Statistical Indicators Research Articles

Качество жизни населения Мурманской области: субъективные оценки и объективные характеристики

The assessment of the population's quality of life is an important analytical tool of the state socio-economic policy, which allows analyzing the current level of regional development, the population's satis-faction with living conditions, making interregional comparisons, determining shortcomings and prospec-tive directions of public administration. At the same time, objective statistical indicators, on which the state management is based, in many respects may not coincide with the subjectively perceived quality of life of the population. In this context, the presented research is particularly relevant and practically significant. The purpose of the research is to identify the problems of development of the Murmansk Oblast, the most industrially and territorially developed region of the Arctic zone of Russia, on the basis of the comparison of subjectively perceived assessments of the quality of life and objective socio-economic characteristics, and to outline ways to solve them. To achieve this goal, the following tasks were solved: the methodology for assessing the subjectively perceived and statistically determined quality of life of the population on the basis of indicators characterizing personal well-being and the quality of the living environment was developed; the subjectively perceived quality of life was assessed on the basis of the author’s methodology including the results of surveys of the population of cities and towns in the Murmansk Oblast; the quality of life of the population of the Murmansk Oblast was assessed on the basis of statistical data of socio-economic development; the data of the obtained assessments were compared, similarities and differences in the satisfaction of the population with personal well-being (as well as with the living environment) and statistical characteristics were revealed; the most important problems of socio-economic development, the resolution of which will improve the quality of life of the population of the Arctic region, were identified.

Carbon Flux Models in a Zonally Dry Tropical Forest Area (Caatinga)

Studies on energy exchange in ecosystems are critical for understanding carbon flows amid different vegetation patterns. In semi-arid areas, comprehending the variability in carbon absorption is essential for quantifying and anticipating the impacts of changes in the caatinga ecosystem. This study aims to calibrate and evaluate models for Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Ecosystem Respiration (Reco) in the caatinga biome. NEE measurements were obtained and calculated at 30-minute intervals using EddyPro 3.6 software, from raw data measured at 10 Hz. GPP was estimated by partitioning NEE and Reco, all measured in micromoles of CO2 per square meter per second (μmol CO2m⁻²s⁻¹) using the eddy covariance (EC) tower, installed in a legal reserve at Embrapa Semiárido, in Petrolina, Pernambuco, within a section of the caatinga canopy. Following the measurement of carbon fluxes and ecosystem respiration, field measurements were conducted using a portableFieldSpec HandHeld spectroradiometer to obtain the reflectance of the canopy around the turbulent eddy covariance tower. These measurements were carried out in 2015 in a preserved caatinga area. Multiple linear regression models weredeveloped to estimate carbon fluxes from orbital images, enabling accurate estimation of GPP, NEE, and Reco, using the MODIS/Terra Daily Surface Reflectance product (MOD09GA). The primary results demonstrate the effectiveness of the developed models, particularly the GPP model, which exhibited the best statistical indices (R = 0.97; R² = 0.95; Root Mean Square Error = 0.20). Observations from the EC tower indicated that the models developed to estimate NEE, GPP, and Reco, using visible and near-infrared reflectance data, accurately represented the dry period and effectively captured the phenological aspects of the caatinga ecosystem.

Methodological Aspects of Analysis of Competition in Regional Markets of Services for Children with Disabilities

The modern development of social services markets is aimed at introducing competition mechanisms into them. These trends create the need to develop and use a methodology for assessing competition, taking into account the characteristics of each market. Existing methods for calculating various indices are labor-intensive for social markets and do not take into account their specifics: a significant share of government institutions, a narrow segment of consumers, and significant differentiation in prices for services. The article proposes a methodology for analyzing competition in the market of psychological and pedagogical support for children with disabilities, based on the use of two groups of indicators: objective and subjective indicators. To analyze subjective assessments, a coefficient of satisfaction with the price-quality ratio is proposed. Statistical indicators are used as objective assessments. The proposed methodology was tested using the example of markets for psychological and pedagogical support for children with disabilities in a number of regions of Russia. The results obtained allowed us to draw a conclusion about the state of competition in the markets under consideration and identify “problem” regions. The presented methodology allows us to obtain relatively adequate results of the analysis of competition in the market of psychological and pedagogical support for children with disabilities and can be extended to other markets in the social sphere with appropriate adjustments.

