Mean Bias Error Research Articles

Calibration of simulation model to analyze hospital building energy performance

This research paper addresses the calibration of an energy simulation model for a naturally ventilated hospital building located in Mangalore, Karnataka, India, in a warm, humid climatic zone. The study aims to enhance the accuracy of energy consumption calculations through a calibration process by developing the energy model of a reference hospital building using EnergyPlus software. The required architectural design data, construction details, occupancy and schedule data and services are collected through documents, energy audits and documentation, measured drawings, and semi-structured interviews. Calibration parameters are systematically identified and adjusted through an iterative process. The monthly electricity bill is used to validate the simulation model. The simulation model reached during the validation process has an excellent Coefficient of Variation-Root Mean Squared Error of 4.24% and a Normalized Mean Bias Error of −4.42%, both of which meet the ASHRAE-approved accuracy standards. The paper also discusses the challenges and emphasizes the need for a nuanced approach to modeling healthcare facilities. Notwithstanding the challenges faced, the study offers insightful information about how to calibrate simulation models for hospital energy usage with the significant influence of scheduling for artificial lighting and equipment usage. By pioneering a manual calibration approach tailored to hospital simulation models in the warm humid climate of India, this paper offers a novel and practical solution to address the challenges of energy performance analysis in resource-constrained environments. The calibrated simulation model presented in this study is a valuable tool for assessing and improving the energy performance of naturally ventilated healthcare facilities. By creating alternative space layouts, the research findings aim to foster the development of hospital infrastructure that is both ecologically friendly and energy-efficient in a specific climatic context. This research is assured to make a substantial contribution to the advancement of sustainable hospital design in warm-humid climates, with implications for both academia and industry.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3−), Magnesium (Mg2+), Sulphate (SO42−), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Maximizing Building Energy Efficiency Through Zone-Based Conditions: An Advanced Dynamic Simulation Approach

Abstract The primary objective of this research is to maximize the building energy efficiency by identifying the zone-based conditions and employing an advanced energy simulation model. It is possible to achieve this by reducing periods of excessive heating and cooling by utilizing a calibrated energy model with 15-minute measured data. Further, by regulating the energy consumption of various functional zones corresponding to the work schedules, building's energy system timetables, and the sensible temperature comfort requirements, additional energy savings can be achieved. To show the effect of dynamic simulation on building energy consumption, periodic 15-min temperature data were collected in different zones to be used both in the calibration of the energy model and in the improvement of the current energy profile. Mean bias error and cumulative variation of the root mean squared error were chosen as a performance indicator. Utilizing measured data, over-heated and over-cooled periods were defined by manually identifying zone-based indoor comfort conditions to predict improvements in overall building energy performance. The potential energy savings that can be achieved by largely eliminating over-heating periods are calculated. In addition, energy needs of different zones were considered and zone-based scheduling and zone-based comfort conditions were applied with the implementation of demand-side management. As a result, 17% energy efficiency can be achieved with an automated heating system that controls the indoor temperatures and ensures that the temperature is always kept at the desired level. As a further improvement, 32% energy efficiency can be achieved by applying zone-based scheduling and comfort conditions.

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a <scp>snow‐dominated</scp> region: An evaluation of <scp>EEMD</scp>, <scp>VMD</scp> and <scp>EWT</scp> decomposition techniques

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

OPTIMISING SPRINT PERFORMANCE: CONCURRENT VALIDITY AND RELIABILITY OF 25HZ GNSS UNITS FOR SPEED PROFILING

Sprint performance profiling is crucial for understanding an athlete’s capabilities and identifying potential areas for improvement. While traditional gold standard systems provide valid and reliable measurements, they are often costly, laboratory-based, or impractical for field-based settings. New GNSS units (25Hz) with higher sampling rates may address some of these limitations. The purpose of the project was to evaluate the concurrent validity and inter-unit reliability of 25Hz GNSS units by examining their agreement with laser devices for velocity measurements and timing gates for interval times. Validity was assessed during a track and field training session. 30 participants performed between 3 and 6 sprints over 40m whilst being assessed simultaneously through all systems. Inter-unit reliability was assessed on a closed athletics track by placing 3 GNSS units on a motor vehicle and completing 60 accelerations from 0-60m. Low mean bias (&amp;lt;1%) and typical error less than &amp;lt;2% for all measurements demonstrate excellent agreement between GNSS and criterion devices. The units demonstrated good reliability for the 0-10m interval (ICC = 0.86), excellent reliability for all remaining 10m intervals to 60m (ICC = 0.91-0.99), full times 0-60m (ICC = 0.97) and maximal sprint velocity (ICC = 1.0). These GNSS units offer a more efficient and practical alternative to traditional measurement devices. For coaches, this technology offers a scalable method to concurrently assess the sprint performances of multiple athletes during training and competitions, enabling evidence-based decisions to guide athletic development.

