Mean Bias Error Research Articles

Comparative study of solar flux using different empirical models at low land urban industrial zone of Biratnagar Nepal


 
 
 
 Nepal is a developing country where sufficient clean energy is not available in our daily needs for cooking, lighting and other developmental activities. So, it is high time to promote clean and green energy, which is freely available in every corner of the country. In this regard, the present study helps to estimate the global solar radiation at the lowland region Biratnagar (lat. 26◦28′53′′N, long. 87◦15′50′′E and Alt. 72 m) using measured global solar radiation (GSR) and sunshine hours on empirical models. This paper uses the regression technique on two different empirical models Angstrom-Prescott(A-P) and Tiwari and Sangeeta, to estimate solar radiation. After analyzing the data on empirical models, the empirical constants 0.25 and 0.23 and 0.13 and 0.24 are found in A-P and Tiwari and Sangeeta models, respectively. The performance of the models was carried out by employing mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE) and adjusted coefficient of determination (R2). These statistical tools reveal that all these models are statistically significant. Such a study is relevant when reliable data for solar radiation is not adequately available. The findings of empirical coefficients can be utilized for predicting solar radiation and solar energy at similar geographical locations in Nepal.
 
 
 

Validation of IES-VE for Assessing Daylight Performance of Building Implementing Horizontal Light Pipe and Shading Systems in the Tropics

Abstract. Various daylight simulation tools, which are rapidly developed, become a reliable way for simulating the complex daylighting environment. Empirical validation of the daylight simulation tool is essential in determining its reliability, especially in simulating light transport and shading system in the Tropics. Distinct from previous research, the validation involves Horizontal Light Pipe (HLP), a side window with shading systems in different room aspect ratios and orientations. This study aims to validate the simulation results of Integrated Environmental Solutions-Virtual Environment (IES-VE) Radiance IES with the measurement results of physical scaled models for evaluating HLP, light shelves, and blinds' daylight performance under intermediate and overcast sky conditions. Two physical scaled models 1:10 represent office rooms with HLP and shading systems with different room aspect ratios were constructed. Daylight Factor (DF) and Daylight Ratio (DF) of physical scaled model measurement and IES-VE simulation were compared. The results showed that under intermediate and overcast sky conditions, the Pearson correlation between simulation and measurement results using DR and DF was strong, significant, and positive, as high as 0.84 and 0.80, respectively. The Mean Bias Error between simulation and measurement results under intermediate and overcast sky conditions were -12% and -7.7%, respectively. IES-VE is reliable to evaluate the HLP and shading systems' daylight performance with different room orientations in the Tropics.

Modeling the dynamic changes in Plasmopara viticola sporangia concentration based on LSTM and understanding the impact of relative factor variability.

Reliable disease management can guarantee healthy plant production and relies on the knowledge of pathogen prevalence. Modeling the dynamic changes in spore concentration is available for realizing this purpose. We present a novel model based on a time-series modeling machine learning method, i.e., a long short-term memory (LSTM) network, to analyze oomycete Plasmopara viticola sporangia concentration dynamics using data from a 4-year field experiment trial in North China. Principal component analysis (PCA)-based high-quality input screening and simulation result calibration were performed to ensure model performance, obtaining a high determination coefficient (0.99), a low root mean square error (0.87), and a low mean bias error (0.55), high sensitivity (91.5%), and high specificity (96.5%). The impact of the variability of relative factors on daily P. viticola sporangia concentrations was analyzed, confirming that a low daily mean air temperature restricts pathogen development even during a long period of high humidity in the field.

