Academic literature on the topic 'Geo-Located time series'

Abstract. In this work we use the multiscale entropy method to analyse the variability of geo-electric time series monitored in two sites located in Mexico. In our analysis we consider a period of time from January 1995 to December 1995. We systematically calculate the sample entropy of electroseismic time series. Important differences in the entropy profile for several time scales are observed in records from the same station. In particular, a complex behaviour is observed in the vicinity of a M=7.4 EQ occurred on 14 September 1995. Besides, we also compare the changes in the entropy of the original data with their corresponding shuffled version.

Abstract. Dam-affected hydrologic time series give rise to uncertainties when they are used for calibrating large-scale hydrologic models or for analysing runoff records. It is therefore necessary to identify and to quantify the impact of impoundments on runoff time series. Two different approaches were employed. The first, classic approach compares the volume of the dams that are located upstream from a station with the annual discharge. The catchment areas of the stations are calculated and then related to geo-referenced dam attributes. The paper introduces a data set of geo-referenced dams linked with 677 gauging stations in Europe. Second, the intensity of the impoundment impact on runoff times series can be quantified more exactly and directly when long-term runoff records are available. Dams cause a change in the variability of flow regimes. This effect can be measured using the model of linear single storage. The dam-caused storage change ΔS can be assessed through the volume of the emptying process between two flow regimes. As an example, the storage change ΔS is calculated for regulated long-term series of the Luleälven in northern Sweden.

This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed.

Abstract FREEWAT is a free and open source QGIS-integrated platform, developed to simulate several hydrological processes by combining the capabilities of geographic information system (GIS) for geo-processing and post-processing tools with several codes of the well-known USGS MODFLOW ‘family’. FREEWAT platform was applied for the groundwater flow simulation of a coastal aquifer system, located in northern Greece. The simulation was conducted using the MODFLOW_2005 code, the Observation Analysis Tool (a FREEWAT module facilitating the integration of time series observations into modeling), while the UCODE_2014 code was used as the main module for the sensitivity analysis and parameter estimation. The statistics used include composite scaled sensitivities, parameter correlation coefficients, and leverage. The simulation of the investigated aquifer system was found to be satisfactory, indicating that the simulated level values were slightly greater than the observed values after the optimization.

Logging while drilling (LWD) plays a crucial role in geo-steering, which can determine the formation boundary and resistivity in real time. In this study, an efficient inversion, which can accurately invert formation information in real time on the basis of fast-forward modeling, is presented. In forward modeling, the Gauss–Legendre quadrature combined with the continued fraction method is used to calculate the response of the LWD instrument in a layered formation. In inversion modeling, the Levenberg–Marquardt (LM) algorithm, combined with the line search method of the Armijo criterion, are used to minimize the cost function, and a constraint algorithm is added to ensure the stability of the inversion. A positive and negative sign is added to the distance parameter to determine whether the LWD instrument is located above or below the formation boundary. We have carried out a series of experiments to verify the accuracy of the inversion. The experimental results suggest that the forward algorithm can make the infinite integral of the Bessel function rapidly converge, and accurately obtain the response of the LWD instrument in a layered formation. The inversion can accurately determine the formation resistivity and boundary in real time. This is significant for geological exploration.

Abstract. Accurately assessing geo-hazards and quantifying landslide risks in mountainous environments are gaining importance in the context of the ongoing global warming. For an in-depth understanding of slope failure mechanisms, accurate monitoring of the mass movement topography at high spatial and temporal resolutions remains essential. The choice of the acquisition framework for high-resolution topographic reconstructions will mainly result from the trade-off between the spatial resolution needed and the extent of the study area. Recent advances in the development of unmanned aerial vehicle (UAV)-based image acquisition combined with the structure-from-motion (SfM) algorithm for three-dimensional (3-D) reconstruction make the UAV-SfM framework a competitive alternative to other high-resolution topographic techniques. In this study, we aim at gaining in-depth knowledge of the Schimbrig earthflow located in the foothills of the Central Swiss Alps by monitoring ground surface displacements at very high spatial and temporal resolution using the efficiency of the UAV-SfM framework. We produced distinct topographic datasets for three acquisition dates between 2013 and 2015 in order to conduct a comprehensive 3-D analysis of the landslide. Therefore, we computed (1) the sediment budget of the hillslope, and (2) the horizontal and (3) the three-dimensional surface displacements. The multitemporal UAV-SfM based topographic reconstructions allowed us to quantify rates of sediment redistribution and surface movements. Our data show that the Schimbrig earthflow is very active, with mean annual horizontal displacement ranging between 6 and 9 m. Combination and careful interpretation of high-resolution topographic analyses reveal the internal mechanisms of the earthflow and its complex rotational structure. In addition to variation in horizontal surface movements through time, we interestingly showed that the configuration of nested rotational units changes through time. Although there are major changes in the internal structure of the earthflow in the 2013–2015 period, the sediment budget of the drainage basin is nearly in equilibrium. As a consequence, our data show that the time lag between sediment mobilization by landslides and enhanced sediment fluxes in the river network can be considerable.

