Automatic Gauge Research Articles

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Integrated AGC Approach for Balancing the Thickness Dynamic Response and Shape Condition of a Hot Strip Rolling Control System

Thickness and shape are two significant indices in the tandem hot strip rolling process. Excessive thickness correction can cause rolling force imbalance, leading to deteriorated shape conditions. Aiming at this problem, we propose a new monitor automatic gauge control (M-AGC) with consideration of the rolling force distribution to not only keep the shape condition but also improve the robustness of the rolling process. First, the M-AGC with the Smith predictor control structure was studied. Second, based on the rolling force and thickness correction redistribution algorithm, shape balance automatic gauge control (SB-AGC), the phenomenon of rolling force imbalance was improved. Third, the transient response was improved by adding a command prefilter to the upstream stands, which is asynchronous shape balance automatic gauge control (ASB-AGC). The proposed algorithms can be applied to all steel grades and are easy to implement. Finally, cross-validation was conducted through numerical simulations and application results, confirming the alignment between the theoretical derivation and practical implementation. The simulation results show improvements in both the rolling force balance and the thickness response. The practical application results also confirm that better flatness conditions and thickness response can be achieved, significantly reducing the amount of head-end cutoff losses.

Computer vision and deep transfer learning for automatic gauge reading detection

This manuscript proposes an automatic reading detection system for an analogue gauge using a combination of deep learning, machine learning, and image processing. The study suggests image-processing techniques in manual analogue gauge reading that include generating readings for the image to provide supervised data to address difficulties in unsupervised data in gauges and to achieve better accuracy using DenseNet 169 compared to other approaches. The model uses artificial intelligence to automate reading detection using deep transfer learning models like DenseNet 169, InceptionNet V3, and VGG19. The models were trained using 1011 labeled pictures, 9 classes, and readings from 0 to 8. The VGG19 model exhibits a high training precision of 97.00% but a comparatively lower testing precision of 75.00%, indicating the possibility of overfitting. On the other hand, InceptionNet V3 demonstrates consistent precision across both datasets, but DenseNet 169 surpasses other models in terms of precision and generalization capabilities.

Development of an Automatic Portable Calibrator for Tipping Bucket Rain Gauge (TBRG) Using a Load Cell and Simple Water Sensor

The tipping bucket rain gauge is an automatic rain gauge that can measure rain intensity in mm/hour. Accurate measurement of rain intensity requires calibration of the rain gauge to ensure traceability. Calibration of a tipping bucket rain gauge is related to the volume of water as a calibration medium and time so as to produce a discharge that can be converted into rain intensity. Technological developments can make tipping bucket rain gauge calibration automatic by utilizing an Arduino Mega2560 microcontroller, a load cell and a simple water sensor based on the LM393 comparator IC, and combined with a compact design printed using a three-dimensional printer so that it can be used for ex-situ calibration. The data produced by this tool can be downloaded via the application that was built and produces a file in Excel form.

IMERG BraMaL: An improved gridded monthly rainfall product for Brazil based on satellite‐based IMERG estimates and machine learning techniques

AbstractPrecipitation is one of the main components of the hydrological cycle and its precise quantification is fundamental to providing information for the understanding and prediction of physical processes. Precipitation observations based on ground‐based devices (manual and automatic rain gauges) are highly accurate but have limited spatial coverage. On the other hand, remote sensing products cover large areas but with lower accuracy. In this context, this study aims to provide a more accurate monthly precipitation estimating product, with lower latency than other products but without directly relying on field data. The methodology consists of applying a machine learning method (k‐nearest neighbours algorithm) to satellite‐based remote sensing data (IMERG Early Run product) and re‐analysis‐based (MERRA‐2) variables with a particular connection to precipitation. The method was applied over the Brazilian territory, which features a large range of precipitation regimes. This methodology resulted in the development of an adjusted IMERG product (IMERG BraMaL). Compared with the original IMERG products (Early Run and Final Run), IMERG BraMaL has improved the evaluated metrics between ground‐based and satellite data in almost all analyses. For instance, KGE (Kling‐Gupta efficiency) went from lower values (0.70 and 0.82 for Early and Late Run, respectively) to values above 0.86 in the IMERG BraMaL. The adjusted product also presented superior performance statistics compared with other global precipitation products (CHIRPS, PERSIANN‐CDR, and MSWEP). The main advantages of IMERG BraMaL compared with IMERG Final Run are (i) much faster availability to the end‐users; (ii) non‐dependency on any field data, allowing its application in areas where rain gauge data is unavailable or of low quality; (iii) the non‐relationship of errors to local features; and (iv) the much‐improved estimations in regions in Brazil where, historically, satellite‐based products usually underestimate the observed data.

