Maximum Research Articles

A 45-year updating wind and wave hindcast over the Oman Sea and the Arabian Sea

This study focuses on developing a comprehensive and reliable wind and wave hindcast for the Oman Sea and Arabian Sea, spanning a significant 45-year period (1979–2024). The objective is to capture the intricate wind and wave climate of the region, characterized by distinct monsoon cycles and occasional tropical cyclones. The availability of extensive wave data over four decades would be an irreplaceable tool for researchers and engineers, enabling improved accuracy in extreme value analysis, sediment transport studies, and wave-induced current simulations. Despite limited field observations, all available measured data within the region were collected, analyzed for configuration of the models and utilized for model calibration and validation. The hindcast data was generated using the Weather Research and Forecasting (WRF) model for winds and WAVEWATCH III (WW3) model for waves, and evaluated against observational and measured wind and wave parameters. A comprehensive statistical analysis of the hindcast model's performance reveals adequate agreement with measured data, as evidenced by root mean square errors (RMSEs) of 1.23 m/s for wind speeds and 0.37 m for significant wave heights in average. These results underscore the model's reliability for research and engineering applications, particularly in the Arabian Sea, with a focus on the northern coastlines of the Oman Sea. The specific model configuration employed in this study holds significant potential for future investigations in the northern Indian Ocean, offering a valuable tool for understanding and predicting climatical conditions in this region.

Main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area

The constantly growing need for fresh water in the Shaim oil and gas region necessitates the exploration of new areas and the development of measures to improve the operation of water intakes. To predict the quality of groundwater, which is an operational parameter of the aquifer aquifer, it is needed up-to-date information on the chemical composition of ground-water. The aim of this study to identify main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area, compared to average values from adjacent areas. Research methods include the systematization of laboratory data on water samples, analysis of groundwater chemistry, and mapping of the main spatial-temporal patterns in component concentration changes. The average values of chemical composition indicators in the studied area are similar to those of neighbouring areas, with some indicators exceeding drinking water standards. Similarities are observed in the variability of the chemical composition, spatial patterns of changes in most chemical indicators, and the presence of areas with extreme values. Current data on the chemical composition of the oligocene aquifer ground-water, including key characteristics and spatial-temporal trends presented in distribution maps, can be used in designing groundwater intakes and forecasting long-term groundwater quality.

Innovativeness of the European economies in the context of the modified European Innovation Scoreboard

Research background: Innovation is an important determinant of economic development, and its importance is growing with the advancement of digitization and the development of the Fourth Industrial Revolution. In turn, the assessment of the innovativeness of a country’s economy affects its investment attractiveness and international image. Purpose of this study: Given the factors presented above, the main aim of this study is to explore and compare the innovativeness of European economies in the context of the modified European Innovation Scoreboard. Methods: To achieve this goal, the authors propose two key modifications of the existing innovation assessment methodology contained in the European Innovation Scoreboard. The first concerns the principles of the normalization of assessment indicators and aims to eliminate extreme assessment values. The second proposes to remove the indicators which are difficult to measure, subjective, and not always useful: (1) those related to sustainable development (resource productivity; emissions of fine particulates; and environment-related technologies) and (2) those that take into account only small and medium-sized enterprises (SMEs introducing product innovations; SMEs introducing business process innovations; and innovative SMEs collaborating with others). Findings & value added: An alternative approach to assessing the innovativeness of the analyzed countries — as proposed by the authors — resulted in changes in the ranking of countries in relation to the European Innovation Scoreboard, but the changes were not significant. The major changes concerned Serbia (down four places), Belgium (down two places), Hungary (up three places), and Poland (up two places). In addition, five countries moved up one place in the ranking (the UK, the Netherlands, Luxembourg, Estonia, Slovenia, Spain, and Slovakia), and five lost one place in the ranking (Norway, Italy, Portugal, Greece, and Croatia). Thirteen countries had no changes. The obtained results allowed us to conclude that the countries with the best and the worst innovativeness maintained the assessment level regardless of changes in the methodology of its implementation. The objectification of rules had the strongest impact on the countries in the middle of the ranking. This study draws attention to the problem of the subjectivity of the innovation rankings. It has also been documented that modifying the selection of evaluation criteria may change the final position in the ranking, which, in the case of emerging and developing economies, may affect investor assessment and economic growth opportunities. Such considerations are important because they are outside the often uncritical mainstream approach to the assessment of the innovativeness of economies. The conclusions imply the need for a multisource analysis of innovation and a critical look at the proposed methodologies.

