Index Of Agreement Research Articles

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Abstract In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a one-dimensional (1D) convolutional neural network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorological, and land-use/land-cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an index of agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal threefold cross validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley additive explanations analysis. The results revealed solar radiation reaching the surface, planetary boundary layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times as fast in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well. Significance Statement This work develops an emulator of the Community Multiscale Air Quality model, using a one-dimensional convolutional neural network to predict hourly surface NO2 concentrations across densely populated regions in Texas. Our emulator is capable of providing rapid and highly accurate NO2 estimates, enabling it to model diverse scenarios and facilitating informed decision-making to improve public health outcomes. Notably, this model outperforms traditional methods in computational efficiency, making it a robust, time-efficient tool for air pollution mitigation efforts. The findings suggest that key variables like solar radiation, planetary boundary layer height, and NOx (NO + NO2) emissions significantly influence the model’s NO2 predictions. By adding appropriate training data, this work can be extended to other regions and other pollutants such as O3, PM2.5, and PM10, offering a powerful tool for pollution mitigation and public health improvement efforts worldwide.

Comparative study of different machine learning approaches for predicting the compressive strength of palm fuel ash concrete

Nowadays, the challenge of industrialising concrete is to produce economical, ecological and efficient concrete. As a result, cementitious additions are increasingly used in concrete binders with proportions conferring the desired properties, particularly compressive strength. Palm oil fuel ash (POFA) from palm trees is one of these additions and introduced by substitution of cement in concrete. Although laboratory tests applied to concrete samples are expensive and time-consuming, considerable research is directed towards modelling approaches and machine learning (ML) applications. The purpose of this study is to explore different ML techniques, including artificial neural network (ANN), ANN with combined inputs (ANNX), particle swarm optimisation (PSO) and genetic algorithm (GA). These techniques are used to predict the compressive strength of POFA concrete derived from a dataset consisting of 249 samples with regard to 6 input parameters, namely, cement and POFA contents, aggregate ratio, water-to-binder ratio, superplasticiser dosage and curing duration. The performance of these techniques is evaluated using six performance indices: correlation coefficient, Willmott index of agreement, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error and mean squared relative error. The comparison shows that ANN and ANNX have better predictive accuracy than PSO and GA, and ANNX performs relatively better than ANN. Results are presented using a 3D surface response plot which visually depicts the relationship between predicted outcomes and influential parameters. The comparison made between ANNX and other models in the literature demonstrated that the present model is reliable to predict CS of POFA concrete with high precision in a wide range of data.

Deterministic and Stochastic Modeling in Prediction of Petrophysical Properties of an Albian Carbonate Reservoir in the Campos Basin (Southeastern Brazil)

Abstract —Permeability is one of the most significant and challenging parameters to estimate when characterizing an oil reservoir. Several empirical methods with geophysical borehole logs have been employed to estimate it indirectly. They include the Timur model, which uses conventional logs, and the Timur–Coates model, which uses the nuclear magnetic resonance log. The first goal of this study was to evaluate porosity, because it directly impacts permeability estimates. Deterministic and stochastic inversions were then carried out, as the main objective of this work was to estimate the permeability in a carbonate reservoir of the Campos Basin, Southeastern Brazil. The ridge regression scheme was used to invert the Timur and Timur–Coates equations deterministically. The stochastic inversion was later solved using fuzzy logic as the forward problem, and the Monte Carlo method was utilized to assess uncertainty. The goodness of fit for the estimations was all checked with porosity and permeability laboratory data using the Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Willmott’s agreement index (d). The results for the Timur model were R = 0.41; RMSE = 333.28; MAE = 95.56; and d = 0.55. These values were worse for the Timur–Coates model, with R = 0.39; RMSE = 355.28; MAE = 79.35; and d = 0.51. The Timur model with flow zones had R = 0.55; RMSE = 210.88; MAE = 116.66; and d = 0.84, which outperformed the other two models. The deterministic inversion showed, thus, little ability to adapt to the significant variations of the permeability values along the well, as can be seen from comparing these three approaches. However, the stochastic inversion using three bins had R = 0.35; RMSE = 320.27; MAE = 190.93; and d = 0.73, looking worse than the deterministic inversion. In the meantime, the stochastic inversion with six bins successfully adjusted the set of laboratory observations, because it provides R = 0.87; RMSE = 156.81; MAE = 74.60; and d = 0.92. This way, the last approach has proven it can produce a reliable solution with consistent parameters and an accurate permeability estimation.