Generation of High Temporal Resolution Full-Coverage Aerosol Optical Depth Based on Remote Sensing and Reanalysis Data

Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can be affected by abnormal weather conditions and high reflectance surfaces, leading to gaps in spatial coverage. To address this issue, we propose a satellite-based AOD filling method based on hourly level-3 Himawari-8 AOD products. In this study, the optimal model with a mean bias error (MBE) less than 0.01 and a root-mean-square error (RMSE) less than 0.1 in most land cover types was selected to generate the full-coverage AOD. The generated full-coverage AOD was validated against in situ measurements from the AERONET sites and compared with the performance of Himawari-8 AOD and MERRA-2 AOD over the AERONET sites. The validation results indicate that the accuracy of full-coverage AOD is comparable to that of the Advanced Himawari Imager (AHI) AOD, and for other land cover types (excluding barren land), the accuracy of full-coverage AOD is superior to that of MERRA-2 AOD. To investigate the practical application of full-coverage AOD, we utilized it as an input parameter to perform radiative transfer simulations in northwestern and southern China. The validation results showed that the simulated at-sensor radiance based on full-coverage AOD was in good agreement with the at-sensor radiance observations from MODIS. These results indicate that complete and accurate measurements of AOD have considerable potential for application in the simulation of at-sensor radiance and other related topics.

Enhancing the efficiency of polytetrafluoroethylene-modified silica hydrosols coated solar panels by using artificial neural network and response surface methodology

Abstract In this article, an attempt was made to improve the efficiency of coated solar panels by using artificial neural networks (ANNs) and response surface methodology (RSM). Using the spray coating technique, the glass surface of the photovoltaic solar panel was coated with silicon dioxide nanoparticles incorporated with polytetrafluoroethylene-modified silica sols. Multilayer perceptron with feed-forward back-propagation algorithm was used to develop ANN models for improving the efficiency of the coated solar panels. Out of the 200 sets of data collected, 75% were used for training and 25% were used for testing. On evaluating the models using performance indicators, a four-input technological parameter model (silicon dioxide nanoparticle quantity, coating thickness, surface temperature and solar insolation) with eight neurons in a single hidden layer combination was observed to be the best. The prediction accuracy indicator values of the ANN model were 0.9612 for the coefficient of determination, 0.1971 for the mean absolute percentage error, 0.2317 for the relative root mean square error and 0.00741 for the mean bias error. Using a central composite design model, empirical relationships were developed between input and output responses. The significance of the developed model was ascertained by using analysis of variance, up to a 95% confidence level. For optimization, the RSM was used, and a high efficiency of 17.1% was predicted for the coated solar panel with optimized factors; it was validated to a very high level of predictability. Using interaction and perturbation plots, a ranking of the parameters was done.

Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes

In this study, various machine learning algorithms, including the minimax probability machine regression (MPMR), functional network (FN), convolutional neural network (CNN), recurrent neural network (RNN), and group method of data handling (GMDH) models, are proposed for the estimation of the seismic bearing capacity factor (Nc) of strip footings on sloping ground under seismic events. To train and test the proposed machine learning model, a total of 1296 samples were numerically obtained by performing a lower-bound (LB) and upper-bound (UB) finite element limit analysis (FELA) to evaluate the seismic bearing capacity factor (Nc) of strip footings. Sensitivity analysis was performed on all dimensionless input parameters (i.e., slope inclination (β); normalized depth (D/B); normalized distance (L/B); normalized slope height (H/B); the strength ratio (cu/γB); and the horizontal seismic acceleration (kh)) to determine the influence on the dimensionless output parameters (i.e., the seismic bearing capacity factor (Nc)). To assess the performance of the proposed models, various performance parameters—namely the coefficient of determination (R2), variance account factor (VAF), performance index (PI), Willmott’s index of agreement (WI), the mean absolute error (MAE), the weighted mean absolute percentage error (WMAPE), the mean bias error (MBE), and the root-mean-square error (RMSE)—were calculated. The predictive performance of all proposed models for a bearing capacity factor (Nc) prediction was compared by using the testing dataset, and it was found that the MPMR model achieved the highest R2 values of 1.000 and 0.957 and the lowest RMSE values of 0.000 and 0.038 in both the training and testing phases, respectively. The parametric analyses, rank analyses, REC curves, and the AIC showed that the proposed models were quite effective and reliable for the estimation of the bearing capacity factor (Nc).

Machine Learning Algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile

The world’s water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources.

