrally buoyant micron-sized seeding particles. The use of this technique in a porous medium can be problematic because seeding particles adhere to grains, which causes the grain bed to lose transparency and can obstruct pore flows. Another non-intrusive optical technique, planar laser-induced fluorescence (PLIF), can be paired with RIM and does not have this limitation because fluorescent dye is used instead of particles, but it has been chiefly used for qualitative flow visualization. Here, we propose a quantitative PLIF-based methodology to map both porous media flow fields and porous media architecture. Velocity fields are obtained by tracking the advection-dominated movement of the fluorescent dye plume front within a porous medium. We also propose an automatic tracking algorithm that quantifies 2D velocity components as the plume moves through space in both an Eulerian and a Lagrangian framework. We apply this algorithm to three data sets: a synthetic data set and two laboratory experiments. Performance of this algorithm is reported by the mean (bias error, B) and standard deviation (random error, SD) of the residuals between its results and the reference data. For the synthetic data, the algorithm produces maximum errors of B & SD = 32% & 23% in the Eulerian framework, respectively, and B & SD = −0.04% & 3.9% in the Lagrangian framework. The small-scale laboratory experimental data requires the Eulerian framework and produce errors of B & SD = −0.5% & 33%. The Lagrangian framework is used on the large-scale laboratory experimental data and produces errors of B & SD = 5% & 44%. Mapping the porous media architecture shows negligible error for reconstructing calibration grains of known dimensions.

Impact of solar radiation on human comfort in a vehicle cabin: An analysis of body segment mean radiant temperature

Ensuring ideal thermal comfort inside vehicle cabins can be a challenging task, especially when accounting for the impact of solar radiation on human occupants. The mean radiant temperature (MRT) is crucial in determining thermal comfort levels in enclosed spaces. This study investigates the impact of solar radiation on the MRTs of local human body segments in a vehicle cabin. A modified solar-adjusted MRT model was proposed, and experiments were conducted to validate the accuracy of the model in predicting MRT values under varying solar radiation conditions inside a vehicle cabin. The model had a mean absolute error of 1.02 °C, a mean absolute percentage error of 2.83%, a root mean square error of 1.28 °C, and a mean bias error of −0.17 °C. The results demonstrate that the proposed model provides precise and reliable MRT predictions with low variability and unbiased outcomes. The proposed model was also used to analyze the effects of solar radiation on MRTs during different seasons, revealing the vulnerability of specific body parts to solar radiation exposure. This study emphasizes the importance of considering solar radiation when designing and assessing thermal comfort in vehicle cabins. The proposed model can be integrated into existing thermal comfort models to provide more accurate and reliable assessments of the impact of solar radiation on vehicle cabin occupants.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study.

This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

A Comparative Study for Estimating Reference Evapotranspiration Models over Kano, Nigeria

The major factor faced by Agricultural activities is water scarcity. Water is very essential in Agricultural activities (plantation), crop acquires water naturally by precipitation and subsurface moisture, when the supply of water is inadequate for crop use, mostly results to irrigation. This present study estimates and compares six various universally accepted models for estimating reference evapotranspiration (ET0) for Kano situated in the Sahelian climatic zone of Nigeria using measured meteorological parameters of monthly average daily global solar radiation, sunshine hour, wind speed, minimum and maximum temperatures and relative humidity covering a period of thirty one years (1988 – 2018). Four different statistical validation indices of Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE) and coefficient of correlation (R) were carried out to test the accuracy of the evaluated models. The result indicated that high value of ETo was found in the month of April with 10.0256 mm/day for Kano and a low value was found to be in August with 5.0804 mm/day for Kano. The Blaney – Morin Nigeria model was found more accurate for Kano with RMSE, MBE, MAE and R values as 1.5078 mm/day, -1.4634 mm/day, 1.4634 mm/day and 0.9790 respectively.