Stability and stabilization for the hydraulic automatic gauge control system of cold rolling mill with time delay and saturation

This paper studies the stability and stabilization of hydraulic automatic gauge control (HAGC) systems with time-varying delay and actuator saturation. First, a mathematical model of HAGC systems is established considering the measurement delay and saturation factors. With the help of state transformation, the measurement delay is transformed into input delay and the generalized sector condition is adopted to deal with the saturation problem in HAGC systems. Then, a new augmented L–K functional is constructed to contain more delay information to reduce the conservativeness of the stability criteria. Furthermore, by combining the proposed L–K functional with some advanced inequalities, the stability conditions and controller are derived with the aid of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed control algorithm is verified by simulation results.

Evaluation of GSMaP Rainfall Data in the Aceh Region

Rainfall measurement data in the field is quite limited, however, satellite data obtained by remote sensing methods are widely available. However, its accuracy must still be tested using observational rainfall data. The objective of this research was to validate GSMaP satellite estimated rainfall data with Automatic Rain Gauge (ARG) rainfall data at rain stations dispersed over Aceh Province's west-central region, as well as to identify the primary drivers for GSMaP data error. ARG and GSMaP data sources. A comparison of GSMaP data accuracy using a combination of Pearson and RMSE tests shows that GSMaP performance is very low in the dry and transition seasons and significantly better in the rainy season. GSMaP, on the other hand, is still unable to offer sufficient precise rainfall estimation data for each season. Meanwhile, a comparison by elevation shows that GSMaP data is not fully reliable.

An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-rolled strips significantly influence the thickness accuracy in strip cold rolling, the traditional deformation resistance prediction model and thickness control mode don't involve that. Thus, based on the Industrial Internet of Things (IIOT) platform and data-driven technology, this work proposed a new method for deformation resistance prediction and a new mode of feed-forward control for strip thickness. IIOT platform can collect production data from the various levels to provide a complete data set. The deformation resistance prediction model is established by adopting the differential evolutionary algorithm to optimize the bi-directional long and short-term memory network. In addition, the feed-forward control (DFFC) strategy for strip thickness is proposed based on deformation resistance prediction. An application results of a 1420 mm production line of CR indicated that the established prediction model could provide a high accuracy prediction for deformation resistance, and compared to traditional thickness gauges-based automatic gauge control methods, the DDFC can capture the effect of the fluctuations of deformation resistance and thickness for the hot-rolled strip to improve thickness accuracy effectively.

Rainfall threshold for landslide awareness – Focusing on the case study in the landslide EVO pilot area region in western Nepal

Nepal’s rugged topography, unstable young geological formations, and fragile rocks make the country highly vulnerable to water-induced hazards such as landslides, soil erosion, and debris torrents. Hilly watersheds and settlements in hills and river banks are naturally vulnerable during heavy rainfall. The landslide EVO project selected two landslide areas, one the Bajedi landslides in the Bajura district, and another Sunkuda landslides in Bajhang district of Nepal. Automatic rain gauges were installed, and data were recorded for 2019 and 2021. The best-fit trend lines are determined by the observed rainfall depths of different durations. In addition, 24-hour rainfall records and landslide events that occurred in the region outside the pilot areas in the year 2019 were also analyzed and correlated. Rainfall intensities and depths corresponding to maximum, minimum, and average depth are correlated for different durations. The correlation between rainfall depths and durations data showed an excellent fitting observed. The trend line is considered as the rainfall threshold line for landslide risk assessment for the region.