Abstract Background Information regarding influenza activity can inform clinical and public health activities. However, current surveillance approaches induce a delay in influenza activity reports (typically 1–2 weeks). Recently, we used data from smartphone connected thermometers to accurately forecast real-time influenza activity at a national level. Because thermometer readings can be geo-located, we used state-level thermometer data to determine whether these data can improve state-level surveillance estimates. Methods We used temperature readings collected by the Kinsa smart-thermometer and mobile device app to develop state-level forecasting models to predict real-time influenza activity (1–2 weeks in advance of surveillance reports). We used state-reported influenza-like illness (ILI) to represent state influenza activity for 48 US states with sufficient surveillance data. Counts of temperature readings, fever episodes and reported symptoms were computed by week. We developed autoregressive time-series models and evaluated model performance in an adaptive out-of-sample manner. We compared baseline time-series models containing lagged state-reported ILI activity to models incorporating exogenous thermometer readings. Results A total of 10,262,212 temperature readings were recorded from October 30, 2015 to March 29, 2018. In nearly all of the 48 states considered, weekly forecasts of ILI activity improved considerably when thermometer readings were incorporated. On average, state-level forecasting accuracy improved by 23.9% compared with baseline time-series models. In many states, such as PA, New Mexico, MA, Virginia, New York and SC, out-of-sample forecast error was reduced by more than 50% when thermometer data were incorporated. In general, forecasts were most accurate in states with the greatest number of device readings. During the 2017–2018 influenza season, the average improvement in forecast accuracy was 24.4%, and thermometer readings improved forecasting accuracy in 41, out of 48, states. Conclusion Data from smart thermometers accurately track real-time influenza activity at a state level. Local surveillance efforts may be improved by incorporating such information. Such data may also be useful for longer-term local forecasts. Disclosures I. Singh, Kinsa Inc.: Board Member, Employee and Shareholder, equity received and Salary. S. Pilewski, Kinsa Inc.: Employee and Shareholder, equity received and Salary. V. Petrovic, Kinsa Inc.: Employee and Shareholder, equity received and Salary.

Reservoir-induced earthquakes are a challenging issue for hydropower, and have occurred at many sites around the world. However, each event is unique in itself and depends on the geo-tectonics and geo-hydrology of the area in which the event is situated. This article focuses on seismic events at the Song Tranh 2 hydropower project located in Quang Nam province, Vietnam. The construction of the 96 meter high Roller Compacted Concrete (RCC) dam of this project was completed in August 2011. Approximately one year after commissioning, the dam began experiencing a serious leakage problem through the dam body. In addition, a series of earthquakes occurred near the project area, and continued for several months. The high intensity and magnitude of the earthquakes caused damage to the project and promoted fear among the local people living in the downstream valley. The issues drew significant media attention and thousands of articles about this project were written within a short time. As a result, dam authorities have been subject to extreme public pressure.This paper describes the earthquake events and difficult situation that both the local population and authorities faced in its aftermath. In addition, we analyze seismic events qualitatively,using data and information on the water filling and drawdown processes. Our analysis provides an insight into these seismic events, as we reconstruct the earthquake scenarios and test a hypothesis of earthquake occurrence. Future earthquake activities are also predicted and compared.DOI: http://dx.doi.org/10.3126/hn.v15i0.11285HYDRO Nepal JournalJournal of Water, Energy and EnvironmentVolume: 15, 2014, JulyPage: 16-20