Application of layer view factor method in high temperature thermal storage packed bed

This study provides a comprehensive evaluation of radiation effects on the thermal behavior of a packed bed using a discrete model for heat transfers integrating a novel method called Layer View Factor for view factors estimation, which significantly decreases computational time by less than 9000 times compared to the parallel ray tracing method. The model is validated and strongly aligned with experimental data at 823K, with a maximum mean bias and root mean square errors of ±4.5K and 10K, respectively. Analyses of temperature profiles and thermocline lengths for charging, discharging, and stand-by processes at a charge temperature of 1473K were performed, finding a thermal flattening effect with radiation, leading to lower peak temperatures and quicker thermocline length evolutions. Notably, the stand-by process exhibited the largest impact, with a maximum temperature difference of up to 100K compared to the non-radiation case. Moreover, analysis of the temperature and energy of discharge found that the maximum discharge temperature drops below 90% of the charge temperature when radiation is included. Finally, efficiency analysis of the processes showed differences of up to 2% between having or not the radiation effect, aiming its role as a redistribution mechanism in the packed bed’s temperature.

Modeling of soil moisture movement and wetting behavior under point-source trickle irrigation

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017–2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h−1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash–Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h−1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm3 cm−3 just after irrigation in 2 L h−1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm3 cm−3 were in 4 L h−1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm3 cm−3 were in 8 L h−1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system.

Improving CINRAD Radar QPE Through GPM‐DPR Reflectivity Bias Correction

AbstractReflectivity bias of ground‐based weather radar (GR) is a common error source that can lead to biases in quantitative precipitation estimation (QPE). In this study, the Dual‐frequency Precipitation Radar onboard the Global Precipitation Measurement Mission Core Observatory (GPM‐DPR) is used to correct GR reflectivity bias. The reflectivity bias correction comprehensively considers variations in GR‐DPR reflectivity difference associated with radar frequency difference, data matching approach, precipitation type, GR orographic beam blockage, attenuation, and sample size. The criteria to perform a robust GR‐DPR comparison are presented. Disdrometer validation, based on eight typical rain events from April to August 2019, demonstrates that GPM‐DPR can effectively reduce GR reflectivity bias to ±1 dB. For the period from June to August 2021, the reflectivity correction method is applied to 17 operational GRs. The multiradar QPE shows a significant increase in correlation coefficient (from 0.62 to 0.71), and decreases in relative mean bias (from −0.28 to −0.19) and root mean square error (from 3.96 to 3.54 mm) compared to rain gauges. These results confirm that GPM‐DPR can effectively correct the reflectivity biases of a GR network and improve multiradar QPE accuracy and consistency.

Removing temperature drift and temporal variation in thermal infrared images of a UAV uncooled thermal infrared imager

A thermal infrared (TIR) imager mounted on an unmanned aerial vehicle (UAV) has been widely used to obtain land surface temperature (LST) at very high-spatial resolutions. The TIR images are valuable for various applications, including mapping fine-scale surface evapotranspiration and monitoring crop water stress. However, UAV-borne uncooled TIR imagers generally suffer from temperature drift and temporal variation impact due to a long data acquisition period, significantly decreasing the initially acquired brightness temperature (BT) reliability and hindering subsequent applications. Here, a so-called DRAT (Digital number probability density function fitting and RAdiative Transfer simulation-based) method is proposed for simultaneously removing such temperature drift and temperature variation on the temporal scale. The DRAT is post-processing-based and only needs very few ground BT observations for calibration, thus significantly reducing dependence on auxiliary data. Test results show that the visual effect of the normalized TIR mosaics has been significantly improved, displaying more reasonable temperature distribution and good consistency with the land cover. The corrected BT has a root mean square error of 1.51 K and a mean bias error of 0.19 K by in situ data. The DRAT is valuable for obtaining high-accuracy LST and is feasible for other UAV uncooled TIR imagers with similar sensor structures and working principles. Therefore, it can significantly enhance the applicability of UAV TIR remote sensing.