A Comparison of Modelling Generalized Extreme Value Distribution of Rainfall Volume in Eastern Thailand Provinces

A Comparison of Modelling Generalized Extreme Value Distribution of Rainfall Volume in Eastern Thailand Provinces

Unravelling the complex interplay between daily and sub-daily rainfall extremes in different climates

Understanding short-duration intense rainfall is crucial for mitigating flash floods, landslides, soil erosion, and pollution incidents. Yet, most observations from rain gauges are only available at the daily resolution. We use the new Global Sub Daily Rainfall dataset to explore extreme rainfall at both daily and sub-daily durations worldwide. Employing Single Gauge Analysis (SGA) and pioneering global-scale Regional Frequency Analysis (RFA), we reveal for the first time how Generalized Extreme Value Distribution (GEV) parameters change across climates and data durations (1h, 3h, 6h, 24h, and daily). This marks the first-ever near-global-scale RFA, made possible by the development of an algorithm that automates RFA on observed rainfall datasets. We compare our results with GEV applied to a gridded rainfall reanalysis (ERA5). Our key findings are that: 1) using ERA5, return levels are significantly underestimated across all climates for 1h rainfall and across all data durations for gauges in the tropical climate region. Even when accounting for differences between point and areal estimates, the median 1h return level estimates are approximately 40% lower compared to RFA. We therefore strongly advise against the use of reanalysis gridded rainfall for studying these extremes. 2) While most gauges show similar return levels with RFA or SGA, some differ significantly, and either method may yield the highest values. Thus, we strongly recommend using both SGA and RFA simultaneously to estimate return levels for a robust risk assessment in flood infrastructure design. 3) The interaction between daily and sub-daily GEV shape parameters varies across climate regions, rendering a universal method for inferring sub-daily rainfall extremes from daily extremes (e.g., using Intensity-Duration-Frequency curves) impractical. Our research provides innovative methodological insights that warrant consideration in future studies on rainfall extremes. Our results not only benefit local stakeholders globally but also serve as a crucial validation tool for the rising number of convection-permitting climate model experiments conducted worldwide.

Hydrodynamic exposure – on the quest to deriving quantitative metrics for mariculture sites

This work attempts to define metrics for hydrodynamic exposure, using known oceanographic variables to provide a universal site assessment method for mariculture structures. Understanding environmental conditions driving open-ocean mariculture siting is crucial in establishing consistent ocean governance, minimizing adverse environmental impacts, and facilitating economically sustainable farm operations. To provide a metric of oceanic conditions and associated requirements for structural design and operation of aquaculture systems, six Exposure Indices (EI) are proposed that consider physical energy levels related to hydrodynamic forces at a site. Four of the proposed indices consider only environmental conditions, while the other two also consider the dimensions of the gear that is exposed to the external loads. These indices are: Exposure Velocity (EV), Exposure Velocity at Reference Depth (EVRD), Specific Exposure Energy (SEE), Depth-integrated Energy Flux (DEF), Structure-centered Depth-integrated Energy (SDE), and a Structure-centered Drag-to-Buoyancy Ratio (SDBR). While these indices are derived with a focus on aquaculture structures, they may also have applications for estimating biological stressors and operational challenges. The proposed exposure indices were evaluated for a range of known aquaculture sites around the world. A sensitivity analysis was conducted that quantified the relationship between the exposure indices and storm event return period. At a regional scale, hindcast numerical data for the German Bight combined with calculations of 50-year extreme values were used to calculate and map each proposed index spatially. Resulting maps showed that exposure is not simply a function of distance from shore. The six indices show plausible performance regarding the objective assessment of aquaculture sites. The authors herein present the indices to the aquaculture and ocean engineering communities for discussion, application, and potential adoption of one or more of the proposed indices.