EVALUATION OF NEW DEVELOPED MODELS FOR ESTIMATION OF DIFFUSE SOLAR RADIATION OVER IKEJA, LAGOS STATE, NIGERIA

This study investigates the estimation of diffuse solar radiation in Ikeja, Nigeria (6.58°N, 3.33°E, 39.40m above sea level). Employing a 22-year dataset (2001-2022) of monthly average climatic data from NASA website, nineteen new models were developed to predict diffuse solar radiation. These models incorporated various meteorological parameters including global solar radiation, wind speed, temperature, pressure, and relative humidity. A categorized approach was used, with models falling into five groups: modified Page, Liu and Jordan; clearness index with one additional variable; two-variable; three-variable; and a four-variable model. Evaluation using five statistical tests tools of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and Coefficient of determination (R²) identified Equation 28h (the model relating diffuse radiation to clearness index and temperature) as the most suitable model out of all the models in each of the five categories for estimation of diffuse solar radiation for Ikeja. Hence, emphasizing the importance of considering both atmospheric clarity and temperature for accurate diffuse solar radiation estimation in this coastal location.

Application of Bayesian models in building information modeling of residential buildings

This paper lays forth a process for continuing Bayesian calibration of full-scale building energy simulation (BES) models by making use of data obtained from building information modeling (BIM) and building energy management systems (BEMS). According to a survey taken in China, it is possible that importing data from BIM and BEMS will dramatically cut down on the amount of time and effort required for the continuing calibration of BES models. After that, the continuous calibration approach that had been developed was examined with the use of a case study that was based on the actual calibration of a building. In China, Building Information Modeling (BIM) and data on the amount of electricity used each month over the course of the preceding three years were both incorporated into the case study. According to the findings, a non-continuous technique on the test dataset fared worse than a continuous Bayesian calibration method in terms of prediction accuracy and reduced uncertainty. The normalized mean biased error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) are both discussed in this research, and comparisons are drawn between the two.

Intelligent prediction model of a polymer fracture grouting effect based on a genetic algorithm-optimized back propagation neural network

Polymer grouting can effectively improve the stability of surrounding rock fractures. However, in practical construction, it is difficult to judge the degree of coupling between the slurry and the rock, and the effective grouting range after grouting. Therefore, early prediction of the effect of grouting on the surrounding rock is crucial. In this paper, a new artificial intelligence method is proposed to predict the polymer fracture grouting effect. The genetic algorithm optimized back propagation neural network (GA-BP) is employed to construct an intelligent prediction model. To acquire a substantial dataset for constructing the model, an easily assembled/disassembled test apparatus for polymer fracture grouting is designed. The maximum coupling degree of the fractures and slurry diffusion distance are chosen as the evaluation metrics for the grouting effectiveness. The influences of the fracture characteristic parameters and grouting volume on the grouting effect are investigated. Furthermore, a comprehensive analysis is conducted on the spatiotemporal diffusion characteristics and slurry-rock coupling mechanism of polymer grouting. Compared to traditional BP neural networks, and three other machine learning algorithms (decision trees, random forests and gradient boosting decision trees), the GA-BP model outperforms them in terms of R2 (coefficient of determination), MSE (mean squared error), MBE (mean bias error), MAE (mean absolute error) and RMSE (root mean squared error) in both the test and training sets. The GA algorithm significantly improves the accuracy and robustness of the prediction model. The optimized model demonstrates significant accuracy in predicting grouting results and assessing efficiency, providing a practical reference for grouting construction.