Altitude Correction of GCM-Simulated Precipitation Isotopes in a Valley Topography of the Chinese Loess Plateau

Altitude is one of the important factors influencing the spatial distribution of precipitation, especially in a complex topography, and simulations of isotope-enabled climate models can be improved by altitude correlation. Here we compiled isotope observations at 12 sites in Lanzhou, and examined the relationship between isotope error and altitude in this valley in the Chinese Loess Plateau using isoGSM2 isotope simulations. Before altitude correction, the performance using the nearest four grid boxes to the target site is better than that using the nearest box; the root mean square error in δ18O using the nearest four grid boxes averagely decreases by 0.37‰ compared to that using the nearest grid boxes, and correlation coefficient increases by 0.05. The influences of altitude on precipitation isotope errors were examined, and the linear relationship between altitude error and isotope simulations was calculated. The strongest altitude isotopic gradient between δ18O mean bias error and altitude error is in summer, and the weakest is in winter. The regression relationships were used to correct the simulated isotope composition. After altitude correction, the root mean square error decreases by 1.21‰ or 0.86‰ using the nearest one or four grid boxes, respectively, and the correlation coefficient increases by 0.13 or 0.08, respectively. The differences between methods using the nearest one or four grids are also weakened, and the differences are 0.02‰ for root mean square error and −0.01 for the correlation coefficient. The altitude correction of precipitation isotopes should be considered to downscale the simulations of climate models, especially in complex topography.

Differences Evaluation among Three Global Remote Sensing SDL Products

At present, a variety of global remote sensing surface downwelling longwave radiation (SDL) products are used for atmospheric science research; however, there are few studies on the quantitative evaluation of differences among different SDL products. In order to evaluate the differences among different SDL products quantitatively, we have selected three commonly used SDL products—Clouds and the Earth’s Radiant Energy System-Synoptic Radiative Fluxes and Clouds (CERES-SYN), the European Centre for Medium Range Weather Forecasts-Surface Radiation Budget (ECMWF-SRB) and the Global Energy and Water Exchanges Project-Surface Radiation Budget (GEWEX-SRB)—to comprehensively study in this paper. The results show that there are significant differences among the three SDL products in some areas, such as in the Arctic, the Antarctic, the Sahara, the Tibet Plateau, and Greenland. The maximum absolute root mean square error (RMSEab) in these areas is greater than 20 Wm−2, the maximum relative root mean square error (RMSEre) is greater than 20%, the maximum and minimum absolute mean bias error (MBEab) are about 20 Wm−2 and −20 Wm−2, respectively, and the maximum and minimum relative mean bias error (MBEre) are about 10% and −10%, respectively. Among the three SDL products, the difference between the ECMWF-SRB and GEWEX-SRB is the most significant. In addition, this paper also analyzed the differences among different SDL products based on three aspects. Firstly, the differences among the three SDL products show that there is significant seasonality, and the differences among different months may vary greatly. However, the differences are not sensitive to years. Secondly, there are some differences in cloud-forcing radiative fluxes (CFRFs) of different SDL products, which is also an important factor affecting the difference between different SDL products. Finally, in the process of converting high temporal resolution SDL products into monthly SDL products, data processing also affects the difference between different SDL products.

Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson’s correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal.

Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources

Aerosol Optical Depth (AOD) is a critical optical parameter that quantifies the degree of light attenuation by aerosols and serves as a fundamental indicator of atmospheric quality. Therefore, accurate quantification and retrieval of AOD is crucial for relevant studies. However, current satellite-based AOD retrieval algorithms suffer from inapplicability under low-light conditions, limiting the development of nighttime AOD retrieval. Under this context, we proposed a novel algorithm, namely Simultaneous Consideration of Artificial and Natural light Sources (SCANS), to obtain nighttime AOD. The core of the SCANS algorithm is considering the synergy of both the natural and artificial light sources to obtain nighttime AOD by integrating atmospheric radiative transfer simulation into an extinction method and performing multiple iterations. SCANS was applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) and the retrieved nighttime AOD was validated with in-situ measurements from five AERONET sites. Results indicate that the Mean Bias Errors (MBEs) of the retrieved nighttime AOD range from 0.0 to 0.08 and the corresponding Root Mean Square Errors (RMSEs) range from 0.11 to 0.17, which exhibit better accuracy than that of the nighttime MERRA-2 AOD. We further compared the retrieved nighttime AOD with the corresponding Air Quality Index (AQI) measurements at six environment monitoring stations and obtained high correlation coefficients (i.e., ranging from 0.733 to 0.940), indicating SCANS's reliability and high accuracy. The proposed SCANS algorithm can effectively obtain nighttime AOD with high quality, thereby advancing research on the diurnal variation of crucial Earth's key elements.