Predicting the Risk of Adverse Neonatal Outcomes in Women With Insulin-Treated Diabetes: Is It Time for a Pregnancy-Specific Glycemic Risk Index (GRI)?

The Glycemia Risk Index (GRI) describes the quality of glycemic control, emphasizing extreme hypoglycemia and hyperglycemia more than less extreme values. However, a pregnancy-specific GRI (pGRI), tailored to the tighter target glucose range required during pregnancy, has not been established. We retrospectively evaluated clinical, metabolic, and Continuous Glucose Monitoring (CGM) data across pregnancy in women with insulin-treated diabetes, managed between September 2021 and March 2024 at the University Hospital of Pisa. First and second levels of hyperglycemia (TAR1: 140-180 mg/dL, TAR2: >180 mg/dL) and hypoglycemia (TBR1: 63-54 mg/dL, TBR2: <54 mg/dL) were used to calculate the pGRI at each trimester. Logistic regression analysis investigated the association between pGRI and risk of at least one adverse neonatal outcome (among preterm delivery, macrosomia, large for gestational age, small for gestational age, neonatal hypoglycemia, neonatal jaundice, and neonatal intensive care unit admission). Of 45 pregnant women, 25 (56%) experienced at least one adverse neonatal outcome. In the third trimester, women with adverse outcomes had significantly higher total TAR (26 [12-32]% vs 10 [4-23]%, P = .018) and lower TIR (71 [64-83]% vs 88 [75-92]%, P = .007). Specifically, the difference was notable in TAR2 (6 [2-15]% vs 1 [0-4]%, P = .004), whereas TAR1 was comparable between the 2 groups. Accordingly, third trimester pGRI was higher in women with adverse neonatal outcomes (38 [18-49]% vs 18 [10-31]%, P = .013) and, at logistic regression, slightly but significantly increased the risk of adverse neonatal outcomes (1.044 [1.004-1.086], P = .024). Pregnant women with insulin-treated diabetes reporting adverse neonatal outcomes spent more time in hyperglycemia, particularly in extreme hyperglycemia. Therefore, the level of hyperglycemia should always be assessed during pregnancy. The pGRI, emphasizing extreme hyperglycemia, may be a novel comprehensive tool for assessing the risk of adverse neonatal outcomes.

Utilizing statistical distribution tests to develop rainfall intensity–duration–frequency curves for enhanced hydrological analysis in Kirkuk city, Iraq

ABSTRACT Rainfall intensity is considered one of the basic factors in designing hydrological models based on rainstorm data. The objective of this research is to employ novel intensity–duration–frequency curves and develop empirical equations for rainfall intensity in the city of Kirkuk. The reduction formula adopted by the Indian Meteorological Department was used to divide the maximum daily rainfall for short periods of 0.5, 1, 2, 4, 6, 12, and 24 h. Three key methods of frequency analysis (lognormal distribution, log Pearson type III distribution, and Gumbel extreme value distribution) were utilized to formulate a statistical relationship based on rainfall intensity data from between the years 1981 and 2023 for a gauging station upstream of the Kirkuk city basin to provide the best data set for all periods of 100, 50, 25, 10, 5, and 2 years. Research has shown that the logarithmic distribution is the best fit for modelling the relationship between the annual maximum rainfall at Kirkuk station and duration of the rainfall. The goodness-of-fit results indicate that the lognormal distribution statistically outperforms other distribution models. Hence, the generated rainfall intensity, duration, and frequency curves that were developed led to estimating the intensity of precipitation to build forecasting and hydrological behaviour of the Kirkuk city basin.