New Models for Estimation of Diffuse Solar Radiation for Warri, Nigeria

This study utilized a 22-year dataset (2001-2022) comprising monthly average daily global solar radiation, diffuse solar radiation, relative humidity, atmospheric pressure, wind speed, and mean temperature for Warri (Latitude 5.52 oN, Longitude 5.73 oE and 6.10 m above sea level). Nineteen models, categorized into modified Page, Liu, and Jordan models; clearness index and one variable models; two variable models; three variable models; and four variable models, were developed and statistically tested using the following validation indices of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and coefficient of determination (R2). Among the models, the linear page model from the modified Page, Liu, and Jordan category, the model that relates the diffuse solar radiation with clearness index and mean temperature for clearness index and one variable models, the model that relates wind speed and atmospheric pressure for two variable models, and the model that relates wind speed, atmospheric pressure and relative humidity for three variable models were found more appropriate. Upon ranking the best-performing models from each category, the model that relates that diffuse solar radiation with clearness index and mean temperature performed best as the most suitable model for estimating diffuse solar radiation in Warri.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction.

Numerical-experimental study of the thermal behavior of a green facade in a warm climate in Mexico

Demographic growth entails an increase in the construction of buildings and streets, whose materials significantly absorb heat and, upon release, create discomfort for the inhabitants of large urban areas. Vegetation emerges as a crucial element to enhance both the indoor environment of buildings and the outdoor surroundings. Green facades present themselves as a highly efficient alternative to integrate vegetation in densely urbanized areas. This study focuses on evaluating the thermal performance of an indirect-type green facade system in a warm climate in Mexico. The research encompasses experimental measurements and numerical simulations of the thermal effect of the green facade and also a parametric study of the vertical leaf area index (LAIV) and air gap size. In the experimental test, temperatures were recorded in two identical test cells, one of which was equipped with a green facade with Pyrostegia Venusta plants on one of its surfaces. The numerical study validated the results obtained through experimental tests, using simulations of Computational Fluid Dynamics (CFD) considering a typical day for each season throughout the year. Validation results revealed Mean Percentage Errors (MPE) of −2.86 % and 0.25 %, Mean Bias Errors (MBE) of −0.49 °C and 0.08 °C, and Root Mean Square Errors (RMSE) of 1.34 °C and 1.29 °C for the bare facade and the green facade, respectively. In the numerical analysis, it was observed that the green facade succeeded in reducing the indoor air temperature in a range of 4.57 to 5.64 °C, while decreasing the heat flux in an interval of 7.84 to 16.79 W/m2. Moreover, a parametric study revealed that the LAIV ceases to be a significant parameter beyond 2.5, while the air gap size proves to be a considerably less influential factor compared to the LAIV. These results confirm the relevance of using vegetation in buildings to mitigate the effects of heat and improve the thermal comfort of inhabitants.

Predicting a Higher Heating Value for Torrefied Kesambi Leaf Biobriquettes through Ultimate Analysis

The escalating global pursuit of sustainable energy solutions has led to the emergence of biomass-derived fuels, such as biobriquettes, as feasible substitutes for traditional fossil fuels. Kesambi leaves, which are abundant in Southeast Asia and boast a high calorific value, represent a promising prospect for the production of biobriquettes. In this investigation, a conclusive analytical method is employed to construct a predictive framework for estimating the Higher Heating Value (HHV) of torrefied kesambi leaf biobriquettes. By incorporating ash content (PS), volatile matter (BR), carbon (C), hydrogen (H), and oxygen (O) percentages, alongside experimental HHV data, through multiple linear regression and elemental composition data acquired from proximal analysis, the model aims to forecast HHV. The model’s modest positive Mean Bias Error (MBE) and satisfactory Root Mean Square Error (RMSE) suggest a good fit. The substantial R-squared value indicates the model’s capability to adeptly capture HHV variability. Ultimately, this approach grounded in fundamental principles contributes significantly to the sustainable exploitation of biomass resources by providing a pragmatic and effective technique for predicting HHV for kesambi leaf biobriquettes.