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling.

Quantitative Structure Photovoltaic Properties Relationship of Coumarin Dyes Derived

The performance of an organic solar cell is strongly influenced by the structure of the photosensitizer. In this work, the open-circuit voltage (<I>V<SUB>OC</SUB></I>) and conversion efficiency (<i>η</i>) of a series of coumarin dyes are quantitatively related to the structure of nine coumarin derivatives. The Quantitative Structure Property Relationship (QSPR) is performed using the statistical method of multiple linear regression. In addition, descriptors determined from the ground state at the Cam_B3lyp/6-31G(d, p) level of theory and from the 2D structure of the molecules are mathematically related to the photovoltaic properties. These VOC and <i>η </i>models are accredited with very good statistical indicators (<I>R<sup>2</sup></I> = 0.906 and 0.918; Q<sub>cv</sub><sup>2</sup>= 0.845 and 0.849; S= 0.045 and 0.112; F = 14.524 and 16.846). These statistical indicators confirm the robustness and performance of the models developed. The results show that Voc improves with decreasing surface tension (<i>ts</i>) and increasing number of cycles (<i>cycle</i>). As for the conversion efficiency of light radiation into electrical energy, it is optimal when the light harvesting efficiency (<i>LHE<sub>th</sub></i>) and the excited state lifetime (<i>τ<sub>th</sub></i>) are high. In conclusion, these models have good predictive capabilities and can be used to predict and explain the open-circuit voltage and efficiency of coumarin derivatives that belong to the same field of application.

Statistical analyses of gender disparities in unpaid work in Georgia

Using data from the Labour Force Survey (LFS) with a quarterly sample size of 6,400 households and the Georgia Time Use Survey (GTUS) covering 3,680 households nationwide, this article investigates gender disparities in unpaid work in Georgia and their impact on individual welfare. Employing international methodologies, we calculated new statistical indicators and refined existing ones, including the improvement of Sustainable Development Goal indicator 5.4.1 (SDG 5.4.1). Regression analysis identified education level as a significant factor influencing unpaid work. Our findings underscore significant gender disparities in unpaid work, leading to heightened workloads and temporary poverty for women. These disparities persist across various demographic factors. This research contributes to understanding the dynamics of unpaid work and its implications for gender equality and welfare.

Prediction and optimization framework of shear strength of reinforced concrete flanged shear wall based on machine learning and non-dominated sorting genetic algorithm-II

Reinforced concrete (RC) flanged shear wall has good lateral strength and stiffness, which has been widely used in building structures. Due to the coupling effect of many factors such as wall section shape, shear span ratio, so the shear performance evaluation of flanged wall is still very limited. This paper proposed a prediction framework for the shear capacity of RC flanged shear walls. A database containing 14 input variables, 1 output variable and 153 samples was constructed to evaluate the prediction accuracy of 11 existing design methods. The Pearson coefficient was used to preliminarily analyze the correlation between variables. The grid search was used to optimize the hyperparameters of 4 machine learning models, and six statistical indicators ( R2, R, RMSE, SD, MAE, and MAPE) were used to comprehensively compare the prediction results of the ML models to determine the best model. On this basis, SHapley Additive exPlanations (SHAP) was used to enhance the interpretability of the prediction models, and the mechanism of the input variables on the shear capacity was quantified. A graphical user interface (GUI) was proposed to guide the engineering design. A multi-objective model (MOO) was established to analyze the trade-off between shear performance and cost, thereby determining the best optimal scheme. The results show that the prediction accuracy of the ML models is better than the existing design methods. The XGB model has the best prediction performance, with R2, R, RMSE are 0.99, 0.99, 118.96, respectively. The SHAP method can effectively enhance the interpretability of the ML models, and tw, lw and f ′c are the key parameters affecting the shear capacity of the flanged shear wall.