Estimation of Surface Water Vapour Density and its Variation with other Meteorological Parameters over Akure, Nigeria

In this paper, the monthly variation of Surface Water Vapour Density (SWVD) with meteorological parameters of monthly average daily mean temperature, relative humidity, surface pressure, cloud cover and sunshine hours during the period of thirty-eight (38) years (1979 - 2016) for Akure (Latitude 7.280N, Longitude 5.300E, 375m above sea level) were investigated. The daily variation of surface water vapour density for the two distinct seasons considering two typical months in each during the period of year 2014 was examined. The results showed fluctuation in the amount of surface water vapour density in each day of the month for the period under investigation. The monthly average daily values indicated that the surface water vapour densities are greater during the rainy season than in the dry season. It was observed that the maximum average value of surface water vapour density of 20.1019 gm-3 occurred in the month of September during the rainy season and minimum value of 15.5110 gm-3 in the month of January during the dry season. The highest value of surface water vapour density was observed on the 4th of June, 2014 with 26.3320 gm-3 and the lowest on the 31st day of December, 2014 with 6.0276 gm-3. The comparison assessment of the developed two variable SWVD based models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). The developed multivariate correlation regression model that relates pressure and precipitable water vapour with R2 = 100%, MBE = -0.0177, RMSE = 0.0179, MPE = 0.1034, NSE = 99.9956% and IA = 99.9989% was found more suitable for surface water vapour density estimation with good fitting and therefore can be used for estimating surface water vapour density in Akure.

Development of In Situ Refrigeration Cycle Measurement Method Using Air-Side Data of Air Source Heat Pump

The refrigeration cycle of an air source heat pump system is an important information that reveals critical operating data, such as the cooling capacity, power consumption, and performance of a system during operation. Operating data, such as refrigerant pressure and enthalpy in situ, can be difficult to measure. Therefore, this study developed an in situ refrigeration cycle measurement method using the airside data of an air source heat pump. A method for measuring the refrigeration cycle is proposed using the characteristics of evaporation, compression, condensation, and expansion processes. The distance function was analyzed by normalizing the difference between the refrigerant pressure and enthalpy of the existing and proposed measurement methods. In addition, the distance function for the maximum error of the pressure and enthalpy measurement devices was analyzed and compared with the distance function of the entire data used in the evaluation. All the evaluation data had low distance function values with a maximum difference of 5%, confirming the reliability of the proposed refrigeration cycle measurement method. The power consumption and calculated COP were also evaluated using the proposed method. The Mean Bias Error (MBE) of power consumption and COP were 0.15% and 0.04%, and the coefficient of variation of root-mean-square error (CvRMSE) was 8.967% and 7.14%, respectively.