Integrated drying model of lychee as a function of temperature and relative humidity

Drying is a universal method applied for food preservation. To date, several models have been developed to evaluate drying kinetics. In this study, lychee was dried employing a hot air dryer, and the drying kinetics was evaluated by comparing the Newtonian model, Henderson and Pabis model, Page model, and Logarithmic model. However, temperature and relative humidity, the key driving forces for drying kinetics, are not considered by these models. Thus, an integrated drying model, as a function of temperature and relative humidity, was developed to predict the hot air-drying kinetics and mass transfer phenomena of lychee followed by the calibration and validation of the model with independent experimental datasets. The model validation consisted of Nash- Sutcliffe model coefficient (E), coefficient of determination (R2) and index of agreement (d) and all of them were found close to 1 indicating perfect model fit. Besides, the developed model was applied for process optimization and scenario analysis. The drying rate constant was found as a function of temperature and relative humidity that was high at high temperature and low relative humidity. Interestingly, temperature showed a higher effect on the drying rate constant compared to relative humidity. Overall, the present study will open a new window to developing further drying model of lychee to optimize quality its quality parameters.

State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature

The safe and stable operation of electric vehicles relies on fast and accurate predictions of the state of health (SOH) of the battery. To address challenges such as limited availability of extensive battery aging data or data with informative missingness, the novel SOH prediction method based on the improved method whale optimization algorithm (IWOA)-Bi-directional Long Short-Term Memory (BiLSTM) with strong correlated single aging feature is proposed. Firstly, to accurately predict the accelerated degradation process of the battery capacity, the knee-point in the capacity degradation curve is identified as a starting point for SOH prediction by Bacon-Watts model. Next, a small number of early partial aging features of the battery cycle are extracted, such as time of charging or discharging, and various correlation analysis methods are used to select the single feature with the highest correlation with capacity degradation to reduce the computational complexity of multiple feature factors. Finally, BiLSTM model is established to predict battery SOH. In addition, in order to improve the efficiency of the adjustment for hyperparameters, IWOA is proposed to optimize the BiLSTM’s hyperparameters. Compared to the traditional Whale Optimization Algorithm (WOA), IWOA has better global search capability, robustness, and efficiency through enhancements in search strategy, mutation operation, adaptive parameter adjustment, and performance optimization. The proposed method is validated using battery datasets from NASA and CALCE. Compared with BiLSTM and WOA-BiLSTM, the simulation results indicate that the MSE of SOH prediction based on IWOA-BiLSTM method mostly remains below 0.05, and index of agreement (IA) basically maintains higher than 99%.

Modeling and multi-objective optimization of forward osmosis process

In order to ensure efficient wastewater treatment and seawater desalination, adequate modeling and optimization of the forward osmosis (FO) process has the potential to be very helpful. This paper deals with the FO model parameters calibration and FO process optimization by a gradient-based optimization method. For this purpose, an upgraded FO model, which involves temperature- and agent-dependent parameters, was developed. The FO model calibration was done using NaCl as agents in draw solution, while MgCl2 was used for model validation. The agreements between simulated and measured FO performance were satisfactory; relative index of agreement are higher than 0.99. By using the proposed FO model, the optimization of FO process conditions was performed with various definitions of the objective and constraint functions. In case of maximizing the water flux, minimizing reverse solute flux, and fulfilling the required constraints, the ratio of water flux and reverse solute flux increased up to 40 % for NaCl and up to 20 % for MgCl2; meanwhile the effective osmotic pressure difference was improved 2-times for NaCl and up to 3.8-times for MgCl2. The optimization process proved to be stable and efficient and can easily be adapted or upgraded for more complex dynamic FO modeling.

Application of aggregation operators for forecasting [formula omitted] fluctuations: From available Caribbean data sites to unequipped ones

Air pollution is a substantial issue for public health. Predicting the levels of airborne particles, especially those originating from natural phenomena like sand mists from the African dust belt, enables more effective warnings for affected populations. Currently, there are only three islands that continuously measure PM10 concentrations in the Caribbean area. Thus, there is a main issue for the forecast of these particulate pollutants for unequipped sites. In this article, a new approach based on the use of aggregation operators in a non-fuzzy framework allows to predict the quantity of PM10 for certain sites in this geographical area. It is the characteristics of non-linearity and downward reinforcement of the fuzzy operators used (triple Pi and t-norms) which make it possible to model the evolution of a phenomenon as complex as the sand mists from African dust belt. The time series having been normalized, the tests were carried out on two of the three sites (Martinique and Guadeloupe) to predict the values of the third site (Puerto Rico) and assess the quality of the prediction. The mean absolute error (MAE), the mean bias error (MBE), the root mean square error (RMSE) and the index of agreement (IOA) values show that this approach provides significant results. The African dust seasonality strongly impacts these performances metrics. These initial promising findings emphasize that the utilization of aggregation operators is a robust method for forecasting PM10 fluctuations, eliminating the need for complex parameterization or learning phase that would otherwise entail significant computational costs.

