Trend Change Detection Research Articles

Global monitoring for biodiversity: Uncertainty, risk, and power analyses to support trend change detection.

Global targets aim to reverse biodiversity declines by 2050 but require knowledge of current trends and future projections under policy intervention. First, given uncertainty in measurement of current trends, we propose a risk framework, considering probability and magnitude of decline. While only 11 of 198 systems analyzed (taxonomic groups by country from the Living Planet Database) showed declining abundance with high certainty, 20% of systems had a 70% chance of strong declines. Society needs to decide acceptable risks of biodiversity loss. Second, we calculated statistical power to detect trend change using ~12,000 populations from 62 systems currently showing strong declines. Current trend uncertainty hinders our ability to assess improvements. Trend change is detectable with high certainty in only 14 systems, even if thousands of populations are sampled, and conservation action reduces net declines to zero immediately, on average. We provide potential solutions to improve monitoring of progress toward biodiversity targets.

Trends in the incidence, prevalence and sales volume of menopausal hormone therapy in Sweden from 2000 to 2021.

To describe the trends in the prevalence of use menopausal hormone therapy (MHT) in Sweden over the period 2000-2021 and to analyse the impact of different lengths of run-in on the calculated incident use. Individual-level data on MHT dispensations for 2.5 million women aged 45-69years for the period 2006-2021 were analysed. Aggregated sales volumes in defined daily dose (DDD) were available for the whole study period (2000-2021). One-year prevalence and one-year incidence (18-month run-in) per 1000 women and DDD per 1000 women per day of MHT were the main outcome measures. The predictive values for incidence representing first-ever use of MHT were calculated for different run-in periods, which is a defined period without dispensations. Both the DDD, from 2000, and the prevalence, from 2006, decreased by over 80% in women aged 50-54years, until 2010, when the use of MHT stabilised. The predictive value for incident users to be first-ever users was 88% in women aged 50-54years, with a run-in of 18months, in 2021. The incidence was stable between 2007 and 2016. From 2017 the incidence increased, being most pronounced for women close to menopause. MHT use decreased significantly after the turn of the century, but has increased since 2017. A run-in period of 18months was found suitable and reliable for defining incident users of MHT in the age intervals closest to menopause. Incidence seems to be a more sensitive measure than prevalence or DDD for the early detection of changes in trends in prescriptions of MHT.

SatVITS‐Flood: Satellite Vegetation Index Time Series Flood Detection Model for Hyperarid Regions

AbstractWe present the satellite vegetation index time series model for detecting historical floods in ungauged hyperarid regions (SatVITS‐Flood). SatVITS‐Flood is based on observations that floods are the primary cause of local vegetation expansion in hyperarid regions. To detect such expansion, we used two time‐series metrics: (a) trend change detection from the Breaks For Additive Season and Trend and (b) a newly developed seasonal change metric based on Temporal Fourier Analysis (TFA) and the growing‐season integral anomaly (TFA‐GSIanom). The two metrics complement each other by capturing changes in perennial plant species following extreme, rare floods and ephemeral vegetation changes following more frequent floods. Metrics were derived from the time series of the normalized difference vegetation index, the modified soil‐adjusted vegetation index, and the normalized difference water index, acquired from MODIS, Landsat, and Advanced Very High‐Resolution Radiometer. The timing of the change was compared with the date of the flood and the magnitude of change with its volume and duration. We tested SatVITS‐Flood in three regions on different continents with 40‐year‐long, systematic, reliable gauge data. Our results indicate that SatVITS‐Flood can predict flood occurrence with an accuracy of 78% and precision of 67% (Recall = 0.69 and F1 = 0.68; p < 0.01), and the flood volume and duration with NSE of 0.79 (RMSE = 15.4 × 106 m3 event−1), and R2 of 0.69 (RMSE = 5.7 days), respectively. SatVITS‐Flood proved useful for detecting historical floods and may provide valuable long‐term hydrological information in poorly documented areas, which can help understand the impacts of climate change on the hydrology of hyperarid regions.