Hydro-informer: a deep learning model for accurate water level and flood predictions

AbstractThis study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting. Utilizing a comprehensive dataset from the Slovak Hydrometeorological Institute SHMI (2008–2020), which includes precipitation, water level, and discharge data, the model was trained using a ladder technique with a custom loss function to enhance focus on extreme values. The architecture integrates Recurrent and Convolutional Neural Networks (RNN, CNN), and Multi-Head Attention layers. Hydro-Informer achieved significant performance, with a Coefficient of Determination (R2) of 0.88, effectively predicting extreme water levels 12 h in advance in a river environment free from human regulation and structures. The model’s strong performance in identifying extreme events highlights its potential for enhancing flood management and disaster preparedness. By integrating with diverse data sources, the model can be used to develop a well-functioning warning system to mitigate flood impacts. This work proposes a novel architecture suitable for locations without water regulation structures.

Cross-cultural adaptation of the Czech version of the core outcome measures index for low back and neck pain.

The Core Outcome Measures Index (COMI) is a short, multidimensional instrument translated into several languages that covers five domains recommended in the assessment of outcome in patients with low-back and neck pain. The purpose of this study was to cross-culturally adapt the COMI from English to Czech language and to test the face and construct validity and reproducibility of its results in patients with low-back and neck pain. Participants (n = 125) were included from primary and secondary care. The participants reported moderate pain and disability levels. All participants filled in the COMI forms before and after surgery. Descriptive statistics, Wilcoxon paired test, Crombach's alpha, principal component analysis and information entropy calculation were used. The instrument was successfully forward and back-translated. It can be seen that the questionnaire applied as part of our intervention study produces answers with a sufficient degree of variability and with a satisfactory degree of representation of extreme values. It can be also seen that the questionnaire can diagnose an objectively occurring change associated with the surgeon within the intervention procedure. Our other findings support the idea of ​​a possible reduction in the number of questions that measure the same latent variable. Our investigations also showed that it is possible to reduce the range of the point scale of the perception of pain to 5 degrees of intensity and thus unify the range with the other questions. The Czech COMI shows acceptable properties and is thus suitable to use as a short instrument for measuring important domains in patients with low-back and neck pain.

Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models

The surge in e-commerce during the past decade has led to dramatic changes in consumer shopping behaviour. The study applies two Generalized Extreme Value (GEV) family models to investigate households' e-shopping demands. The study proposes a model structure to jointly model ordinal-based choice behaviour with choice-makers' latent class membership. Introducing latent class structure with the OGEV formulation accounts for the relationship between choice-makers heterogeneous preferential groups and their ordinal choice outcomes. Furthermore, the study also applies the Ordered General Extreme Value (OGEV)-Negative Binomial (NB) model, capturing the interplay between consumers' in-store shopping demands and online shopping behaviour. The RUM principle inherited within the OGEV-NB model allows econometric valuation of in-store shopping activity explicitly considering households' e-shopping demands. Both models are empirically estimated using a dataset collected in the Greater Toronto Area (GTA), Canada. The empirical findings and behavioural implications are also discussed.