The objectives of this paper are (1) to reconstruct time series of the historical and current landscape structures based on historical documents and serial cadastral maps, (2) to analyse the changes of agricultural production function by the application of historical soil assessments and (3) to analyse the connections between landscape structure and production function in reference to the social and economic driving forces. The case study area is today an intensively-used agricultural landscape located nearby Taucha-Eilenburg (NW-Saxony), Germany. Arable landscapes in Germany are changing with increasing dynamics: valuable structures and landscape functions of the traditional and multifunctional landscape were lost. New landscape structures replaced the traditional ones slowly or sometimes also in short time steps. Therefore, this paper focuses on the changes of landscape structures and that of the soil production function induced by land use since the 18th century. The changes are analysed on the basis of historical and serial cadastral maps and documents by covering four time steps from 1750 to 2005. The historical maps were scanned, geo-referenced and digitalised in GIS. Thus, quantitative analysis of landscape structure changes on parcel level is enabled. The production function is explicitly reconstructed on the basis of the Prussian Taxation of the real estate of 1864 (Preußische Grundsteuerbonitierung) and The German Soil Taxation (Reichsbodenschätzung) of 1937. Changes observed on the serial cadastral maps were linked with the social and economical driving forces and the soil production function. Moreover, there is a high demand for the development of methodologies to analyse and to assess time series of landscape structures, land use and landscape functions in the historical context of landscape development.

Les séries temporelles sont un type de données endémique dans de nombreux domaines d'applications, telles que l'analyse financière, le diagnostic médical, la surveillance de l'environnement ou encore l'astronomie. Du fait de leur structure complexe, les séries temporelles amènent à de nouveaux défis dans le traitement et l'extraction de connaissances de ces données. La représentation des séries temporelles joue un rôle déterminant dans les méthodes d'apprentissage et les tâches de fouille de données. Cependant, peu de méthodes tiennent compte des interdépendances entre séries temporelles différentes. De plus, la fouille de séries temporelles nécessite de considérer non seulement les caractéristiques des séries temporelles en termes de complexité des données, mais également les contextes particuliers des applications et la tâche de fouille de données à effectuer. Cela nous permet de construire des représentations spécifiques à la tâche.Dans cette thèse, nous étudions différentes représentations de séries temporelles capables de s'adapter à diverses tâches de fouille de séries temporelles, tout en capturant les relations entre elles. Nous nous concentrons spécifiquement sur la modélisation des interdépendances entre séries temporelles lors de la construction des représentations, qui peuvent être la dépendance temporelle au sein de chaque source de données ou la dépendance inter-variable entre des sources de données différentes. En conséquence, nous étudions les séries temporelles collectées dans diverses applications sous différentes formes. Tout d'abord, pour tenir compte de la dépendance temporelle entre les observations, nous apprenons la représentation de série temporelle dans un contexte de flux dynamique, où la série temporelle est générée en continu à partir de la source de données. Quant à la dépendance inter-variable, nous étudions les séries temporelles multivariées (MTS) avec des données collectées à partir de plusieurs sources. Enfin, nous étudions le MTS dans le contexte de la ville intelligente, où chaque source de données est associée à une localisation spatiale. Par conséquent, le MTS devient une série temporelle géo-localisée (GTS), pour laquelle la modélisation de la dépendance inter-variable requière la prise en compte de l'information spatiale sous-jacente. De ce fait, pour chaque type de séries temporelles collectées dans des contextes différents, nous proposons une méthode de représentation adaptée aux dépendances temporelles et/ou inter-variables.Outre la complexité des données provenant des interdépendances des séries temporelles, nous étudions diverses tâches d'apprentissage automatique sur des séries temporelles afin de valider les représentations apprises. Les tâches d'apprentissage étudiées dans cette thèse consistent en la classification de séries temporelles, l'apprentissage semi-supervisé de séries temporelles et la prévision de séries temporelles. Nous montrons comment les représentations apprises sont exploitées dans ces différentes tâches et pour des applications distinctes.Plus précisément, nos principales contributions sont les suivantes. En premier lieu, nous proposons un modèle d'apprentissage dynamique de la représentation des séries temporelles dans le contexte du flux de données, où nous considérons à la fois les caractéristiques des séries temporelles et les défis des flux de données. Nous affirmons et démontrons que le motif de Shapelet, basé sur la forme, est la meilleure représentation dans le contexte dynamique. Par ailleurs, nous proposons un modèle semi-supervisé pour l'apprentissage de représentation dans les MTS. Ce modèle considère la dépendance inter-variable dans l'hypothèse réaliste où les annotations de données sont limitées. Enfin, nous proposons un modèle d'apprentissage de représentation de GTS dans le contexte de la ville intelligente. Nous étudions spécifiquement la tâche de prévision du trafic routier avec un focus sur le traitement intégré des valeurs manquantes
Time series is a common data type that has been applied to enormous real-life applications, such as financial analysis, medical diagnosis, environmental monitoring, astronomical discovery, etc. Due to its complex structure, time series raises several challenges in their data processing and mining. The representation of time series plays a key role in data mining tasks and machine learning algorithms for time series. Yet, a few methods consider the interrelation that may exist between different time series when building the representation. Moreover, the time series mining requires considering not only the time series' characteristics in terms of data complexity but also the concrete application scenarios where the data mining task is performed to build task-specific representations.In this thesis, we will study different time series representation approaches that can be used in various time series mining tasks, while capturing the relationships among them. We focus specifically on modeling the interrelations between different time series when building the representations, which can be the temporal relationship within each data source or the inter-variable relationship between various data sources. Accordingly, we study the time series collected from various application contexts under different forms. First, considering the temporal relationship between the observations, we learn the time series in a dynamic streaming context, i.e., time series stream, for which the time series data is continuously generated from the data source. Second, for the inter-variable relationship, we study the multivariate time series (MTS) with data collected from multiple data sources. Finally, we study the MTS in the Smart City context, when each data source is given a spatial position. The MTS then becomes a geo-located time series (GTS), for which the inter-variable relationship requires more modeling efforts with the external spatial information. Therefore, for each type of time series data collected from distinct contexts, the interrelations between the time series observations are emphasized differently, on the temporal or (and) variable axis.Apart from the data complexity from the interrelations, we study various machine learning tasks on time series in order to validate the learned representations. The high-level learning tasks studied in this thesis consist of time series classification, semi-supervised time series learning, and time series forecasting. We show how the learned representations connect with different time series learning tasks under distinct application contexts. More importantly, we conduct the interdisciplinary study on time series by leveraging real-life challenges in machine learning tasks, which allows for improving the learning model's performance and applying more complex time series scenarios.Concretely, for these time series learning tasks, our main research contributions are the following: (i) we propose a dynamic time series representation learning model in the streaming context, which considers both the characteristics of time series and the challenges in data streams. We claim and demonstrate that the Shapelet, a shape-based time series feature, is the best representation in such a dynamic context; (ii) we propose a semi-supervised model for representation learning in multivariate time series (MTS). The inter-variable relationship over multiple data sources is modeled in a real-life context, where the data annotations are limited; (iii) we design a geo-located time series (GTS) representation learning model for Smart City applications. We study specifically the traffic forecasting task, with a focus on the missing-value treatment within the forecasting algorithm