Evaluation of SEBS and SEBAL algorithms for estimating wheat evapotranspiration (case study: central areas of Khuzestan province)

This study aimed to accurately estimate daily wheat evapotranspiration using two remote sensing algorithms, Surface Energy Balance System (SEBS) and Surface Energy Balance Algorithm for Land (SEBAL), in central Khuzestan province during 2019–2020. The results of two algorithms were compared with lysimeter (as a direct method), FAO-Penman–Monteith (FAO-PM), two temperature-based methods (Hargreaves-Samani and Blaney-Criddle), two radiation-based methods (Priestley–Taylor and Doorenbos–Pruitt), and two mass transfer-based methods (Mahringer and World Meteorology Organization) (as indirect methods). Coefficient of Determination (R2), Root-Mean-Square Error (RMSE), Percentage of Bias (PBIAS), Mean Bias Error, Mean Absolute Percentage Error, and Nash–Sutcliffe indicators used for comparing the results. According to the results, both SEBAL and SEBS algorithms showed the highest compatibility with lysimeter data (R2 = 0.92 and 0.96, RMSE = 2.15 and 1.53 mm/day, respectively). Comparing both algorithms with the FAO-PM method, resulted in RMSE and R2 of 2.42 mm/day and 0.87 for SEBS and 3.14 mm/day and 0.79 for SEBAL. The Hargreaves-Samani method (R2 = 0.72, RMSE = 16.4 mm/day) and (R2 = 0.8, RMSE = 10.4 mm/day) among temperature-based methods, Doorenbos–Pruitt (R2 = 0.71, RMSE = 3.33 mm/day) and (R2 = 0.79, RMSE = 2.63 mm/day) among radiation-based methods, and the Mahringer method (R2 = 0.6, RMSE = 6.8 mm/day mm/day) and (R2 = 0.68, RMSE = 5.51 mm/day) among mass transfer-based methods yielded better estimations than SEBAL and SEBS algorithms, respectively. Owing to the high accuracy of SEBAL and SEBS algorithms, in estimating the amount of evapotranspiration in the study area and close to the actual values in the field, using energy balance algorithms is recommended in Khuzestan province.

Multi-region electricity demand prediction with ensemble deep neural networks.

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.

Sunshine and Temperature Based Models for Estimating Global Solar Radiation in Maiduguri, Nigeria

Nine existing sunshine based models and three existing temperature based models were evaluated and compared to ascertain the most suitable models for estimating global solar radiation in Maiduguri; the most suitable sunshine based and most suitable temperature based models were also compared. The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one years (1980 – 2010) was utilized and the evaluated models were tested statistically using validation indicators of coefficient of determination, Mean Bias Error, Root Mean Square Error, Mean Percentage Error, t – test, Nash – Sutcliffe Equation and Index of Agreement. The results indicated that the linear exponential sunshine based model and the logarithmic temperature based model were found more accurate for global solar radiation estimation in Maiduguri as compared to other evaluated models. Furthermore, the logarithmic temperature based model was found more accurate for estimating global solar radiation as compared to the linear exponential sunshine based model, and this was testified from the figure showing the comparison between the recommended sunshine based and temperature based models in which the recommended temperature based model depicts the best fitting with the measured global solar radiation data.

A thermal radiation directionality correction method for the surface upward longwave radiation of geostationary satellite based on a time-evolving kernel-driven model