Hydrological dynamics and water resource availability in the Rift Valley Lakes Basin (Ethiopia), using a WEAP model

Abstract This study aims to assess hydrologic dynamics and the water resource potential of the Rift Valley Lake Basin (RVLB) in Ethiopia using the Water Evaluation and Planning System (WEAP) model. The surface water of the RVLB comprises streamflow and an open water system. The model was configured with 13 catchments and a monthly time series of approximately 43 years (1981–2022) to address the spatial variability of rainfall-runoff interaction. Statistical performance indicators were used to evaluate the accuracy of the model in simulating streamflows, and the results showed that the coefficient of determination (R2) ranges from 0.82–0.93, Nash-Sutcliffe coefficient of efficiency (NSE) ranges from 0.68–0.86, percentage of bias (PBIAS) ranges from –9.45 to –1.85, standard deviation ratio (RSR) ranges from 0.35 to 0.59 and index of agreement (IA) ranges from 0.62 to 0.84. The available surface water for abstraction is estimated to be 358 million cubic meters (MCM) available as lake water abstraction, and 6,534 MCM as streamflow water, making it a total surface water flow of 6,892 MCM. Considering the temporal distribution of the surface water sources, 67.5% is available in the rainy season, June–October, and 32.5% during the dry period, November–May, in the basin.

Trajectory Tracking and Disturbance Rejection Performance Analysis of Classical and Advanced Controllers for a SCORBOT Robot

This work presents the design and assessment of four control schemes for the monitoring and regulation of joint trajectories applied in the dynamic model of a SCORBOT-ER V plus robot, which includes the dynamics of the actuators, and the estimation of the friction forces present within the joints. The two classical control strategies calculated torque and PID, and the two advanced control strategies, fuzzy and predictive, are considered. In the latter case, a gravitational compensation stage is incorporated, as well as the inverse models of the motors and the transmissions of belt movement for each joint. Computational tests are performed by applying an external step-type disturbance to the third joint of the robot. Finally, an evaluation of the results obtained is presented through trajectory curves, joint errors, and the three performance indexes residual mean square, residual standard deviation, and index of agreement.

Common agronomic adaptation strategies to climate change may increase soil greenhouse gas emission in Northern Europe

Climate change poses a significant threat to agriculture, highlighting the need for adaptation strategies to reduce its impacts. Agronomic adaptation strategies, such as changes in planting dates, fertilization, and irrigation, might sustain crop yield. However, their impact on soil greenhouse gas (GHG) emission is unknown under future climate scenarios. Using the LandscapeDNDC model, we assessed the effect of agronomic adaptation strategies (early sowing, increased fertilization dose, and increased irrigation amount) on soil GHG emission, yield, and yield-scaled GHG emission. A diversified crop rotation (potato – winter wheat – spring barley – faba bean) of a long-term experiment in Denmark was used for model validation. The adaptation practices to climate change were implemented for two representative concentration pathways (RCPs; 4.5 and 8.5) and five coupled global circulation and regional climate models. The adaptation scenarios were contrasted against a baseline scenario under current management practices. Soil-related variables showed better model fit (refined index of agreement ≥ 0.38) and lower errors (mean absolute error ≤ 8.18) than crop-based outputs for model validation. A total yield of ∼29 (± 3) t DW ha−1, and soil GHG emission of ∼3.02 (± 1.39) t CO2e ha−1 (RCP8.5) were obtained for the crop rotation system under the baseline for 2071–2100. Early sowing and its combination with increased fertilization decreased the yield compared to the baseline by 6.1 and 4.8 %, respectively (RCP8.5). Conversely, early sowing with increased irrigation, and early sowing with increased fertilization and irrigation, produced higher yields by 2.3 and 4.0 %, respectively (RCP8.5). All the agronomic adaptation strategies increased soil GHG emissions (ranging from 4.1 to 17.8 %) as well as yield-scaled GHG emissions (varying from 3.0 to 12.9 %) (RCP8.5). The highest soil GHG emission was simulated for early sowing in combination with increased fertilization and irrigation. Our study indicates that soil GHG emission will increase in the coming decades and that the agronomic adaptation strategies needed to sustain food production may further exacerbate this emission.