Detection of trend changes in surface energy budget caused by urbanization and land cover/use changes in Dezful plain, Iran

Detection of trend changes in surface energy budget caused by urbanization and land cover/use changes in Dezful plain, Iran

Time-Frequency-Based Bridge Scour Identification by Trend-Change Detection

Time-Frequency-Based Bridge Scour Identification by Trend-Change Detection

Improving near real-time anomaly event detection and classification with trend change detection for smart water grid operation management

ABSTRACT To improve the efficiency of Smart Water Grid (SWG) operation, it is of great importance to timely detect and classify anomaly events. Authors have developed a comprehensive solution framework for anomaly detection. However, the framework may detect undesirable false alarms, which will incur wasteful field investigation. Therefore, it is imperative to reduce the number of false alarms for real world SWG operation. In this paper, the anomaly event classification has been improved by an algorithm of trend change detection in two phases including (1) detecting trend change in single sensor, and (2) identifying the effective evaluation time window by correlating the flow anomaly trend period and pressure anomaly trend periods to filter the detected events. A real case study has been conducted to demonstrate the application of the proposed algorithm; the number of false alarms is reduced by as much as 31% without compromising the detection accuracy.

TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks

Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicating without human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc and quasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructures that are able to perform heavyweight network management tasks using, e.g. machine learning-based Network Traffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do not need to be (re-) trained has received much attention due to the time complexity of the training phase. In this study, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), is proposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statistical light-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trends and recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA is then compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detects approximately 60% less false positive alarms than RuLSIF.

Gross Solids Content Prediction in Urban WWTPs Using SVM

The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision (Radj2 = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers.

Detection of future changes in trends and scaling exponents in extreme short-term rainfall at selected stations in Slovakia

Abstract This paper analyses projected changes in short-term rainfall events during the warm season (April – October) in an ensemble of 30 regional climate model (RCM) simulations. The analysis of trend changes and changes in scaling exponents was done for the Hurbanovo, Bratislava, Oravská Lesná, and Myjava stations in Slovakia. The characteristics of maximum rainfall events were analysed for two scenario periods, one past and one future (1960–2000 and 2070–2100) and compared to the characteristics of the actual observed events. The main findings from the analysis show that 60-min short-term events for most of the RCM simulations will either increase or remain constant. On the other hand, the depths and intensities of daily events are projected to increase significantly; in some cases they were found to be ten times larger. Trends in future events at the Hurbanovo station were found to be insignificant. In the other stations positive trends in future rainfall events prevail, except for daily rainfall at the Myjava station, which shows a negative trend. Using results from the selected simulations, the scaling exponents estimated are on average lower than the exponents of the data observed. On the other hand, due to the higher daily precipitation amounts in the future seen to all the scenarios, the downscaled values of short-term rainfall at all the stations analysed might be considerably higher in the future horizons, which could subsequently affect future design rainfall values for engineering designs.

Scientific toolkit for predictive efficiency management of railway transportation

The problem of uncertainty and early prediction of possible “breaking points” in the trends of changes in key performan ce indicators, which impedes long-term economic forecasting, is extremely serious, especially in such a capital-intensive industry as rail transport. For the purpose of preventive detection of changes in trends, the authors suggested using the “marginal factor of operating costs” as a forecasting tool, which will be the most sensitive to changes in the efficiency of railways, playing the leading role, as compared to gross indicators or derived from them. This indicator with the developed classification of its zonal levels will allow initiating the adoption of the necessary managerial decisions, taking into account marginal revenues and marginal costs. An analysis of change in the marginal coefficient of operating costs, taking into account the effect of filling the capacity of the railway line on it, makes it possible to identify with high sensitivity the signals of emerging positive or negative changes in the level of efficiency. Thus, the timely formation and refinement of measures for the development of the railway network, as well as the introduction of new transport products on the market, the implementation of innovative, including digital, technologies should be based on a scientific tool for predictive management of the efficiency of rail transport. The marginal coefficient of operating costs as a leading indicator of efficiency can become an indicative basis for such management. It is advisable to monitor the marginal coefficient of operating costs in the network as a whole, and also to develop the procedure for its evaluation by kinds of freight, directions of transportation and transport products. This will allow to prioritize the implementation of specific activities more justified, on the one hand, and to assess the effectiveness of its implementation on the other.

The Filter Design Technique for Trend Change Detection of Random Process in Real Time

A filter for non-stationary processes, which allows receiving real-time filtering result without edge effects and delay, was proposed. Designed filtering algorithm can be used for continuous monitoring of technological processes of production conducted in order to prevent emergency situations and/or output of the process beyond the technological limitations. Built filtration system includes a Kalman filter and optimum discrete filter for random time series with stationary increments.

Does Testing More Frequently Shorten the Time to Detect Disease Progression?