Thermal radiation directionality (TRD) characterizes the anisotropic signature of most surface targets in the thermal infrared domain. It causes significant uncertainties in estimating surface upward longwave radiation (SULR) from space observations. In this regard, kernel-driven models (KDMs) are suitable to remove TRD effects from remote sensing dataset as they are computationally efficient. However, KDMs requires simultaneous multi-angle observations as inputs to be well calibrated, which yields a difficulty with geostationary satellites as they can only provide a single-angle observation. To overcome this issue, we proposed a six-parameter time-evolving KDM that combines a four-parameter SULR diurnal variation model and a two-parameter TRD amplitude model to correct the TRD effect for single-angle estimated SULR dataset of geostationary satellites. The significant daytime TRD effect when solar zenith angle is within 60° can be effectively eliminated. The modeling accuracy of the time-evolving KDM is evaluated using a simulated SULR dataset generated by the 3D Discrete Anisotropic Radiative Transfer (DART) model; the TRD correction method based on the new time-evolving KDM is validated using a two-year single-angle estimated SULR dataset derived from data of the Advanced Baseline Imager (ABI) onboard Geostationary Operational Environmental Satellite-16 (GOES-16) against in situ measurements at 20 AmeriFlux sites. Results show that the proposed time-evolving KDM has a high accuracy with an R2 > 0.999 and a small RMSE = 1.5 W/m2; the TRD correction method based on the time-evolving KDM can greatly reduce the GOES-16 SULR uncertainty caused by the TRD effect with an RMSE decrease of 4.5 W/m2 (22.1%) and mean bias error decrease of 7.9 W/m2 (62.7%). Hence, the proposed TRD correction method is practically efficient for the operational TRD correction of SULR products generated from the geostationary satellites (e.g., GOES-16, FY-4A, Himawari-8, MSG).

Performance evaluation of procedures used to correct measured I‐V characteristics of photovoltaic modules for temperature and irradiance

AbstractIEC 60891 ed.3 published in 2021 has defined four standard I‐V characteristics correction procedures numbered 1 through 4. The aim of this work is to evaluate these four I‐V translation methods. The results show that correction procedure 1 (CP1) and 2 (CP2) work well over a broad range of irradiances and temperatures. However, CP1 requires I‐V curves being measured adequately down to negative current regime at low‐irradiance levels and CP2 is not so suitable for low shunt‐resistance modules; both also require a set of correction parameters. Based on our performance analysis, a new method based on CP2 is introduced to improve the correction performance for low shunt‐resistance modules; the mean bias error (MBE) value of maximum power (PMAX) improved from −10.26% to −1.32%. Correction procedure 3 (CP3) employs a drastically different correction procedure as compared with the other CPs. This work shows that CP3 works well over a broad range of irradiances and temperatures, but significant distortion of the corrected I‐V curves may occur when extrapolation is required. Correction procedure 4 (CP4) requires only a single I‐V curve and shows generally good results in short‐circuit current (ISC) but worse agreement in open‐circuit voltage (VOC) and PMAX.

Intercomparison of the parameterized Linke turbidity factor in deriving global horizontal irradiance

This study presents a comparative assessment of nine parameterized methods to estimate Linke turbidity (TL) in the five Köppen–Geiger climate zones. The evaluation is intensified by simulating all TL values into a global horizontal irradiance (GHI) model, called the Hammer model. The results found that atmospheric turbidity on the Korean Peninsula varies per season. The atmosphere tends to be more transparent in fall and winter, with lower turbidity. Meanwhile, the turbidity consistently rises in spring and peaks and summer, indicating a significant decline in atmospheric transparency.All nine TL methods generated varied degrees of accuracy according to their Köppen–Geiger climate zones. In terms of relative root mean square error (rRMSE) and relative mean bias error (rMBE), TLValko1, TLValko2, and TLCapdrou consistently perform poorly. Further, these three methods can only classify the atmospheric turbidity into two types: least and fully turbid. This study recommends the following methods for evaluating atmospheric turbidity in various climate types: TLRemund, TLDogniaux1, TLDogniaux2, TLIneichen, TLMolineaux, and TLGreiner. This study concludes that none of the six methods can be considered “the most superior” due to minor differences in rRMSE and rMBE values.

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0.994 and minimum value of RMSE = 0.109, MAE = 0.041, MBE = −0.010 and RE = −0.1%; combination 2; NSE = 0.941, d = 0.984, KGE = 0. 923, PCC(r) = 0. 973, and R2 = 0. 947 and minimum value of RMSE = 0. 331, MAE = 0.143, MBE = −0.089 and RE = −0.9%) performed superior to the GRG, LR, LR-RSS, LR-SVM, and LR-M5P models in all input combinations during the testing period. It was also noticed that the performance of the alone LR and its hybrid models (i.e., LR-RSS, LR-REPTree, LR-SVM, and LR-M5P) was better than the conventional stage-discharge rating curve, including the GRG method.

