Temporal Anomalies Research Articles

Time-Frequency Domain Monitoring Method for the Fault of HTS HVDC Systems Based on AI Classifiers

Since high-temperature superconductor (HTS) based HVDC systems are affected by temperature and cooling system performance, noise, the monitoring system which can classify the temporal disturbance and anomalies must be developed to ensure stable system operation. To solve this, it needs anomaly detection that accounts for system sensitivity and is able to continuously manage the response to disturbances. In this paper, we propose an integrated solution for fault severity diagnosis and anomaly detection colorred with AI based reflectometry. Anomalies such as the quench phenomenon can be detected through the anomaly score based on the reconstruction error calculated through the proposed autoencoder(AE) method. After anomaly detection, a fault classification algorithm based on the convolutional neural network (CNN) using a 2D image of a converted reflected signal through the proposed image processing was conducted. Based on the signal acquired from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7 m$</tex-math></inline-formula> length 1st generation 22.9 kV/50 MVA HTS cable, PSCAD simulation was utilized to construct a long-distance line model and verified the performance of the proposed algorithm.

Labyrinthine Bifurcation of the Facial Nerve

The labyrinthine bifurcation of the facial nerve is extremely rare. Diverse congenital temporal bone anomalies usually coexist, and a detailed preoperative evaluation is needed to detect them. We report a case of labyrinthine bifurcation of the facial nerve detected on the preoperative evaluation of a patient with congenital aural atresia.

High-risk LEO satellite network path detection based on spatial and temporal delay anomaly analysis

High-risk LEO satellite network path detection based on spatial and temporal delay anomaly analysis

АНАЛИЗ ПРЕДСКАЗУЕМОСТИ СОСТОЯНИЯ АТМОСФЕРЫ И ВЕРХНЕГО СЛОЯ ОКЕАНА НА СРОК ДО 5 ЛЕТ С ПОМОЩЬЮ РЕТРОСПЕКТИВНЫХ ПРОГНОЗОВ МОДЕЛИ КЛИМАТА INMCM5

A series of hindcasts for 1-5 years were performed using the INM RAS climate model (INMCM5). Each forecast started on November 1 every year during 1980-2020. The predictability of large-scale climate variability modes on timescales from a season to 5 years is assessed with the series of INMCM5 hindcasts. The spatial distribution of correlation coefficients of the temporal anomalies of the annual mean ocean water temperature averaged for the depths from 0 to 300 m and correlation coefficients of temporal anomalies of the Arctic sea ice area are calculated using the hindcasts, historical experiments and their extensions according to the SSP3-7.0 scenario of the INM RAS climate model. The contributions of initial conditions and external forcings to the predictability of the anomaly correlation coefficients are evaluated.

Detecting Temporal and Spatial Anomalies in Users’ Activities for Security Provisioning in Computer Networks

Detecting Temporal and Spatial Anomalies in Users’ Activities for Security Provisioning in Computer Networks

Reply to Comments by Kale and Pande on: “Machine-learning based re-classification of the stratigraphy of the Rajahmundry Traps, India”

Hasandağ is a double-peaked long-dormant volcano, where associated volcanic risk is poorly known and needs to be assessed. Weak fumaroles and water vapor emissions have been observed at several vents, on the western flank of the Greater Hasandağ summit, between 3000 and 3100 m (a.s.l.). The highest temperature and CO2 values measured in those vents were ~ 70 °C and > 100,000 ppm respectively. Multisource and wide-period thermal remote sensing analyses were performed to investigate the thermal state and to test the possibilities of remote thermal monitoring of the volcano.MODIS daily surface temperature data were analyzed for a period of 18 years. We used a statistical approach using the Seasonal Decomposition of Time-series procedure to decompose the temperature data into the trend, seasonal, and remainder components. The trend component was used to analyze temporal thermal anomalies in comparison to the decomposed meteorological temperature data. 56 nighttime ASTER TIR imagery acquired between 2001 and 2018 had been used to derive and analyze the Surface temperature, Surface temperature anomaly and Relative radiative heat flux. High-resolution ground-based and aerial thermal mosaic images were built to investigate the thermal radiation over the summit fumarole zone and its structural dependence.A baseline reference value for thermal monitoring of the fumarole zone was achieved using remotely sensed data and time-series decomposition of long-term TIR data. The resolution of the thermal imagery positively affects the mapping and monitoring capability. A permanent ground-based thermal observation station should be established for direct and remote thermal monitoring of the fumarole zone.