PurposeWith the rise of smartphone devices to monitor health status remotely, it is tempting to conclude that sampling more often will provide a more sensitive means of detecting changes in health status earlier over time, when interventions may improve outcomes.MethodsThe answer to this question is derived in the context of a model where observations are generated from a linear-trend model with independent as well as autocorrelated autoregressive-moving average, or ARMA(1,1), errors.ResultsThe results imply a cautionary message that an increase in the sampling frequency may not always lead to a faster detection of trend changes. The benefit of rapid successive observations depends on how observations, taken closely together in time, are correlated.ConclusionsShortening the observation period by half can be accomplished by increasing the number of independent observations to maintain the same power for detecting change over time. However, a strategy to detect progression of disease sooner by taking numerous closely spaced measurements over a shortened interval is limited by the degree of autocorrelation among adjacent observations. We provide a statistical model of disease progression that allows for autocorrelation among successive measurements, and obtain the power of detecting a linear change of specified magnitude when equal-spaced observations are taken over a given time interval.Translational RelevanceNew emerging technology for home monitoring of visual function will provide a means to monitor sensory status more frequently. The model proposed here takes into account how successive measurements are correlated, which impacts the number of measurements needed to detect a significant change in status.

The application of social network mining to cattle movement analysis: introducing the predictive trend mining framework

This paper describes a predictive social network mining framework which is demonstrated using the Great Britain cattle movement datasets. The proposed framework, the predictive trend mining framework (PTMF), is used to analyse episodes of time-stamped social network data. The PTMF has two main components (1) a frequent pattern trend analysis component that efficiently identifies temporal frequent patterns and trends and also provides a mechanism for clustering and analysing these patterns and trends so as to detect dynamic changes within the cattle movement network, and (2) the predictive modelling component for forecasting the percolation of information or data across the network. The PTMF incorporates a number of novel elements including mechanisms to: (1) identify temporal frequent patterns and trends, (2) cluster large sets of trends, (3) analyse temporal clusters for pattern trend change detection, (4) visualise these changes using pattern migration network maps and (5) predict the paths whereby information moves across the network over time.

Drought Detection of West Java's Paddy Field Using MODIS EVI Satellite Images (Case Study: Rancaekek and Rancaekek Wetan)

Nowadays, drought phenomenon occurred in several area in Indonesia. The length of the dry season, especially in the southern equatorial allegedly caused by El Nino phenomenon. This causes crop failures in many center area of agriculture. West Java Province as one of the centers of agricultural activities in Indonesia experienced a severe drought within a period of 6 months (April-September in 2003). The monitoring of drought is useful to understanding the characterization of drought itself. In the next period, we can decide what should we do to decreased the impact of this phenomenon. The study aimed to implement Breaks for Additive Seasonal and Trend (BFAST) algorithm for detecting and monitoring paddy field areas experiencing drought in The West Java during the period 2000-2015. This study used remote sensing data to study the response of vegetation on the drought phenomenon. MODIS EVI time series were decomposed into seasonal, trend, and remainder component using BFAST which enables the detection of trend changes within the time series. The result of study shows that BFAST able to detect drought in MODIS EVI time series. The result also compared to a new drought index, called SPEI.

Extreme Drought-induced Trend Changes in MODIS EVI Time Series in Yunnan, China

Extreme climatic events triggered by global climate change are expected to increase significantly hence research into vegetation response is crucial to evaluate environmental risk. Yunnan province, locating in southwest China, experienced an extreme drought event (from autumn of 2009 to spring of 2010), with the lowest percentage rainfall anomaly and the longest non-rain days in the past 50 years. This study aimed to explore the characteristics and differences in the response to drought of four land cover types in Yunnan province, including forest, grassland, shrub, and cropland during the period 2001-2011. We used remote sensing data, MODIS-derived EVI (Enhanced Vegetation Index) to study the vegetation responses to this extreme drought event. The EVI time series were decomposed into trend, seasonal and remainder components using BFAST (Breaks For Additive Seasonal and Trend) which accounts for seasonality and enables the detection of trend changes within the time series. The preliminary results showed that: (1) BFAST proved to be capable of detecting drought-induced trend changes in EVI time series. (2) Changes in the trend component over time consisted of both gradual and abrupt changes. (3) Different spatial patterns were found for abrupt and gradual changes. (4) Cropland exhibited an abrupt change, due to its sensitivity to severe drought, while the forest seemed least affected by the extreme drought.