Validating IMERG data for diurnal rainfall analysis across the Indonesian maritime continent using gauge observations

The availability of surface rainfall data with high spatial-temporal resolution is needed to understand the diurnal rainfall characteristics in the Indonesian Maritime Continent (IMC) to improve the accuracy of weather and climate models in this region. This study validates the accuracy of the Final Run product of IMERG data version 06B (V06B) and version 07A (V07A), which have a resolution of 0.1°–30 min for diurnal rainfall analysis over IMC. Validation was conducted for precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI), by recording 302 automatic rain gauges (RG) every 10 min from January 2014 to September 2021. IMERG V06B and V07A perform well in observing diurnal PA and PF but struggle in observing diurnal PI compared to RG observations. This has been determined by the correlation coefficient (CC) values of IMERG V06B (V07) data, which are 0.76 (0.72) for PA, 0.77 (0.76) for PF, and 0.21 (0.13) for PI. The IMERG data align to an extent with RG observations for the PA value, having a small relative bias (RB). The results also display a systematic PF and PI values error. The high false alarm ratio (FAR) of IMERG data suggests rain detection errors, leading to overestimated PF values. Additionally, the underestimation of PI values is due to the limitation of IMERG data in observing extreme and small-scale rain events. About 86.10% (78.14%) and 81.45% (81.78) of IMERG V06B (IMERG V07A) data show a peak time difference of <3h for PA and PF when compared with RG observations. Overall, IMERG V06B performs better than V07, possibly due to inaccurate orbits of GPROF data, removal of SAPHIR satellite observations, and inter-calibration issues with CORRA and GPCP data. Continuous monitoring and data improvements are necessary to improve the accuracy and reliability of IMERG data in detecting diurnal rainfall patterns in the IMC region.

Cryosphere–Hydrometeorology Observations for a Water Tower Unit on the Tibetan Plateau Using the BeiDou-3 Navigation Satellite System

Abstract Life and civilization in arid regions depend on the availability of freshwater. Arid alpine river basins, where hydrological processes are highly sensitive to rapid warming, act as vital water towers for lowland oases. However, scientific understanding of precipitation variability and related cryosphere–hydrology processes is extremely limited because of the scarcity of in situ observations. The upper Danghe River basin (UDB; ∼14,000 km2) is an arid and westerly dominated basin on the northeastern Tibetan Plateau and is the water source for the Dunhuang Oasis in China. We have established a comprehensive cryosphere–hydrometeorology observation network in the basin since 2014. At present, the network consists of 21 automatic rain gauges, 22 soil freeze–thaw monitoring stations, 4 automatic weather stations (AWS), and a 50-m gradient meteorological tower with an eddy covariance system. In particular, the 18 sites, located in remote areas without public networks, are equipped with new-generation BeiDou-3 communication terminals that enable the observations to be easily, safely, and reliably read and quality controlled in near–real time from offices in the city or at home. This integrated observation network over the UDB that facilitates the monitoring of cryosphere–hydrology processes, land–atmosphere interactions, and local weather processes. In addition, the observations are helpful for the objective evaluation, and continual improvement, of hydrological models, satellite-retrieval products, and reanalysis datasets. Finally, the network is expected to promote a better understanding of the status and role of water towers in arid zones and to provide basic data support for the sustainable development of the Dunhuang Oasis and the Belt and Road.