Skill improvement of the yearly updated reforecasts in ECMWF S2S prediction from 2016 to 2022

Hazardous weather events are often accompanied by subseasonal processes, but the forecast skills of subseasonal prediction are still limited. To assess the skill improvement of the constantly updated model version in ECMWF subseasonal-seasonal (S2S) prediction from 2016 to 2022, the performance of yearly updated reforecasts was evaluated against ERA5 reanalysis data using the temporal anomaly correlation coefficient (TCC) as a metric. The newly updated reforecasts exhibit stable superiority at the weather scale of the first two weeks, regardless of whether the 2-m temperature or precipitation forecast is being considered. At the subseasonal time scale starting from the third week, some slight improvements in prediction skills are only found in several tropical regions. Generally, the week-3 TCC values averaged over global land grids still reflect an advancement in prediction skills for updated reforecasts. For the Madden–Julian Oscillation (MJO), reforecasts can reproduce the characteristics of eastward propagation, but there are deviations in the intensity and propagation range of convection anomalies for reforecasts of all seven years. Based on an evaluation of MJO prediction skill using the bivariate anomaly correlation coefficient and bivariate root-mean-square error, some differences are apparent in the MJO prediction skills among the updated reforecasts, but the improvements do not increase monotonically year by year. Despite the inherent limitation of S2S prediction, positive progress has already been achieved via the constantly updated S2S prediction in ECMWF, which reinforces the confidence in further collaboratively improving S2S prediction in the future.摘要在2016年至2022年间, ECMWF次季节预测系统不断升级并逐年完成新的回报试验. 本文考察该预测系统逐年升级带来的预测技巧提升潜力. 从2米气温和降水来看, 在起报之后的前两周内天气尺度上预测技巧表现出逐年稳定提升的趋势; 在从第三周开始的次季节时间尺度上, 预测技巧的提升仅限于热带部分区域. MJO预测技巧并不随着模式升级而逐年单调提升. 尽管目前S2S预测技巧存在局限性, 但目前已有的进展增强了在未来深入合作以提高S2S预测技术的信心.

Satiation, habit formation, and other temporal anomalies: Extending the choice theory to multiple neighborhoods of time

The rational choice model under certainty and the discounted utility (DU) and expected utility (EU) models under uncertainty are criticized for their inability to capture many observed temporal behaviors, e.g., satiation, habit formation, and change-of-taste. By considering consumption as an activity, i.e., a combination of goods on the set of prospects X and consumption time on the time set T, this paper shows that the previous temporal anomalies are possible over the extended choice field X × T. In particular, the present framework demonstrates that the same decision maker could have different preference ordering in different psychological states. A key advantage of the present framework over some DU modifications that account for satiation and habit formation is that it is less ambiguous about the time attribute of the decision-making process.

The Wastewater Contamination Index: A methodology to assess the risk of wastewater contamination from satellite-derived water quality indicators

One of the major sources of pollution affecting inland and coastal waters is related to poorly treated or untreated wastewater discharge, particularly in urbanized watersheds. The excess of nutrients, organic matter, and pathogens causes an overall deterioration of water quality and impairs valuable ecosystem services. The detection of wastewater pollution is essential for the sustainable management of inland and coastal waters, and remote sensing has the capability of monitoring wastewater contamination at extended spatial scales and repeated frequencies. This study employed satellite-derived water quality indicators and spatiotemporal analysis to assess the risk of wastewater contamination in Conceição Lagoon, a coastal lagoon in Southern Brazil. Using an analytical model, three water quality indicators were derived from Level 2A Sentinel-2 MSI images: the absorption coefficients of chlorophyll-a and detritus combined with coloured dissolved organic matter, and the backscattering coefficient of suspended solids. The temporal standardized anomalies were calculated for each water quality indicator for the period of 2019–2021, and their anomalies during a known outfall event were used to evaluate spatial variation modes. The spatial mode explaining most of the variability was used to estimate weights for the water quality indicators anomalies in a linear transformation that can indicate the risk of wastewater contamination. Results showed that the wastewater spatial mode for this region was characterized by positive anomalies of backscattering coefficient of particulate matter and absorption coefficient of detritus combined with coloured dissolved organic matter, each with a relative importance of 50%. The application of this spatiotemporal analysis was formulated as the Wastewater Contamination Index. With the aid of photographic records, and additional meteorological and water quality data, the results of the index were verified for wastewater outfall events in the study area. The methodology for constructing the proposed Wastewater Contamination Index applies to other locations and can be a valuable tool for operational monitoring of wastewater contamination.