Analysis of Trend Change Detection of Total Electron Content on Ionosphere when Massive Earthquakes Occurred

This paper describes an analysis of trend changes of the total electron content (TEC) on the ionosphere as a possible precursor to massive earthquakes from 2005 to 2012 around Japan. To evaluate the TEC, we defined an index called Ratio, which can measure the deviation of a trend line of TEC around the target period. Regarding the earthquakes whose magnitude was more than 5, the results confirmed that the value of Ratio tended to be affected especially when the depth of earthquake occurrence points was less than 100 km.

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

Defining syndromes using cattle meat inspection data for syndromic surveillance purposes: a statistical approach with the 2005–2010 data from ten French slaughterhouses

BackgroundThe slaughterhouse is a central processing point for food animals and thus a source of both demographic data (age, breed, sex) and health-related data (reason for condemnation and condemned portions) that are not available through other sources. Using these data for syndromic surveillance is therefore tempting. However many possible reasons for condemnation and condemned portions exist, making the definition of relevant syndromes challenging.The objective of this study was to determine a typology of cattle with at least one portion of the carcass condemned in order to define syndromes. Multiple factor analysis (MFA) in combination with clustering methods was performed using both health-related data and demographic data.ResultsAnalyses were performed on 381,186 cattle with at least one portion of the carcass condemned among the 1,937,917 cattle slaughtered in ten French abattoirs. Results of the MFA and clustering methods led to 12 clusters considered as stable according to year of slaughter and slaughterhouse. One cluster was specific to a disease of public health importance (cysticercosis). Two clusters were linked to the slaughtering process (fecal contamination of heart or lungs and deterioration lesions). Two clusters respectively characterized by chronic liver lesions and chronic peritonitis could be linked to diseases of economic importance to farmers. Three clusters could be linked respectively to reticulo-pericarditis, fatty liver syndrome and farmer’s lung syndrome, which are related to both diseases of economic importance to farmers and herd management issues. Three clusters respectively characterized by arthritis, myopathy and Dark Firm Dry (DFD) meat could notably be linked to animal welfare issues. Finally, one cluster, characterized by bronchopneumonia, could be linked to both animal health and herd management issues.ConclusionThe statistical approach of combining multiple factor analysis with cluster analysis showed its relevance for the detection of syndromes using available large and complex slaughterhouse data. The advantages of this statistical approach are to i) define groups of reasons for condemnation based on meat inspection data, ii) help grouping reasons for condemnation among a list of various possible reasons for condemnation for which a consensus among experts could be difficult to reach, iii) assign each animal to a single syndrome which allows the detection of changes in trends of syndromes to detect unusual patterns in known diseases and emergence of new diseases.

Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change

AbstractField observations and time series of vegetation greenness data from satellites provide evidence of changes in terrestrial vegetation activity over the past decades for several regions in the world. Changes in vegetation greenness over time may consist of an alternating sequence of greening and/or browning periods. This study examined this effect using detection of trend changes in normalized difference vegetation index (NDVI) satellite data between 1982 and 2008. Time series of 648 fortnightly images were analyzed using a trend breaks analysis (BFAST) procedure. Both abrupt and gradual changes were detected in large parts of the world, especially in (semi‐arid) shrubland and grassland biomes where abrupt greening was often followed by gradual browning. Many abrupt changes were found around large‐scale natural influences like the Mt Pinatubo eruption in 1991 and the strong 1997/98 El Niño event. The net global figure – considered over the full length of the time series – showed greening since the 1980s. This is in line with previous studies, but the change rates for individual short‐term segments were found to be up to five times higher. Temporal analysis indicated that the area with browning trends increased over time while the area with greening trends decreased. The Southern Hemisphere showed the strongest evidence of browning. Here, periods of gradual browning were generally longer than periods of gradual greening. Net greening was detected in all biomes, most conspicuously in croplands and least conspicuously in needleleaf forests. For 15% of the global land area, trends were found to change between greening and browning within the analysis period. This demonstrates the importance of accounting for trend changes when analyzing long‐term NDVI time series.

Detection of trend changes in time series using Bayesian inference

Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics or external forcings. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate on a bayesian algorithm to estimate the location of the singularities and to quantify their credibility. We validate the performance and sensitivity of our inference method by estimating change points of synthetic data sets. As an application we use our algorithm to analyze the annual flow volume of the Nile River at Aswan from 1871 to 1970, where we confirm a well-established significant transition point within the time series.