Calibration and validation of the AquaCrop model for forage cactus production systems under different management interventions in the semi-arid region of Brazil

Simulation models are useful tools for estimating plant response and contributing to the development of management strategies and yield predictions in crops. This study aimed to calibrate and evaluate the AquaCrop-FAO model for the forage cactus imposed on different cropping systems in the semi-arid region of Brazil. Four experiments were conducted: cactus-sorghum intercropping system with five spacings between plants (0.10, 0.20, 0.30, 0.40 and 0.50 m); cactus-sorghum intercropping system with five spacings between rows (1.00, 1.25, 1.50 and 1.75 m); single cactus with four levels of mulch (0, 5, 10 and 15 Mg ha−1); and single forage cactus with four levels of nitrogen fertilisation (50, 150, 300 and 450 kg of N ha−1). The performance of the model was evaluated using the following parameters: root mean squared error (RMSE); normalised RMSE (NRMSE), coefficients of determination (R2), Nash-Sutcliffe model efficiency coefficient (ME) and the Willmott index of agreement (d). During the calibration stage, the statistical indices pointed to the good performance of the AquaCrop model in estimating dry biomass in most of the cropping systems, with 10 < NRMSE <20; R2 > 0.96 and ME > 0.95. The AquaCrop model showed the best performance simulating systems under different levels of nitrogen fertilisation. Among the intercropping systems, the denser arrangements showed the highest values for simulated biomass.

STATISTICALLY STABLE VERBAL-NOMINAL COMBINATIONS WITH THE COMPONENT ЖИЗНЬ IN THE ANCIENT RUSSIAN CHRONICLES

The article describes statistically stable verbal-nominal combinations with the component ‘жизнь’, functioning in the Old Russian language. The material for the study is an electronic collection of three ancient Russian Chronicles, published on the portal “Manuscript: Slavic Written Heritage” (URL: http://manuscripts.ru/). The study presents a statistical analysis of bigrams, which made it possible to identify statistically significant verbal-nominal combinations with the component ‘жизнь’ and qualify them as expressions that claim stable status. Analysis of the obtained statistical information makes it possible to identify connections between various linguistic elements, determine patterns in the development of certain phenomena, describe the functioning of complex processes in language and clarify the mechanisms of interaction of units in it. This study is carried out using special statistical indicators that allow us to determine the degree of stability of expressions, thereby avoiding subjectivity in assessing a particular construction from the point of view of ideomaticity. To identify collocations in the corpus of the oldest Russian Chronicles, the tools of the Manuscript IAS are used, in particular the n-gram module, which allows you to obtain text combinations with a different number of components. As a measure of association, the T-score measure is used, which allows us to determine how non-random the strength of connection between words in a phrase is. The selected bigrams and their statistical data demonstrate the presence in the Old Russian period of combinations that cannot be called phraseological units in the modern sense, but it is necessary to note their statistical stability in the text, which allows them to be qualified as stable combinations / collocations / statistically stable expressions.

Measuring Data Quality from Building Registers: A Case Study in Italy

Geographic data quality is a complex issue requiring continuous operational improvements. Considering this to be one of the most topical research and technical issues in official statistics and environmental monitoring, this study re-connects the operational dimension of ‘geographic data quality’ with the broader issue of monitoring the quality of official statistics. By estimating the accuracy of public (spatially explicit) data, this study illustrates an operational framework with an exploratory exercise in estimating the geographic data quality characteristic of a specific information source within the official statistical system (i.e., building registry) in a given European country, namely, Italy. The results of this exercise provide a paradigmatic example of profound innovation in the activities of statistical services in Europe and specifically the Italian National Statistical Institute (Istat), transitioning from independent (and poorly connected) field surveys to an integrated system of registries. Since several studies are based on spatially explicit survey units, it is essential to estimate the quality of geographical data, especially those derived from information sources where space is topical information, such as (local, regional, or national) building registers. Thanks to the results of an empirical exercise applied to Italian building registers, the present article will discuss the issue of data accuracy, considered the main issue related to monitoring geographic data quality, from an official statistics’ perspective. Statistical indicators will be proposed for the assessment of systematic and random errors of spatially explicit measures, possibly enabling a quali-quantitative improvement in the semantic content of building registers that address the inherent requirements of official statistics. Such indicators have some positive implications for the entire system of official statistics in Italy and, for generalization, within the European Statistical System.