Electricity Load Demand Prediction for Microgrid Energy Management System Using Hybrid Adaptive Barnacle‐Mating Optimizer with Artificial Neural Network Algorithm

In recent years, one of the primary causes of high electricity consumption is the growing global population and the availability of power‐demanding smart devices. As a result, accurate load forecasting tools are required for effective energy conservation in microgrids. Various simulation tools and artificial intelligence (AI)‐based methods have been used to make the best forecasts of electricity demand. Furthermore, conventional systems are static and based solely on historical data. To address this practical need, this work aims to create a machine learning (ML) model for short‐term load forecasting based on feature selection and parameter optimization. This research proposes a hybrid short‐term load demand prediction approach that combines an adaptive barnacle‐mating optimizer (ABMO) and an artificial neural network (ANN). When compared to the regression tree (RT), support vector machine (SVM), ANN, and particle swarm optimization‐based ANN (PSO‐ANN) algorithms, the proposed ABMO‐ANN algorithm reduces mean absolute percentage errors by 67.69%, 64.58%, 59.18%, and 42.02%, respectively. Compared to conventional RT, SVM, ANN, and PSO‐ANN algorithm, the hybrid ABMO with ANN outperforms them based on mean absolute percentage error, root mean square error, correlation coefficient , symmetric mean absolute percentage error, and agreement index (d).

Current Status of Stroke Thrombolysis in the Mission Thrombectomy 2020+ Caribbean Region

Background Low‐ to middle‐income countries have limited access to thrombolytic therapy. To our knowledge, there is no validated tool available to objectively measure access to thrombolytic agents or barriers to routine clinical use. Methods We developed the 17‐item tissue plasminogen activator Spot Check tool to assess usage of acute stroke thrombolysis regarding local experience, financial constraints, and perceived barriers to care; evaluating the current state of clinical practices in the Mission Thrombectomy 2020+ Caribbean region. The survey was disseminated via an online link, and the information was collected and analyzed via SPSS. Results The tool was validated by 3 international experts with an Average Content Validity Index of 1 and a Universal Agreement Index of 1 across 3 domains: local experience, financial constraints, and barriers to usage. The participant survey response rate was 64%, representing 15 of 44 Mission Thrombectomy 2020+ Caribbean countries. There was limited or no access to thrombolytic agents in 40% of countries surveyed. Among cases treated with thrombolytics, 43% of patients had to pay out of pocket before treatment was provided, and l<10% were covered by insurance/government. Among 51% of countries surveyed, no acute thrombolytic treatment was provided for acute stroke in the 2021 calendar year. Only 1 center treated >100 cases per year. Most respondents (88%) agreed there were barriers to acute stroke thrombolysis in the region. The tissue plasminogen activator Spot Check tool was able to identify barriers impacting the number of cases per year, including absence of stroke protocol ( P <0.001), upfront cost of alteplase ( P = 0.003), restricted the amount of thrombolytics ( P = 0.002), neurology intensive care unit or stroke unit monitoring of patients following thrombolytics ( P = 0.017), cost of thrombolytic agents to the hospital ( P = 0.042), and access to computed tomography scan ( P = 0.03). Conclusion This survey brings light to an enormous disparity in the care of stroke patients around the world, specifically in the Mission Thrombectomy 2020+ Caribbean region.

Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements

Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements

Enhancing rutting depth prediction in asphalt pavements: A synergistic approach of extreme gradient boosting and snake optimization

This study investigates the development of pavement performance models, focusing on the predictive analysis of pavement distress using ensemble machine learning methods. We employ the Research Institute of Highway Ministry of Transport track dataset to construct predictive models for pavement distress, comparing various methodologies including Random Forest (RF) regression, Extreme Gradient Boosting (XGB) machines, Snake Optimizer-Random Forest (SO-RF), and Snake Optimizer-Extreme Gradient Boosting (SO-XGB). The approach is conducted by multiple experiments and a comprehensive comparative analysis leveraging the repeated K-Fold cross-validation method to ensure the robust assessment for these models. Our findings show that the SO-XGB method is particularly effective in predicting rutting depth. The strength of the SO-XGB model lies in its excellent performance in several performance metrics, such as coefficient of determination (R2), the mean absolute error (MAE), root mean square error (RMSE), the mean absolute percentage error (MAPE), performance index (PI), index of agreement (IA), variance accounted for (VAF) and objective function (OBJ). These results not only illustrate the model’s accuracy but also the practical applicability of SO-XGB model in automatically forecasting pavement damage. Thus, the SO-XGB method stands out as a direct, powerful, and efficient tool in the field of pavement performance prediction.

Methodological Proposal for the Adaptation of the Living with Long-Term Conditions Scale to the Family Caregiver.