Experimental Analyses of Temporal Activity-Sequencing Anomalies in Process Mining

In the research field of the automated process discovery and analysis, the purity of event log datasets ought to be a matter of vital importance to the success of discovering sound and exact process models. Moreover, there exist various types of anomalies that result in the discovery of inaccurate process models from the process enactment event log datasets. A peculiar one out of these anomalies, which is the core challenging issue of this paper, is the temporal activity-sequencing anomaly that critically affects the overall quality of the automated process discovery. This paper explores such event-log anomalies and noises produced by the special type of anomalies inevitably formed in the event-log preprocessing phase of the automated process discovery. More precisely, it implements an algorithmic approach that is able to detect and filter out those anomalies and noises in performing the automated process discovery. The author also carries out a series of experimental analyses by applying the implemented approach to the five datasets of process event logs available in the 4TU Center for Research Data.

Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.

Pre-quake frequency characteristics of Ms ≥7.0 earthquakes in mainland China

In this study, natural orthogonal expansion was performed on earthquake frequencies to compute the pre-quake frequency fields of 9 Ms ≥7.0 earthquakes in mainland China from 1980 to 2020. The temporal and spatial pre-quake anomalies of these earthquakes were extracted from their frequency fields. We found that the majority of pre-quake temporal anomalies (i.e., variations exceeding two-times the absolute mean square error) of a strong earthquake are condensed within the first four frequency fields, and typically comprise multiple components. The temporal factor of the first frequency field usually accounts for the largest proportion of these anomalies (40%–60% of the entire field). Most Ms ≥7.0 earthquakes exhibited long-term anomalies 5–8 years before their occurrence; some presented medium-term anomalies 1–2 years prior to the quake, and only a few presented short-term and imminent anomalies (≤3 months before the quake). Anomalous seismic hazard zones have high-gradient turning points in regional frequency-field contour maps, and the epicenters of strong earthquakes are often located in areas containing active faults that have contour values. Through the comparison of seismic frequency field and the traditional method of regional seismic activity frequency (3 months), it is shown that the frequency-field time factor has the advantages of diversified and rich abnormal information. The slope comparison between the frequency field and the cumulative frequency curve shows that the frequency anomaly time of the two is consistent, and the conclusion is reliable. Therefore, the seismic frequency method can predict the occurrence time and location of strong earthquakes, which is closer to the predictable seismic model.

Satiation, Habit Formation, and Other Temporal Anomalies: Extending the Choice Theory to Multiple Neighborhoods of Time

Satiation, Habit Formation, and Other Temporal Anomalies: Extending the Choice Theory to Multiple Neighborhoods of Time

Hillslope-scale variability of soil water potential over humid alpine forests: Unexpected high contribution of time-invariant component

Soil water potential (SWP) gradients control water and energy fluxes in the Soil – Plant – Atmosphere Continuum. Exploring the spatial – temporal variability of SWP at the hillslope scale is an essential step towards developing ecohydrological models. In this study, SWP monitoring was carried out on two contrasting, alpine, humid forested hillslopes (i.e., coniferous and broadleaf forests) at three depths (10, 50, and 100 cm). The effects of static (soil properties and topography) and dynamic (precipitation dynamics and soil wetness conditions) factors on SWP dynamics and variability were investigated based on statistical methods, temporal stability, and variance decomposition analysis. The results showed that the SWP in the soil – root zone (10 and 50 cm depths) in the broadleaf forest exhibited higher variability and lower temporal stability than in the coniferous forest, likely because of the stronger evapotranspiration and more variable soil hydraulic conductivity. The relatively high SWP variability in the deeper soil layer (100 cm depth) of both forested hillslopes was due to the frequent lateral/preferential flow. Furthermore, topographic factors (i.e., distance of hillslope to stream and slope) and soil properties (clay and rock fragment contents) regulated the spatial patterns in SWP and influenced representative site selection in the coniferous and broadleaf forests, respectively. Decomposition of the spatial variance showed that the time-invariant component was the dominant contributor to the total spatial variance. An exponentially increasing relationship between the time-invariant component and mean SWP and a concave-up parabolic shape between the contribution of the temporal anomalies and mean SWP were observed. Our results provide an improved understanding of the temporal and spatial variability in terms of SWP in forested hillslopes.