On differentiability and mass distributions of typical bivariate copulas

Despite the fact that copulas are commonly considered as analytically smooth/regular objects, derivatives of copulas have to be handled with care. Triggered by a recently published result characterizing multivariate copulas via (d−1)-increasingness of their partial derivative we study the bivariate setting in detail and show that the set of non-differentiability points of a copula may be quite large. We first construct examples of copulas C whose first partial derivative ∂1C(x,y) is pathological in the sense that for almost every x∈(0,1) it does not exist on a dense subset of y∈(0,1), and then show that the family of these copulas is dense. Since in commonly considered subfamilies more regularity might be typical, we then focus on bivariate Extreme Value copulas (EVCs) and show that a topologically typical EVC is not absolutely continuous but has degenerated discrete component, implying that in this class typically ∂1C(x,y) exists in full (0,1)2.Considering that regularity of copulas is closely related to their mass distributions we then study mass distributions of topologically typical copulas and prove the surprising fact that topologically typical bivariate copulas are mutually completely dependent with full support. Furthermore, we use the characterization of EVCs in terms of their associated Pickands dependence measures ϑ on [0,1], show that regularity of ϑ carries over to the corresponding EVC and prove that the subfamily of all EVCs whose absolutely continuous, discrete and singular component has full support is dense in the class of all EVCs.

Effect of Horizontal Continuous Casting Parameters on Cyclic Macrosegregation, Microstructure, and Properties of High-Strength Cu–Mg Alloy Cast Rod

AbstractThe objective of this paper is to present the effect of horizontal continuous casting parameters on macrosegregation and its effect on microstructures, textures, mechanical properties, and electrical resistivity of Cu–Mg alloy rod. By increasing the pulling distance and intermittent extraction time, chemical macrosegregation was observed along the longitudinal section of the cast rod, which reached a distance of 40 mm. Three areas were identified along the length of the material, especially at high pulling distance: with decreasing, quasi-stable, and increasing Mg concentration area. As the pulling distance increased, the difference in the observed extreme values of Mg concentration increased. Two distinct zones of different grain size were also observed. The first zone was formed of large columnar grains with a preferred direction of solidification $$\langle 100\rangle$$ ⟨ 100 ⟩ inclined to the axis of the wire, followed by a second zone formed of small grains of random texture. Consequently, difference in resistivity occurred from one zone to another and the resistivity increased from 37.5 to 41 nΩm. Likewise, the hardness varied between the values of 90 up and 110 Hv. The most homogenous chemical composition was obtained with up to 6 mm of pulling distance and up to 3 seconds of intermittent extraction time.

A new Bayesian method for estimation of value at risk and conditional value at risk

AbstractValue at Risk (VaR) and Conditional Value at Risk (CVaR) have become the most popular measures of market risk in Financial and Insurance fields. However, the estimation of both risk measures is challenging, because it requires the knowledge of the tail of the distribution. Therefore, Extreme Value Theory initially seemed to be one of the best tools for this kind of problems, because using peaks-over-threshold method, we can assume the tail data approximately follow a Generalized Pareto distribution (GPD). The main objection to its use is that it only employs observations over the threshold, which are usually scarce. With the aim of improving the inference process, we propose a new Bayesian method that computes estimates built with all the information available. Informative prior Bayesian (IPB) method employs the existing relations between the parameters of the loss distribution and the parameters of the GPD that models the tail data to define informative priors in order to perform Metropolis–Hastings algorithm. We show how to apply IPB when the distribution of the observations is Exponential, stable or Gamma, to make inference and predictions. .Afterwards, we perform a thorough simulation study to compare the accuracy and precision of the estimates computed by IPB and the most employed methods to estimate VaR and CVaR. Results show that IPB provides the most accurate, precise and least biased estimates, especially when there are very few tail data. Finally, data from two real examples are analysed to show the practical application of the method.

Dynamic Response Prediction Model for Jack-Up Platform Pile Legs Based on Random Forest Algorithm

Jack-up offshore platforms are widely used in many fields, and it is of great importance to quickly and accurately predict the dynamic response of platform pile leg structures in real time. The current analytical techniques are founded upon numerical modelling of the platform structure. Although these methods can be used to accurately analyze the dynamic response of the platform, they require a large quantity of computational resources and cannot meet the requirements of real-time prediction. A predictive model for the dynamic response of the pile leg of a jack-up platform based on the random forest algorithm is proposed. Firstly, a pile leg dynamic response database is established based on high-fidelity numerical model simulation calculations. The data are subjected to cleaning and dimensional reduction in order to facilitate the training of the random forest model. Cross-checking and Bayesian optimization algorithms are used for the selection of random forest parameters. The results show that the prediction model is capable of outputting response results for new environmental load inputs within a few milliseconds, and the prediction results remain highly accurate and perform well at extreme values.