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

Comparative study and validation of a new analytical method for hydraulic modelling of bidirectional low temperature networks

Bidirectional Low Temperature Networks (BLTNs) are an innovative technology of district heating and cooling systems. Because of their intrinsic feature of supplying simultaneous heating and cooling either directly from an energy source/sink or through distributed heat pumps and chillers, circulation pumps installed at each building extract water from the network and a distinct pressure cone is subsequently created. Variations in the amount (and direction) of the extracted water call for accurate hydraulic design and analysis to ensure safe and adequate network operation. This paper proposes a new analytical solution method for fast hydraulic modelling of BLTNs. The analytical method was compared against three numerical solution methods and validated using different current and future operating scenarios of a real-world bidirectional network. In all modelled cases, the calculated mass flows and pressure drops using the new analytical method yielded very small Normalised Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Square Error (CV(RMSE)), suggesting excellent agreement between the analytical and numerical solution methods. The new analytical method offers better performance over its numerical counterparts due to its explicit nature, ease of implementation, and quick modelling time.

Validity of a commercially available load cell dynamometer in measuring isometric knee extension torque in patients with knee disorders

ObjectiveAssess the validity of hand-held dynamometry (HHD) and the Tindeq Progressor (TP) in assessing peak isometric knee extension torque and limb symmetry index (LSI) versus isokinetic dynamometer (IKD). DesignProspective cross-sectional study. SettingLaboratory. Participants31 individuals with unilateral knee disorders (21 female; 28.3 ± 11 years). Main outcome measuresPeak isometric knee extension torque; Knee extension LSI. ResultsStrong to almost perfect (p < 0.001) correlations (Cohen's Kappa k) with IKD were found for both devices for peak torque of the uninvolved limb (HHD [k = 0.84], TP [k = 0.91]) and involved limb (HHD [k = 0.93], TP [k = 0.98]). For LSI, moderate to strong (p < 0.001) correlations with IKD were found for HHD (k = 0.79) and TP (k = 0.89). Mean bias errors were equivalent for determining LSI (HHD = 0.02%; TP = 0.03%). Both HHD and TP were highly sensitive (96.2–100.0%) and specific (100.0%) at the 70% LSI threshold. TP showed higher sensitivity and specificity at the 90% LSI threshold. ConclusionHHD and TP are valid in measuring isometric knee extension torque with the reference standard IKD. TP showed superior validity in identifying LSI. TP also shows greater specificity in identifying the 90% LSI threshold.

Integration of Different Density UAV Lidar and Single Beam Echo Sounder (SBES) for River and Riparian Area Digital Terrain Model (DTM) Construction

Rivers and riparian areas are vital components of ecosystems, but accurately modeling their terrain presents challenges, especially in detecting the river surface. This paper proposes an integrated approach that combines UAV LiDAR and Single Beam Echo Sounder (SBES) data to construct a Digital Terrain Model (DTM) of river and riparian areas. The objective is to overcome the limitations posed by water, which absorbs near-infrared laser energy, resulting in weak or absent LiDAR returns. Different UAV LiDAR densities were examined to determine the optimal configuration for capturing riparian areas. Evaluation of the results utilized various metrics, including root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean bias error (MBE), and correlation coefficient (CC). Three ground filtering methods were implemented and assessed: morphological filters, adaptive triangulated irregular network (ATIN) filtering, and above-ground level (AGL) filtering. Among the evaluated methods, the DTM constructed using ATIN with an 80-meter flight configuration yielded the most accurate results. It achieved an RMSE of 0.18m, an MSE of 0.03m, an MAE of 0.17m, an MBE of 9.08m, and a CC of 1.00. Comparatively, other methods exhibited higher error values and lower correlation coefficients. The findings highlight the efficacy of ATIN filtering in conjunction with an 80-meter UAV LiDAR flight for obtaining reliable DTMs of river and riparian areas. This approach demonstrates significant improvement in accuracy, particularly in terms of RMSE and MSE. The derived DTM can be a valuable tool for safeguarding and managing these critical ecosystems. In summary, this paper successfully addresses the challenge of modeling river and riparian terrains by integrating UAV LiDAR and SBES data. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a highly accurate DTM. By employing ATIN filtering with an 80-meter flight configuration, the study achieves a precise, high DTM with minimal error. The developed model contributes to protecting and preserving river and riparian ecosystems.