Virtual snow stakes: a new method for snow depth measurement at remote camera stations

AbstractRemote cameras are used to study demographics, ecological processes, and behavior of wildlife populations. Cameras have also been used to measure snow depth with physical snow stakes. However, concerns that physical instruments at camera sites may influence animal behavior limit installation of instruments to facilitate collecting such data. Given that snow depth data are inherently contained within images, potential insights that could be made using these data are lost. To facilitate camera‐based snow depth observations without additional equipment installation, we developed a method implemented in an R package called edger to superimpose virtual measurement devices onto images. The virtual snow stakes can be used to derive snow depth measurements. We validated the method for snow depth estimation using camera data from Latah County, Idaho, USA in winter 2020–2021. Mean bias error between the virtual snow stake and a physical snow stake was 5.8 cm; the mean absolute bias error was 8.8 cm. The mean Nash Sutcliffe Efficiency score comparing the fit of the 2 sets of measurements within each camera was 0.748, indicating good agreement. The edger package provides researchers with a means to take critical measurements for ecological studies without the use of physical objects that could alter animal behavior, and snow data at finer scales can complement other snow data sources that have coarser spatial and temporal resolution.

Research on Modeling Weighted Average Temperature Based on the Machine Learning Algorithms

In response to the nonlinear fitting difficulty of the traditional weighted average temperature (Tm) modeling, this paper proposed four machine learning (ML)-based Tm models. Based on the seven radiosondes in the Yangtze River Delta region from 2014 to 2019, four forecasting ML-based Tm models were constructed using Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) algorithms. The surface temperature (Ts), water vapor pressure (Es), and atmospheric pressure (Ps) were identified as crucial influencing factors after analyzing their correlations to the Tm. The ML-based Tm models were trained using seven radiosondes from 2014 to 2018. Then, the mean bias and root mean square error (RMSE) of the 2019 dataset were used to evaluate the accuracy of the ML-based Tm models. Experimental results show that the overall accuracy of the LightGBM-based Tm model is superior to the SVM, CART, and RF-based Tm models under different temporal variations. The mean RMSE of the daily LightGBM-based Tm model is reduced by 0.07 K, 0.04 K, and 0.13 K compared to the other three ML-based models, respectively. The mean RMSE of the monthly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean RMSE of the quarterly LightGBM-based Tm model is reduced by 0.09 K, 0.04 K, and 0.11 K, respectively. The mean bias of the LightGBM-based Tm model is also smaller than that of the other ML-based Tm models. Therefore, the LightGBM-based Tm model can provide more accurate Tm and is more suitable for obtaining GNSS precipitable water vapor in the Yangtze River Delta region.

Development of a new curve equation representing thin layer drying process

ABSTRACT In this study, a new model for thin-layer drying process was developed, tested, and verified by using data from the literature and compared. In this regard, the required data were collected from the experiments of green tea leaves in fixed bed and swirling flow fluidized bed infrared drying system, fixed bed, and swirling flow fluidized bed microwave drying system, apricot and kiwi in microwave drying system, and mammoth pumpkin (Cucurbita Maxima) in a laboratory scale dryer. As a result, the proposed model called “Improved Midilli-Kucuk Model” was determined to be the best model among the thin-layer drying-curve equations in the literature. Correlation coefficient (r), the coefficient of determination (R2), reduced chi-square ( χ 2 ), reduced sum square error (RSSE), and mean bias error (MBE) were calculated between 0.99583 and 1, 0.99543 and 1, 0.00273 and 0, 0.00103 and 0, and 0.00046 and 0, respectively. The highest values of r, R2, and R ‾ 2 and the lowest values of χ 2 , RMSE, RSSE, and MBE were obtained for green tea leaves drying in swirling flow fluidized bed infrared drying system at infrared power of 1000 W.