Sefton, on the north side of Liverpool, holds a radioactive legacy from its industrial past. This legacy is in the form of Tin slag buried in sub-surface seams. Located near the docks and adjacent to the rich Lancashire coal seams, Sefton became one of the main production centres of Tin plate in Britain. A consequence of this industrial process is the production of mildly radioactive waste slag. Tin rich ores are heated under reducing conditions to produce a molten metal stream This is then separated into the component metal streams. Solid wastes produced by this process are known as slag and were usually stored on site in spoil heaps. Because this slag is a very hard, glassy material it has been historically used as aggregate in underlying roads and rail way sleepers. Many of these sites pre-date the introduction of the regulation of radioactive substances in the UK and have never been under legislative control under the Radioactive Substances Act, RSA93. There is a risk that the existence may not be known of some of these sites. U-238 and Th-232 and their associated decay chains, are the major contributors to the radionuclide inventory of the slags, levels of these radionuclides being in the range 1–10Bq/g. A series of alpha and beta decays for both chains leads eventually to the generation of a stable isotope of lead. Radiologically, the main area of concern is with the potential inhalation or ingestion of contaminated dusts. There is also a potential for Ra-226 to leach out into groundwater. AMEC has worked for Sefton Metropolitan Council and various developers, to carry out specialist, non intrusive gamma radiation surveys of numerous sites in Sefton. This is the first stage in carrying out a radiological risk review of a given site. What often then follows is an intrusive, geo-technical survey, with trial pitting and radiological sampling for later sensitive lab based radiochemical analysis. Radiological supervision is also required at this time to ensure that the radiological exposure of the Contractors carrying the survey is restricted and ensure that plant dose not become contaminated with radionuclides. These surveys are the preliminary stage for redevelopment works with new housing replacing antiquated commercial premises. By bringing together expertise in sensitive gamma surveying, radiochemical analysis and a detailed understanding of the regulatory framework, AMEC is able to support the borough of Sefton in its re-development programme ensuring safe compliant development of an area with an historic radiological legacy.