Statistical implication analysis: a novel approach to understand the reciprocal relationships between outcomes in early psychosis.

Patients can respond differently to intervention in the early phase of psychosis. Diverse symptomatic and functional outcomes can be distinguished and achieving one outcome may mean achieving another, but not necessarily the other way round, which is difficult to disentangle with cross-sectional data. The present study's goal was to evaluate implicative relationships between diverse functional outcomes to better understand their reciprocal dependencies in a cross-sectional design, by using statistical implication analysis (SIA). Early psychosis patients of an early intervention program were evaluated for different outcomes (symptomatic response, functional recovery, and working/living independently) after 36 months of treatment. To determine which positive outcomes implied other positive outcomes, SIA was conducted by using the Iota statistical implication index, a newly developed approach allowing to measure asymmetrical bidirectional relationships between outcomes. Two hundred and nineteen recent onset patients with early psychosis were assessed. Results at the end of the three-years in TIPP showed that working independently statistically implied achieving all other outcomes. Symptomatic and functional recovery reciprocally implied one another. Living independently weakly implied symptomatic and functional recovery and did not imply independent working. The concept of implication is an interesting way of evaluating dependencies between outcomes as it allows us to overcome the tendency to presume symmetrical relationships between them. We argue that a better understanding of reciprocal dependencies within psychopathology can provide an impetus to tailormade treatments and SIA is a useful tool to address this issue in cross-sectional designs.

Enhancing groundwater resource detection in Ahmednagar District through an integrated GIS framework utilizing AHP, SI, and FR models

ABSTRACT Groundwater resources affected by climate change exacerbate groundwater level declines in areas relying on erratic monsoons and finite surface water supplies. This research developed and compared an integrated geographic information system (GIS) with the analytical hierarchy process (AHP), the statistical index model (SI), and the frequency ratio model (FR). Multiple layers of the GIS framework have captured surface and subsurface characteristics, including rainfall, slope, elevation, land use/land cover, soil, geology, and geomorphology. 30% of the 69 wells in the study area are the testing dataset, and the remaining 70% make up the training dataset. Based on AHP, the results show that 35.34% of the region has low potential, 43.47% has moderate potential, and 21.09% has high potential. While SI modeling reveals 9.60, 57.36, and 24.79% for low, moderate, and high potential, respectively, FR analysis suggests 25.50% low potential, 52.74% moderate potential, and 21.60% high potential areas. The superior reliability of the SI model has been indicated by the validation of the GWPZ map using receiver operating characteristic area under the curve values. The results of this study are crucial for planning sustainable water supplies and managing groundwater resources in areas susceptible to climate change and groundwater depletion.

Prediction of groundwater level using artificial neural network as an alternative approach: a comparison assessment with numerical groundwater flow model

ABSTRACT Over-exploitation of groundwater in arid and semi-arid regions requires an understanding of dynamics for effective management strategies. This study uses an artificial neural network (ANN) to predict groundwater levels using initial water level, pumping, and hydrological and meteorological input variables. A sensitivity analysis assessed predictors’ impact on accurate groundwater level prediction. The accuracy of predictions by the ANN model was examined through different performance evaluation criteria and the result achieved is satisfactory as compared to reasonable statistical indicator values. A comparison of results simulated by the ANN model and numerical groundwater flow model (MODFLOW) was carried out and it was found that the prediction of the ANN model was consistently superior to that of the numerical model. Therefore, data-driven models such as ANN can provide better predictions of groundwater level that will aid in groundwater resource management.