Comparative estimation of a random error component of automatic well level gauges based on processing measurements of earth-tidal level of groundwater

Relevance. Introduction of autonomous digital level gauges allows significantly expanding the range of tasks solved in various fields of geology and ecology, by increasing the frequency of measurements and applying mathematical processing methods to avoid distortions of different nature. The expansion of the field of application of downhole level meters makes it a topical issue to choose a particular type of device to ensure the required measurement accuracy. Aim. To consider the approaches to comparative estimation of random error of several types of automatic level gauges based on their recording of earth-tidal variations of groundwater level. Methods. Methods of compensating the influence of variations in atmospheric pressure on groundwater levels; methods of calculating tidal level variations; methods of correlation and frequency analysis of the calculated and experimental data. Results. The authors have carried out a comparative assessment of the accuracy in readings of several digital level gauges, installed in one well, by comparing the measured values of earth-tidal variations of the groundwater level with the calculated ones. Based on the experimental data, tidal variations of the groundwater level were obtained by eliminating distortions from variations in atmospheric pressure and then filtering with the moving mean. From correlation and amplitude-frequency analysis of the calculated and experimental data, indicators were determined for making a comparative assessment of a random component of the error of the tested level gauges. Conclusions. The approach suggested allows choosing independent digital level gauges with a minimum random error component using the totality of calculated parameters. The most promising way to eliminate the distorting effects of tidal variation of groundwater level is the method based on the cutter frequency filtering of the level signal measured by the level gauge.

Development of Experimental Low-Cost Rain Gauges and their Evaluation During a High Intensity Storm Event

The measurement of rainfall via ground sensors is fundamental in a variety of hydrological applications, including rainfall-runoff simulations, basin water balance and flood forecasting. The tipping bucket rain gauge (TBR) constitutes the most common type of automatic gauge for the measurement of rainfall intensity. The objective of this work is the development of low-cost and reliable rain gauges, including their data logger, which could be installed at remote, rural areas, in order to supplement with rainfall data the limited or non-existing network of hydrological stations. To achieve this target, two experimental TBRs with diameters of 20 cm (RG20) and 28 cm (RG28) were developed. Electronic boards Arduino UNO and Raspberry Pi were used for their data logger. The measurements of RG20 and RG28 were compared with those of a high quality rain gauge ARG100 and a daily non-recording rain gauge. The cyclone Daniel on 06-09-2023 caused an intermittent storm event in the city of Athens, Greece, which was measured by all three TBRs for purposes of evaluation. The results showed that the variations between ARG100 and RG28 were lower than 6%, while the variations between ARG100 and RG20 were about 10% during a few time intervals of high rainfall intensity. The return period of the storm event were estimated at 43, 59 and 45 years for rainfall durations of 10, 20 and 30 min, respectively.

RANCANG BANGUN INSTRUMEN PENGUKUR PASANG SURUT OTOMATIS PORTABEL (P2SOP) BERBASIS SISTEM ARDUINO DAN DATA LOGGER

The design of a portable automatic tide gauge instrument (P2SOP) based on the arduino system and data logger is an activity of designing and implementing an instrument that can measure sea level fluctuations during tides using an integrated mechatronics system between mechanical bodies, electronic systems in the form of input systems, processing system, output system in the form of an LCD (liquid crystal display) display and in the form of a data logger and a controlled program system. This P2SOP instrument has a mechanical body that functions as a shelter for electronic components and as a wave ripple damper so that tide level fluctuations are actually not affected by wind wave ripples. In the electronics system section, the input section uses the HC SR04 sensor, the processing system uses an integrated Arduino Uno board system and an RTC DS3231 timer system, for the output itself to be displayed in the form of an LCD (liquid crystal display) and stored in the Secure Digital Card module in the form of a data logger. It is hoped that with this P2SOP instrument in the future tide measurements can be carried out in an automated manner and minimize the occurrence of inaccurate measurement times and reading errors on manual tide measurements.

Evaluasi Spasial Estimasi Curah Hujan pada Radar Cuaca Menggunakan Metode Z-R Marshall-Palmer di Wilayah Jawa Barat

Rainfall is one of the weather parameters that affect various sectors. High rainfall intensity can trigger hydrometeorological disasters, so rainfall observation data is vital to monitor rainfall conditions in an area. An automatic rain gauge is an instrument that measures rainfall at an observation point, but the instrument has reasonably low coverage and has yet to reach the entire region. Weather radar is a remote sensing instrument capable of spatially estimating rainfall. Weather radar data can be used to estimate rainfall using the Marshall-Palmer Z-R method. The application of the method can be an alternative for areas that do not have rainfall observation equipment. However, the estimation needs to be evaluated to improve the accuracy of the estimation value. Based on the evaluation, the highest coefficient of determination was 0.92, and the lowest was 0.67. The lowest RMSE value was 2.40, the highest was 6.76, the highest ME value was 16.59, and the lowest was 5.93; the highest bias was 12.90, and the lowest was 5.30. The study results show that the weather radar can operate according to the specifications of the maximum observation distance of up to 220 KM, but the farther the observation distance to a point, the higher the performance of rainfall estimation accuracy.

Evaluating the Performance of Satellite Derived Temperature and Precipitation Datasets in Ecuador

Temperature and precipitation data are crucial for hydrology and meteorology. In 2014, Ecuador started an automatic gauge network which monitors these variables. The measurements are not publicly available. Global gridded datasets from numerical models and remote sensors were previously the only way to obtain measurements for temperature and precipitation. Now that in situ measurements are beginning to be available in significant quantities, we assessed the performance of IMERG, CHIRPS, GLDAS and ERA5 for both temperature and precipitation using the in situ data. We used the Pearson R correlation coefficient, ME (Mean Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). We found that global gridded data were more suited for determining averages over time rather than for giving exact values at specific times for in situ gauges. The Pearson R values increased for all datasets when we used monthly aggregations in place of daily aggregations, suggesting that the monthly values are more correlated than the daily. The Pearson R value for temperature increased from 0.158 to 0.719 for the ERA5 dataset. Additionally, we show the statistical values for each of the three regions in Ecuador. We found that the IMERG and CHIRPS datasets, which contain station data, performed significantly better for both RMSE and MAE. Both IMERG and CHIRPS have a RMSE value a little over 260, whereas ERA5 and GLDAS had values over 300. We discuss the short comings of these datasets as being related to their relatively coarse resolution, lack of in situ data in Ecuador to calibrate against, and the rapidly varying terrain of Ecuador. We recommend using higher temporal and spatial resolution datasets for immediate applications. We recommend repeating this analysis in the future when more automatic gauges and longer time periods are available to facilitate a more detailed analysis which is presently not possible.

Combined a Pressure Sensor and Internet of Things in a Low-Cost Tide Gauge

Tides are important in many fields, such as port planning, nautical charting, and bathymetry surveys. However, the distribution of tide observation stations in Indonesia is still uneven, so simple tidal data collection still uses tide palms to observe the movement of sea levels. The manual tidal observation method has limitations because visually the ability of the observer to see the tide reading is different, so it is subjective. Therefore, the solution to this problem is a tidal instrument that is easy to make, provides real-time data, and produces accurate information, saving energy and time. However, using automatic tide gauge instruments is still not affordable in the Indonesian market. This study creates a low-cost pressure tide gauge by integrating a pressure sensor with an Arduino microcontroller. In addition, the data monitoring process uses the Internet of Things system. Data from the instrument measurements were uploaded to the Thingspeak website live, allowing observers to monitor in real time. Instrument’s data with noise and anomalies will go through a data filtering process with the moving average and moving median methods. Testing the instrument’s error rate compares the data generated by the instrument with tidal data from the Indonesian Geospatial Agency (BIG). Both tidal data went through calculating Root Mean Square Error, or RMSE. Based on the instrument error test results, the RMSE value for moving average data against BIG is 27.963 cm and moving median data against BIG is 27.787 cm.

A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station.

As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

Hydromechanics – Slope Monitoring in Rainy Season

Rainfall and soil response are necessary to be monitored to have slope characteristics in detecting landslide occurrence. Even though much research has been carried out worldwide for rainfall monitoring, less research has been conducted in Indonesia for slope monitoring. Therefore, this research was conducted to observe the suction, soil moisture content, and rainfall in a silty sand slope. An automatic rain gauge was set on the ground to measure precipitation. Tensiometer and soil moisture content sensors were installed at depths of 0.5 m; 1 m; and 1.5 m from the slope surface. The monitoring was conducted during the peak rainy season from December 2022 to January 2023. The rainfall amount is about 436.6 mm, and the 6 hours of rainfall events contribute a relatively sizeable rainfall amount (about 31%) to the total. The safety factor of the slope is estimated to decrease by 39%-40% due to the rainfall.

MICROCIRCULATORY PREDICTORS OF THE FORMATION OF PRIMARY ARTERIAL HYPERTENSION IN CHILDHOOD

Introduction. The problem of early diagnosis of arterial hypertension in children is extremely urgent. According to some foreign scientists, the prevalence of this disease among school-age children in the world has been increasing over the past ten years. Children of prepubertal and pubertal age are most prone to the development of arterial hypertension, which is largely determined by vegetative dysfunctions and microcirculation disorders characteristic of these periods of childhood.It has been proven that changes in microcirculation are determined by the variant of arterial hypertension and are a fairly early marker of the degree of damage to target organs.The aim of the study is to study microcirculatory predictors of the formation of primary arterial hypertension in childhood.Material and methods.113 school-age children from the residents of Chernivtsi region and Chernivtsi were examined. The examined children were divided into two groups: the main group consisted of 30 children with arterial hypertension and the control group - 83 children with a normal level of blood pressure.Blood pressure was measured with automatic pressure gauge with removable cuffs, in the morning on both arms, three times with an interval of 2 minutes. Results were evaluated according to percentile tables for age, sex, and height.The recommendations of the American Academy of Pediatrics (AAR) were used to diagnose hypertension in children.Capillaroscopic examination of the nail bed was used to assess the state of peripheral microcirculation in the examined children. Quantitative and qualitative parameters of microcirculation were evaluated.The research was carried out in compliance with the "Rules of Ethical Principles of Conducting Scientific Medical Research with Human Participation" approved by the Declaration of Helsinki (1964-2013), ICH GCP (1996), EU Directive No. 609 (from 24.11.1986), orders of the Ministry of Health of Ukraine No. 690 from 23.09.2009, No. 944 from 14.12.2009, No. 616 from 03.08.2012.Statistical processing of the data was performed using nonparametric methods of evaluation. Mathematical processing of the data was performed using Microsoft Office Excel and Statistica 10.0.The work was performed within the framework of the planned research work of the Department of Pediatrics, Neonatology and Perinatal Medicine of Bukovinian State Medical University: Chronobiological and adaptation aspects and features of autonomic regulation in pathological conditions in children of different age groups, (№ 0122U002245, term of execution 2022 - 2026).Results. When comparing the quantitative capillaroscopic indicators of the nail bed between the groups, it was found that the children from the main group showed probable differences in individual indicators (reduced linear density, greater distance between capillaries, 2 times less number of anastomoses, change in the shape of capillaries) compared to the control group, which indicates on changes in peripheral microcirculation in children with elevated blood pressure.After conducting a correlation analysis of the obtained data, we found correlations between changes in the microcirculation of the nail bed and indicators of the functional state of the cardiovascular system in the form of changes in the linear density of capillaries and the distance between them, the presence of increased tortuosity of capillaries and "sweetening". Thus, the linear density of capillaries had high correlations with the level of systolic blood pressure (r=0.73, p&lt;0.05) and the level of diastolic blood pressure (r=0.69, p&lt;0.05).When conducting a multifactorial analysis, a clear relationship between changes in the indicators of the state of the cardiovascular system, on the one hand, and the capillaries of the nail bed, on the other, can be traced.Conclusions. 1. Microcirculatory disorders in the capillaries of the nail bed reflect the changes in central hemodynamics and may be early signs of arterial hypertension in children.2. According to the results of the epidemiological analysis, the presence of predictors of the development of primary arterial hypertension in children was established. These factors are a reduced number of capillaries per unit area (OR = 2.54; RR = 2.12; 95% CI 2.06-4.98) and their pathological tortuosity (OR = 1.75; RR = 1.44; 95% CI 1.18-2.63).