(1) Background: Living with long-term conditions affects both patients and family caregivers. To obtain a more complete overview of this phenomenon, a measurement instrument is needed that includes both perspectives. The aim is to adapt a scale to family caregivers of individuals with long-term conditions. (2) Methods: A methodological proposal is presented that illustrates the adaptation of the EC-PC scale to the family caregiver. Three phases are proposed: adaptation of the items, panel of experts, and pre-test. (3) Results: In the adaptation phase, the items from the original EC-PC were modified to adapt them to the family caregiver, and new items were added associated with the differences in living with LTC from the perspective of family caregivers. In the panel of experts phase, a universal agreement was reached related to the clarity, relevance, and essentiality of the items included. In the pre-test phase, the content of the scale was verified quantitatively and qualitatively. (4) Conclusions: The content of the items of version 5 of the EC-PC-Family showed a high index of inter-judge agreement. When a phenomenon affects both patients and their environment, such as living with LTC, it is necessary to include both perspectives in the measurement tools.

Preventing sexual violence in adolescence: digital booklet construction and validity

Objective: To develop and analyze a digital booklet for preventing sexual violence in adolescence as proof of content validity. Methods: This is a methodological study developed in three stages: digital booklet construction based on the Falkembach model; material validity by a committee of experts using the Index of Agreement (IA), considering it greater than 80%; and assessment by the target audience (adolescents), based on the application of the Suitability Assessment of Materials (SAM), considering it “Superior” between 70% and 100%; “Suitable”, between 40 and 69%; “Unsuitable”, between 0 and 39%. Results: The digital booklet was considered valid by experts, with an agreement rate between 86% and 100%, and qualified as “Superior” by adolescents with a score percentage of 97% on the SAM. Conclusion: The booklet was considered valid by experts and target audience, being able to be used as a technological resource to identify situations of sexual violence in adolescence and enable the adoption of preventive behaviors.

Comparison of conjunctival swabs and blood samples in dogs for molecular and serological detection of Leishmania infantum infection in Colombia.

Surveillance of canine leishmaniasis in Colombia is restricted to the appearance of visceral leishmaniasis cases in humans, and is mainly performed by serological tests. This requires blood sampling by veterinarians or technicians according to Colombian laws. The aim of this study was to evaluate the utility of conjunctival swabs in the molecular detection of Leishmania in dogs from the municipality of Ovejas, Sucre. The present study was cross-sectional and descriptive. The collection source of samples and information was primary. Blood samples and conjunctival swabs from 121 dogs were analysed by PCR-ITS1 to detect Leishmania spp. Positive samples were used to amplify a conserved region of the Leishmania infantum kinetoplast minicircle. Performance of both sample types was calculated by proportion of positive samples of each type and the degree of agreement between them was determined by Cohen's kappa (κ) agreement index. Leishmania infection was detected in 17.4% (21/121) of blood samples and in 16.5% (20/121) of conjunctival swabs. In total, 28.1% (34/121) of the canines were infected, of which 11.8% (4/34) were infected with L. infantum in the conjunctival swabs and 5.9 % (2/34) in the blood samples. The agreement between blood and conjunctiva was medium (κ = 0.207) by PCR-ITS1 amplification. The use of conjunctival swab as a non-invasive sample could be used as an alternative method for surveillance of canine leishmaniasis.

Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions

A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R2), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day−1) and RMSE (0.326 mm day−1) were also obtained.

The autonomous word as a component of a compound predicate in European languages

The article is devoted to a typology of the compound predicate in a number of European languages (English, German, French, Spanish, Russian, Polish, Turkish and Bashkir), which goes a long way to building a theory of language in general and a theory of the sentence in particular. The method of our analysis is based on contrasting inductively selected verbal structures of some natural languages with a language etalon. The aim is to establish factors determining the positional and syntactic autonomy of the word. Positional autonomy is related to the word’s ability to change position in a sentence according to a certain degree of word order freedom, while syntactic autonomy suggests the ability of a word to function as an isolated one-word sentence. An ideal model characterized by absolute positional and syntactic word autonomy stands for the mentioned language etalon. It is assumed hypothetically that languages with a maximum possible free word order enjoy a marked word autonomy, unlike languages with a fixed word order being suggestive of reduced word autonomy. The hypothesis is verified on the basis of optional sampling of various types of the predicate in three temporal forms of the verb representing the category of person. The research ascertains dependence of word autonomy on agreement of grammatical form and meaning. The index of agreement determined as a ratio between the signifier and the signified in the paradigm of the verb conjugation enables to reveal the autonomy degree for each language of contrast. According to the decrement of autonomy the contrasted languages can be disposed in the following order: (Polish, Turkish, Bashkir) → (Russian) → (Italian) → (German) → (French) → (English).