Development of a basic evaluation model for manual therapy learning in rehabilitation students based on the Delphi method

ObjectiveManual therapy is a crucial component in rehabilitation education, yet there is a lack of models for evaluating learning in this area. This study aims to develop a foundational evaluation model for manual therapy learning among rehabilitation students, based on the Delphi method, and to analyze the theoretical basis and practical significance of this model.MethodsAn initial framework for evaluating the fundamentals of manual therapy learning was constructed through a literature review and theoretical analysis. Using the Delphi method, consultations were conducted with young experts in the field of rehabilitation from January 2024 to March 2024. Fifteen experts completed three rounds of consultation. Each round involved analysis using Dview software, refining and adjusting indicators based on expert opinions, and finally summarizing all retained indicators using Mindmaster.ResultsThe effective response rates for the three rounds of questionnaires were 88%, 100%, and 100%, respectively. Expert familiarity scores were 0.91, 0.95, and 0.95; coefficient of judgment were 0.92, 0.93, and 0.93; authority coefficients were 0.92, 0.94, and 0.94, respectively. Based on three rounds of consultation, the model established includes 3 primary indicators, 10 secondary indicators, 17 tertiary indicators, and 9 quaternary indicators. A total of 24 statistical indicators were finalized, with 8 under the Cognitive Abilities category, 10 under the Practical Skills category, and 6 under the Emotional Competence category.ConclusionThis study has developed an evaluation model for manual therapy learning among rehabilitation students, based on the Delphi method. The model includes multi-level evaluation indicators covering the key dimensions of Cognitive Abilities, Practical Skills, and Emotional Competence. These indicators provide a preliminary evaluation framework for manual therapy education and a theoretical basis for future research.

A Develop Strategies Management of Private Tennis in Xi’an City, China

Background and Aims: Developing strategies for managing private tennis programs is critical for maximizing player potential and maintaining a sustainable competitive advantage. It entails tailoring training, facilities, and resources to individual and organizational objectives, resulting in long-term success in the sport. Thus, this paper aims: (1) What are the problems of development strategies management of private tennis clubs? And (2) to develop management strategies for private tennis clubs. Methodology: This research is a survey and interview research. The population used in this research was 21 professionals who are former or current presidents of the Chinese Tennis Association national team coaches and tennis players. Samples mean the l9 experts selected for Purposive selection by determining the characteristics of the sample sampling were as follows: 7 tennis professors and 12 tennis club officers (8 female and 11 male) using Delphi techniques—data analysis using statistics indicator median and interquartile range, and POLC. Results: The tennis club management development platform model effectively develops private tennis clubs in Xi’an City with an average mean of 4.64 on a 5-point Likert scale for agreement. The platform includes: (1) Planning to build a model for private tennis club development strategies management can plan the creation of many forms in the presentation. There are elements of building a platform in the planning that can be used differently. Therefore, it depends on creating a model for managing private tennis club development strategies. (2) Management builds a model for private tennis club development strategies management can be as ranged in many ways and can be organized in many forms, the organization must be ready in all aspects, such as people, places, model tools, and model platform. In modern times, owners are interested in an organization that forms a platform for developing an organization model style. (3) Implementing the practice of creating a model for private tennis club development strategies management can be achieved according to the principles of development strategies management according to the steps of private tennis club development strategies management according to the established guidelines. (4) Controlling the creation of a model for private tennis club development strategies management can control the use of planning at every stage whether it’s an evaluation form measure and control the way to use the model in the right way. Conclusion: The study found that the tennis club management development platform is extremely effective in improving private tennis clubs in Xi'an City, with strong agreement among participants. The platform's success can be attributed to its comprehensive approach, which includes planning, management, implementation, and control. These elements all contribute to the effective development of private tennis club strategies.