Assessment of turbulence intensity estimates from floating lidar systems

In the pre-construction phase of offshore wind energy projects floating lidar systems (FLS) are used for wind site assessment. Their measurements of mean wind speed and direction have proven to be accurate according to existing standards. But for the assessment of turbulence intensity (TI), a widely-accepted procedure and acceptance criteria are missing. This study investigates different evaluation methods, namely linear regression analysis and the key performance indicators (KPIs) presented by the Consortium for Advancing Remote Sensing (CFARS). The assessment is based on data from an offshore trial of the Fugro SEAWATCH Wind Lidar Buoy (SWLB) against the meteorological mast and a collocated fixed profiling wind lidar at Blyth, UK. The results show that the accuracy of TI estimates from FLS can be evaluated well, when the KPIs mean bias error (MBE) and representative TI error suggested by CFARS are used. We propose best-practice acceptance thresholds of ±1% for MBE and ±1.5% for the representative TI error. The motion-compensation approach of the SWLB works well and after compensation, its TI data are similar to the data from the fixed reference lidar. The impact of lidar-specific effects is minor in the analyzed data set and near-to-zero biases are found for all measurement elevations and wind speed bins in comparison to mast data.

Rapid Estimation of Sulfur Content in High-Ash Indian Coal Using Mid-Infrared FTIR Data

High-ash Indian coals are primarily used as thermal coal in power plants and industries. Due to the presence of sulfur in thermal coal, flue gas is a major environmental concern. Conventional methods (Ultimate Analysis of Coal) for sulfur content estimation are time-consuming, relatively costly, and destructive. In this study, Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising alternative method for the rapid and nondestructive analysis of the sulfur content in coal. In the present study, the actual sulfur content in the coal samples was determined using Ultimate Analysis (CHNS analyzer). In contrast, mid-infrared FTIR spectroscopic data (4000–400 cm−1) were used to analyze the functional groups related to sulfur or its compounds in the coal samples to predict the sulfur content. A comparison of sulfur estimated using a CHNS analyzer and predicted using mid-infrared spectroscopy (FTIR) data shows that it can accurately predict sulfur content in high-ash Indian coals using the piecewise linear regression method (Quasi-Newton, QN). The proposed FTIR-based sulfur prediction model showed a coefficient of determination (R2) of up to 0.93, where the total no. of samples (Coal + KBr pellets, n) was 126 (using 17:1 split, K-fold cross validation). The root-mean-square error (RMSE, wt.%) is 0.0035, mean bias error (MBE, wt.%) is −0.0003, MBE (%) is 3.31% and mean absolute error (MAE, wt.%) is 0.0020. The two-tailed t-test and F-test for mean and variance indicated no significant difference between the pair of values of observed sulfur (SCHNS, wt.%) using CHNS data and the model predicted sulfur (SFTIR, wt.%) using FTIR data. The prediction model using mid-infrared FTIR spectroscopy data and the Quasi-Newton method with a breakpoint and loss function performs well for coal samples from the Johilla Coalfield, Umaria. Thus, it can be a valuable tool for analyzing sulfur in other ash-rich coals from various basins worldwide.

A practical method for angular normalization of global MODIS land surface temperature over vegetated surfaces