The influence of increasing sea surface temperature on particulate organic carbon in Indonesian waters based on MODIS Aqua satellite

Both Sea Surface Temperature (SST) and Particulate Organic Carbon (POC) have important roles in the marine ecosystem and carbon cycle. Therefore, we analyze the influence of SST on POC in Indonesian water. We used SST and POC products from the moderate-resolution imaging spectroradiometer (MODIS) Aqua satellite from 2003 to 2021 (nineteen years). Regression and a temporal anomaly correlation approach were applied to assess the effect of SST on POC in Indonesian waters. Our results show that the southeast Indonesian waters, south of Java Island and around the Riau Islands, mostly have a high SST variation. On the other hand, the high variation of POC mostly appeared in Indonesian coastal. During the last nineteen years, the increasing SST anomalies (SSTA) in Indonesian waters have been followed by decreasing POC anomalies (POCA). These results indicate that rising SST reduces the ocean’s ability to absorb carbon in Indonesian waters. A strong negative relation between SSTA on POCA was found from April to August and October to January in a seasonal timescale. In the interannual timescale, our result reveals that the strong negative relationship between SSTA and POCA is likely linked to the El Niño Southern Oscillation, the Indian Ocean Dipole, and global warming.

Spatiotemporal Analysis and War Impact Assessment of Agricultural Land in Ukraine Using RS and GIS Technology

Military conflicts are one of the inevitable factors that can cause countries to suffer from food insecurity due to reduced agricultural productivity, increased food prices, and the deterioration of agricultural land and infrastructure. Farmland may become fallowed and abandoned as a result of reduced investment in agricultural management caused by military conflicts. To rapidly assess the impact of conflicts on agricultural land and food security, the utilization of effective and feasible methods for the regular monitoring agricultural management status is necessary. To achieve this goal, we developed a framework for analyzing the spatiotemporal distribution of agricultural land and assessing the impact of the Ukraine–Russia war on agricultural management in Ukraine using remote sensing (RS) and geographic information system (GIS) technology. The random forest (RF) classifier, gap filling and Savitzky–Golay filtering (GF-SG) method, fallow-land algorithm based on neighborhood and temporal anomalies (FANTA) algorithm, and kernel density method were jointly used to classify and reveal the spatiotemporal distribution of fallowed and abandoned croplands from 2018 to 2022 based on Landsat time series data on the Google Earth Engine (GEE) platform. The results demonstrated that fallowed and abandoned croplands could be successfully and effectively identified through these proven methods. Hotspots of fallowed croplands frequently occurred in eastern Ukraine, and long-term consecutive fallow agricultural management caused cropland abandonment. Moreover, hotspots of war-driven fallowed croplands were found in western Kherson and the center of Luhansk, where the war has been escalated for a long time. This reveals that the war has had a significant negative impact on agricultural management and development. These results highlight the potential of developing an accessible methodological framework for conducting regular assessments to monitor the impact of military conflicts on food security and agricultural management.

Skill assessment of Saudi-KAU and C3S models in prediction of spring season rainfall over the Arabian Peninsula

A skillful prediction of precipitation has great value, particularly for regions that suffer from water stress. In this study, we assess the potential predictability and skill of the Copernicus Climate Change Service (C3S) and Saudi-KAU models in their simulation of precipitation over the Arabian Peninsula during spring (March–May) for the period 1993–2016. For this purpose, data from individual models as well as the multi-model ensemble (MME) is used. The prediction data for MAM precipitation initialized at Feb (Lead 1), Jan (Lead 2), and Dec (Lead 3), were obtained from the 5 C3S and Saudi-KAU coupled global climate model. The potential predictability was computed by evaluating the signal to noise ratio and the theoretical limit of correlation skill, while the prediction skill was estimated from the temporal anomaly correlation co-efficient. The results show that the Saudi-KAU, CMCC, and UKMO models have slightly higher potential predictability of about 0.25, 0.35, and 0.25 respectively, as compared to other models. It is also observed that individual models as well as their MME show a high (low) potential predictability over southwestern (northern) regions of the Peninsula. Moreover, the Saudi-KAU, CMCC, and MME show a reasonably good correlation skill (0.68, 0.59, and 0.57) while the SEAS model displays lower skill (0.14) for spring precipitation. All model simulations reveal a decrease in prediction skill for longer lead times. On the other hand, the individual models and their MME successfully reproduced the Pacific (i.e. ENSO) teleconnection patterns while displaying lower skill over the tropical Atlantic Ocean. The results indicate that the model biases have negative impacts on potential predictability and prediction skill over the Arabian Peninsula during the spring season.