Effectiveness of demographic policy measures in addressing population decline in contemporary Russia

Demographic policy is one of the key tasks of almost any state at the present time. It correlates with the solution of pressing problems in the economic and social spheres, directly depends on the state of healthcare, education, migration policy and other factors and directly affects the socio-economic development of both individual regions and the country as a whole. Many Russian and foreign researchers believe that demographic indicators very accurately reflect the socio-economic and political situation of the state. The relevance of the study is due to the fact that for the progressive socio-economic development of any country, positive demographic dynamics are necessary. The main sign of the negative demographic situation that has developed in modern Russia and a number of countries, primarily European, is the growing scale of depopulation (population extinction). The purpose of this work was to analyze the existing demographic policy of Russia and compare demographic trends in Russia and other countries. The work uses methods of statistical data analysis, comparison of statistical indicators of fertility, mortality, natural population decline, migration, marriage rates in Russia and the Republic of Srpska, methods of retrospective analysis, research of the institutional environment created by the action of state and national programs “Demography”, “Providing accessible and comfortable housing and public services for citizens of the Russian Federation”, “Strategy of socio-economic development for the period until 2024”, Presidential decrees, etc. Research has shown that despite measures taken to overcome the demographic crisis, Russia’s population continues to decline. According to the Federal State Statistics Service of the Russian Federation (Rosstat), as of 1 January 2023, 146.45 million people lived in Russia. By 1 January 2046, according to a Rosstat forecast published in October 2023 the country’s population will decrease to 138.77 million people. To solve demographic problems in the Russian Federation, a national project “Demography” was developed and approved. The government has allocated more than 3 trillion rubles for its implementation. However, it is not possible to completely overcome the negative trend. The authors proposed a number of economic and ideological measures within the framework of agglomeration, migration, and family support policies that can be used within the framework of socio-economic development strategies and national programs aimed at overcoming the demographic crisis.

Socio-Economic Impact of Ethnobotanical Appraisal of Traded Medicinal Plants on Local Communities of District Astore (Gilgit Baltistan)

Medicinal plants are the only possible solution to treat various complications of human beings. The local community of district Astore, Gilgit Baltistan are dependent on medicinal plants for their primary healthcare. The present study was conducted to identify and document the medicinal uses of traded medicinal plants and to assess their socio-economic impact and current status in the respective area. A total of 26 traded medicinal plants were explored from the various villages as informed by the experienced respondents. The collected plants were pasted on herbarium sheets for identification purpose. Ethno medicinal uses were also documented and the collected data was analyzed by using statistical indices i.e. Use value, Relative frequency citation, Fidelity level and Pearson correlation coefficient. Questionnaire survey was conducted and 80 respondents were interviewed to assess the socio-economic impact and current status of traded medicinal plants. The most frequent used part of medicinal plant was leaves followed by roots and seeds. The RFC (0.88) and UV (0.98) were recorded highest for Thymus linearis. The Pearson correlation coefficient value between RFC and UV was 0.944, indicating a strong relationship between RFC and UV. Survey results showed that the traded medicinal plants played a pivotal role by enhancing socio-economic condition of local communities. Findings also indicated that the traded medicinal plants are gradually decreasing due to anthropogenic activities like improper method of collection, illegal trade, unsustainable use and lack of protection and conservation measures.

Advanced PSO Algorithms Development with Combined lbest and gbest Neighborhood Topologies

Abstract This paper introduces an innovative approach integrating global best (gbest) and local best (lbest) PSO communication topologies. The algorithm initiates with lbest and seamlessly transitions to gbest, with the switching rate controlled by the parameter “a”. Rational values of “a” is determined through numerical experiments. A comparative methodology employing two estimation criteria is used to showcase the improved performance of the modified PSO-based algorithms. Furthermore, the efficacy of this approach is demonstrated in addressing two optimal control problems within dynamical systems. Results highlight the modified algorithms’ superiority in terms of the total number of successful runs and statistical indicators. Consequently, these advanced algorithms prove effective for applications such as artificial neural network training, controller gains determination, and similar problem domains.

Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

Artificial intelligence-driven enhanced CBR modeling of sandy soils considering broad grain size variability

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively.