Search Results (3)

Increasing rainfall intensity and altered temporal patterns due to climate change pose significant threats to pedestrian safety in highly urbanized areas. Reliable pedestrian safety assessment is therefore essential for evacuation planning and flood risk management. This study evaluated pedestrian stability under various rainfall patterns and return periods using four instability indicators derived from hydraulic and empirical formulations. To mitigate indicator-dependent variability, the normalized indicators were combined into an integrated instability index through an ensemble-averaging approach. The flood-intensity-based indicator systematically underestimated non-walkable areas compared with force-balance-based indicators, whereas the integrated index produced more consistent spatial patterns of pedestrian risk across rainfall scenarios. The most hazardous conditions occurred under the 1 h, Huff fourth-quartile storm, highlighting the influence of late-peaking rainfall on short-duration urban flooding. These findings demonstrate that the proposed ensemble-averaged framework enhances the robustness of pedestrian flood risk evaluation and provides a quantitative basis for prioritizing mitigation measures and evacuation planning in urban areas. Full article

Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring. Full article

Thermostatic bath calibration performance is usually checked for uniformity and stability to serve a wide range of industrial applications. Particularly challenging is the assessment at the limiting specification ends where the sensor system may be less effective in achieving consistency. An ensemble of eight sensors is used to test temperature measurement stability at various topological locations in a thermostatic bath (antifreeze) fluid at −20 °C. Eight streaks of temperature data were collected, and the resulting time-series were processed for normality, stationarity, and independence and identical distribution by employing regular statistical inference methods. Moreover, they were evaluated for autoregressive patterns and other underlying trends using classical Auto-Regressive Integrated Moving Average (ARIMA) modeling. In contrast, a continuous-time quantum walker algorithm was implemented, using an available R-package, in order to test the behavior of the fitted coefficients on the probabilistic node transitions of the temperature time series dataset. Tracking the network sequence for persistence and hierarchical mode strength was the objective. The quantum walker approach favoring a network probabilistic framework was posited as a faster way to arrive at simultaneous instability quantifications for all the examined time-series. The quantum walker algorithm may furnish expedient modal information in comparison to the classical ARIMA modeling and in conjunction with several popular stochastic analyzers of time-series stationarity, normality, and data sequence independence of temperature end-of-scale calibration datasets, which are investigated for temporal consistency. Full article