Time series data anomaly detection based on LSTM-GAN

With the improvement of modern technology, a large number of time series data have been produced. The anomaly detection of time series data can provide relevant information for key situations faced by various fields. This paper proposed an unsupervised temporal anomaly detection method based on generation countermeasure network. In this model, Wasserstein distance is used instead of the original measurement method, and LSTM is used as the basic network of GAN. The model uses the reconstruction loss of the generator and the loss of the discriminator to define the anomaly function to judge the anomaly. This paper uses real world time series data sets involving various fields to evaluate the model. Experiments show that the model is effective in anomaly detection of time series data.

Temporal Rainfall Anomaly and Its Association with Crop Yield: In the Case of Afar Region, Ethiopia

Ethiopian economy is predominantly agrarian and the majority of the population in the country engaged in agricultural activities which is considerably affected by rainfall variability. The majority of the nation's crops are rain-fed, making them particularly vulnerable to the fluctuation that can cause severe food insecurity, which can result in famine and poverty over the whole nation. The impact of rainfall variability was very serious in the pastoral areas that are largely inhabited by the pastoralist whose livelihood depend primarily on livestock farming. For this study, rainfall data were taken from Ethiopian Meteorology Institute (EMI) for 1981-2020 period and yield data were taken from Central Statistical Agency (CSA) of Ethiopia for 1994-2019 period. This study was briefly evaluated rainfall variability and its impact on yield and computed Rainfall Anomaly Index (RAI) for Kiremt cropping season in a pastoral area of Afar region, Ethiopia. Regression analysis for the Kiremt cropping season based on observed rainfall dataset was used in the study to further analyze the relationship between rainfall variability and yield. The temporal analysis of RAI clearly indicated that there were more dry years than wet years with varies magnitude. The year of 1984 and 2009 were found extremely dry with magnitude of -4.95 and -4.13, respectively whereas 1987, 2015, 2002, 2004 and 1997 were identified as very dry with magnitude of -3.92, -3.39, -3.16, -2.88 and -2.41, respectively. The year of 1988 and 1998 found extremely humid with magnitude of 5.87 and 5.33 whereas 2020, 1994, 2010 and 1992 were very humid with magnitude of 3.47, 3.20, 2.98 and 2.34 respectively. The outcome of the regression analysis showed a positive link between rainfall anomaly and crop production for the Kiremt cropping season, with yield increasing with a positive rainfall anomaly and rainfall anomaly accounting for 29.4% of crop yield. Keywords: Kiremt Cropping Season, Rainfall Anomaly Index, Pastoral Area, Regression, Yield DOI: 10.7176/JEES/12-9-03 Publication date: September 30 th 2022

Identifying Anomalies in Seismic Velocity and Scattering Property Changes at Active Volcanoes Based on Seismic Interferometry and the Local Outlier Probability Method

The identification of anomalies in seismic wave interferometry data is important in the prediction of imminent volcanic eruptions. Herein, we propose using the local outlier probability (LoOP) method to evaluate the degree of anomaly in seismic wave velocities and scattering properties, estimated via seismic wave interferometry. LoOP is the likelihood that an observation is anomalous and is always in the range of 0–1 (0–100%). We quantitatively evaluated the degree of anomaly in seismic wave velocities and scattering properties before and after the eruption of Mt. Aso, Japan, in October 2016 and Mt. Shinmoedake, which lies within the Mt. Kirishima cluster of volcanoes, Japan, in 2017 and 2018. We found that LoOP exceeded 70% 2 to 3 days before Mt. Aso erupted on October 8, 2016, and it exceeded 70% 1 to 5 days before Mt. Shinmoedake erupted on October 11, 2017 and March 6, 2018. Adjusting the reference and quiet periods for the estimation of changes in seismic velocity/scattering property and LoOP calculations can allow the tracking of repeated, significant LoOP increases during times of high volcanic activity. The quantitative evaluation of temporal anomalies in seismic datasets will improve the precision of predictions of imminent volcanic eruptions.