Verification of Some Weather Elements Forecasts from BSC-DREAM8b V2 Model

Abstract The aim of this study is to verify 2-meter temperature and mean sea level pressure forecasts from BSC-DREAM8B V2.0 model, which is a short-term numerical forecasting model against observed data from the Iraqi Meteorological Organization & Seismology constitution and modelled data (ERA-Interim) reanalysis from the European Centre for Medium Range Weather Forecasts (ECMWF) for the period 1-3 June 2012 for four selected stations in Iraq, they are Mosul, Baghdad, Rutba and Basra which represent northern, central, western and southern of Iraq respectively by using some statistical methods including Mean Absolute Error (MAE), Bias Error (BE) and Correlation Coefficient (R). The results showed that the BSC-DREAM8B V2.0 model worked well in representing the daily temperature variation and with relatively good compatibility with the ERA-I modelled data, however, the latter showed lower absolute error rates. The two modelled data showed the highest absolute error rate for Baghdad station whereas BSC showed lower values for Basra station and different values for Rutba and Mosul, and the predicted values were overestimated for all stations and for both models except for Rutba station where ERA-I data showed underestimated value. As for the surface pressure at mean sea level, the BSC-DREAM8B V2.0 model showed its ability to simulate the daily change of pressure and worked in a good consistency with ERA-I modelled data with few hetopascal as mean absolute error. Both modelled data showed underestimated values for all stations, and thus, BSC modelled data is better for simulating temperature and mean sea level pressure in low elevated regions compared to ERA-Interim data and vice versa.

Effectiveness of evolutionary-tuned neurofuzzy inference system in predicting wind turbine gearbox oil temperature

Monitoring the gearbox oil temperature of wind turbines has been one of the most effective ways of increasing their reliability, and reducing maintenance and operational costs. The temperature variation is affected by several factors, including meteorological variables. Unfortunately, this is less considered in the condition monitoring of wind turbine gearboxes. Also, while the neurofuzzy inference model offers a high prospect in improving the understanding of wind turbine gearbox oil temperature behaviour, tuning its hyperparameters for such an application has been less explored. Hence, this study investigates the effectiveness and efficiency of predicting wind turbine gearbox oil temperature using an adaptive neurofuzzy inference system optimized with population-based evolutionary techniques (the genetic algorithm and particle swarm optimization). Relevant meteorological variables were selected from a case study wind farm in South Africa and used as model inputs. The hyper-parameter tuning of the neurofuzzy inference system model was carried out using population-based techniques before training the models. The performances of these hybrid models were compared based on established statistical performance metrics. The genetic algorithm hybrid model outperformed the particle swarm optimization hybrid on the test dataset with a root mean square error of 3.79, mean absolute deviation of 2.93, mean absolute percentage error of 5.9, and relative mean bias error of 1.74. The genetic algorithm-based hybrid model showed higher accuracy and reliability for wind turbine gearbox oil temperature prediction in the case study wind farm and offers potential use for neighbouring wind farms in the same renewable energy development zone.

Soil Temperature, Organic-Carbon Storage, and Water-Holding Ability Should Be Accounted for the Empirical Soil Respiration Model Selection in Two Forest Ecosystems

Soil respiration (SR) is a main component of the carbon cycle in terrestrial ecosystems, and being strongly affected by changes in the environment, it is a good indicator of the ecosystem’s ability to cope with climate change. This research aims to find better empirical SR models using 25-year-long SR monitoring in two forest ecosystems formed on sandy Entic Podzol and loamy Haplic Luvisol. The following parameters were considered in the examined models: the mean monthly soil or air temperatures (Tsoil or Tair), the amount of precipitation during the current (P) and the previous (PP) months, and the storage of soil organic carbon (SOC). The weighted non-linear regression was used for model parameter estimations for the normal, wet, and dry years. To improve the model resolutions by magnitude, we controlled the slope and intercept of the linear model comparison between the measured and modeled data through the change in R0—SR at zero soil temperature. The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R2) were used for the estimation of the goodness of model performances. For the sandy Entic Podzol, it is more appropriate to use the models dependent on SOC (TPPC). While for the loamy Haplic Luvisol, the Raich–Hashimoto model (TPPrh) with the quadratic Tsoil or Tair dependency shows the better results. An application of Tsoil for the model parameterization gives better results than Tair: the TPPC model was able to adequately describe the cold-period SR (Tsoil ≤ 2 °C); the TPPrh model was able to avoid overestimations of the warm-period SR (Tsoil > 2 °C). The TPPC model parameterized with Tsoil can be used for the quality control of the cold-period SR measurements. Therefore, we showed the importance of accounting for SOC and the water-holding ability when the optimal SR model is chosen for the analysis.