Land surface temperature (LST) is an essential physical quantity in surface energy balance and a good indicator of exchange of heat and water between the land and atmosphere at regional and global scales. However, the thermal-infrared remotely sensed LST from large swath-width polar-orbiting and geostationary satellite sensors is usually subject to the angular anisotropy, bringing great uncertainties in the application of LST products in many fields such as evapotranspiration estimation, urban thermal environment monitoring, and climate change study. In this study, a practical angular normalization method was developed for the first time for correcting global MODIS off-nadir LST to nadir over vegetated surfaces, using MODIS (Terra + Aqua) products from 2000 to 2020, simultaneous and collocated observations at 213 sites worldwide from the AmeriFlux, FLUXNET, and National Tibetan Plateau/Third Pole Environment Data Center (TPDC), and ERA5-Land reanalysis datasets. Results showed that the angular normalization method could well correct the angular anisotropy of MODIS LST products. Validated against datasets at 213 sites, the angular normalization model showed root mean square errors (RMSE) of 1.57 K, mean bias errors (MBE) of 0 K, and coefficient of determination (R2) of 0.99. In the cross-validation of nadir LST by correcting global MODIS/Terra off-nadir LST products against Sentinel-3A nadir LST products over four typical days in 2020 (February 26, June 1, August 12 and November 16), the angular normalization model presented the RMSE of 2.26 K and the MBE of −1.20 K, in contrast to the RMSE of 3.01 K and the MBE of −2.15 K when MODIS off-nadir LST was compared to the Sentinel-3A nadir LST. The angular anisotropy of global MODIS/Terra LST over vegetated surfaces for the four days of interest ranged between −1.79 to −1.30 K (1% quantile) and 3.64 to 4.37 K (99% quantile), with a median of 0.33 to 0.74 K, and a mean of 0.46 to 0.89 K. The developed angular normalization model is a promising approach to operationally correcting all time-series of global MODIS/Terra and MODIS/Aqua LST products with high accuracies, which is significant to provide a benchmark of angle-consistent MODIS LST product for its further application.

Assessment of a New Solar Radiation Nowcasting Method Based on FY-4A Satellite Imagery, the McClear Model and SHapley Additive exPlanations (SHAP)

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R2 values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations.

AquaCrop Model Performance in Yield, Biomass, and Water Requirement Simulations of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

The application of crop growth simulation and water management models will become increasingly important in the future. They can be used to predict yield reductions due to water scarcity and allocate water to ensure profitable crop production. The objective of this research was to calibrate the AquaCrop model for common bean (Faseolus vulgaris L.) grown in temperate climates and to test whether the model can be used for different irrigation strategies to achieve high yield productivity. The model was calibrated using data obtained from two years of experimental research in the Serbian territory of the Syrmia region. There were three sowing periods/plots: I—mid April, II—end of May/beginning of June, and III—third decade of June/beginning of July; and three levels of irrigation/subplots: full irrigation (F) providing 100% of crop evapotranspiration (ETc), mild deficit irrigation (R) at 80% of ETc, and moderate deficit irrigation (S) at 60% of ETc. The results show that the AquaCrop model accurately predicts common bean yield, biomass, canopy cover, and water requirements. The statistical indices of the calibrated dataset, coefficient of determination (R2), normalized root mean square error (NRMSE), mean bias error (MBE), and Willmott agreement index (d) for yield and biomass were: 0.91, 0.99; 6.9%, 11.4%; −0.046, 1.186 and 0.9, 0.89, respectively. When testing three irrigation strategies, the model accurately predicted irrigation requirements for the full and two deficit irrigation strategies, with only 29 mm, 32 mm, and 34 mm more water than was applied for the Fs, Rs, and Ss irrigation strategy, respectively. The AquaCrop model performed well in predicting irrigated yield and can be used to estimate the yield of common bean for different sowing periods and irrigation strategies.

Implementation of hybrid wind speed prediction model based on different data mining and signal processing approaches

Accurate estimation of wind speed (WS) data, which greatly influences meteorological parameters, plays a vital role in the safe operation and optimization of the power system and water resource management. The study's main aim is to combine artificial intelligence and signal decomposition techniques to improve WS prediction accuracy. Feed-forward back propagation neural network (FFBNN), support vector machine (SVM) and Gaussian processes regression (GPR) models, discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were used to forecast the WS values ​​1 month ahead in Burdur meteorology station. Statistical criteria such as Willmott's index of agreement, mean bias error, mean squared error, determination coefficient, Taylor diagram and regression analysis, and graphical indicators were used to evaluate the prediction success of the models. As a result of the study, it was determined that both wavelet transform and EMD signal processing increased the WS prediction performance of the stand-alone ML model. The best performance was obtained with the hybrid EMD-Matern 5/2 kernel GPR with test (R2:0.802) and validation (R2:0.606). The most successful model structure was obtained using input variables with a delay of up to 3 months. The study's results contribute to wind energy-related institutions in terms of practical use, planning, and management of wind energy.