Application of surface energy balance Algorithm for land (SEBAL) for mapping spatiotemporal evapotranspiration over various climatic zones of Pakistan

To address the emerging issues of water scarcity in a hydrological realm, estimation of evapotranspiration (ET) at spatiotemporal scale is essential. It is crucial for understanding climate change, particularly for Pakistan's ongoing water resource management policy under the United Nations' Sustainable Development Goals (SDGs), where water is given top priority. In situ techniques can quantify ET, but with a small spatial footprint, it cannot accurately depict its regional distribution. Therefore, geospatial techniques and data, especially high resolution satellite imagery, estimate ET for large heterogeneous surfaces. The Surface Energy Balance Algorithm for Land (SEBAL) was employed in this study to estimate the spatiotemporal distribution of ET in various climatic zones of Pakistan using Landsat 8 images. A total of 48 satellite images were processed for six study sites (Skardu, Kalam, Peshawar, D.I. Khan, Quetta and Tando Jam) for the summer months from 2013 to 2020. Accuracy of SEBAL-based ET was evaluated with ground-based ET. The model performs well with daily rate of ET under southern desert hot climate of Tando Jam with highest pearson’s correlation coefficient (r) of 0.89, lowest root mean squared difference (RMSD) and mean bias error (MBE) of 0.42 mm/day and 0.26 mm/day, respectively. The steppe-hot and extremely humid climate of Peshawar follow Tando Jam, with r value of 0.82, RMSD value of 0.76 mm/day, and MBE value of 0.40 mm/day. Central desert hot climate of Dera Ismail Khan(D.I. Khan) has the lowest (though still better) r value at 0.74, while the other two indices (RMSD and MBE) do not differ significantly from other sites. A high RMSD (0.84 mm/day) for the daily ET rate is recorded in Skardu's northern, cold, snowy climate; the rest of the indices are comparable to D.I. Khan region. In Kalam, the ET rate for various land uses was calculated and was found highest for snow cover (4.66 to 6.45 mm/day), followed by forest cover (3.69 to 5.79 mm/day), and open water body (3.25 to 5.25 mm/day). The SEBAL model exhibitedtremendous potential to be applied under a range of climatic conditionsin this study. Evapotranspiration can provide vital information for monitoring hydrological extremes (droughts and floods), environmental protection, planning climate-resilient agriculture, combating climate change, and sustainable management of water resources.

A comprehensive comparison of various machine learning algorithms used for predicting the splitting tensile strength of steel fiber-reinforced concrete

Incorporating steel fibers into concrete effectively enhances its tensile strength by managing crack formation, increasing toughness and ductility, and reducing brittleness, making it a valuable choice for various applications, including bridge decks. Thus, the objective of this research is to employ the machine learning (ML) technique to estimate the splitting tensile strength (STS) of steel fiber-reinforced concrete (SFRC) that incorporates hooked industrial steel fibers (ISF) using SFRC mixtures collected from various research papers in this field. The dataset comprises 116 data points from various research papers. Utilizing the recursive feature elimination with cross-validation and leave-one-out (RFECV-LOO) method, the most influential parameters, including ISF content, water-to-cement ratio (W/C), cement content (C), fine aggregate content (FA), superplasticizer (SP), and fly ash, were identified to train the ML models. Eight statistical metrics, including the root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE), mean bias error (MBE), statistical test (Tstat), and scatter index (SI) were employed as metrics to assess model performance. Various ML models including multiple linear regression (MLR), ridge regression (Ridge), lasso regression (Lasso), elastic net regression (ElasticNet), k-nearest neighbor (KNN), support vector regression (SVR), categorical boosting (CatBoost), light gradient boosting model (LightGBM), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) were employed to predict the SFRC’s STS. The Python programming language, TensorFlow framework as well as the Scikit-learn packages, were used to run the models. Among the various ML methods, SVR demonstrated higher accuracy, achieving an R2 of 0.930, RMSE of 0.502, and MAE of 0.330. In contrast, linear regression models, especially Lasso with an R2 of 0.631, RMSE of 0.854, and MAE of 0.710, exhibited the least effective performance when it came to predicting the STS of SFRC. Moreover, the performance of the developed ML models was approved through sensitivity analysis as well as parametric analysis.