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Analysis of Extreme Daily Rainfall in Cotonou, Benin (West Africa)

Global warming has a global impact on the water cycle and in particular on precipitation. This work is devoted to the analysis of the variability of extreme rainfall in the economic capital of Benin. For this purpose, the 58-year daily rainfall data collected at the Meteo Benin synoptic station in Cotonou between 1960 and 2018 are used. The annual daily maxima was established for the analysis. The generalized distribution of extreme values ​​(GEV) is used to estimate the quantiles according to the defined return periods. The daily heavy rainfall had a normal character with return periods of less than six years in Cotonou, between 1960 and 2018. There was a marginal increase in frequency of extreme rainfall since the beginning of 21st century. Extreme rainfall events may lead to floods as seen in 2010. Therefore, pro-active short- and long-term measures are the need of the hour to cope up against rainfall variability under the global warming scenario.

A Study of Free Surface Agitation in a Shipyard Using Numerical Modeling

In recent years, the Port of Topolobampo, Sinaloa, Mexico, has experienced unusual free sea surface elevations, particularly during the months of November and December, affecting the shipyard areas, service docks, and berthing locations. This study focuses on analyzing the oscillatory behavior of free surface elevations in shipyard regions. A hydrodynamic model was employed to simulate the circulation and sea surface agitation, aiming to quantify the elevation magnitudes based on oceanographic and meteorological data from November of the preceding year. A 30-day numerical simulation was conducted, revealing the velocity fields associated with coastal currents and tides during November, as well as the interaction between incident waves and wave transformations due to protective structures. The results demonstrated accurate behavior in 95% of the simulation period, while anomalous elevations exceeding those specified in the design and operational guidelines of the Port of Topolobampo were observed during the final five days of the simulation. An ANOVA test was performed between the surface elevation and vertically integrated velocity to assess whether the deviations in the last five days were statistically significant compared to the rest of the simulation period. With a P-value of less than 0.05, the null hypothesis of no difference was rejected, confirming a significant variation. These findings suggest that the extreme values recorded should be considered for the potential redesign of shipyard infrastructure.

Expectation generation and its effect on subsequent pain and visual perception.

Bayesian accounts of perception, such as predictive processing, suggest that perceptions integrate expectations and sensory experience, and thus assimilate to expected values. Furthermore, more precise expectations should have stronger influences on perception. We tested these hypotheses in a paradigm that manipulates both the mean value and the precision of cues within-person. Forty-five participants observed cues-presented as ratings from 10 previous participants-with varying cue means, variances (precision), and skewness across trials. Participants reported expectations regarding the painfulness of thermal stimuli or the visual contrast of flickering checkerboards. Subsequently, similar cues were each followed by a visual or noxious thermal stimulus. While perceptions assimilated to expected values in both modalities, cues' precision mainly affected visual ratings. Furthermore, behavioral and computational models revealed that expectations were biased towards extreme values in both modalities, and towards low-pain cues specifically. fMRI analysis revealed that the cues affected systems related to higher-level affective and cognitive processes-including assimilation to the cue mean in a neuromarker of endogenous contributions to pain and in the nucleus accumbens, and activity consistent with aversive prediction-error-like encoding in the periaqueductal gray during pain perception-but not systems related to early perceptual processing. Our findings suggest that predictive processing theories should be combined with mechanisms such as selective attention to better fit empirical findings, and that expectation generation and its perceptual effects are mostly modality-specific and operate on higher-level processes rather than early perception.

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&amp;DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&amp;DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.