GPS has been used to monitor engineering structures for a number of reasons. One important reason for monitoring high rise buildings (and other engineering structures) is their safety assessment in events of extreme loading, such as earthquakes and storms. Decisions must be made as soon as possible, whether to allow re-occupation of such buildings, or to assess them for further damage. The time required to reach such decisions is cost-critical, both for the building owner or manager and for the agency doing the assessment. Peak inter-storey drift ratio and detection of permanent damage are some of the damage assessment parameters recommended by assessment agencies. Traditionally, accelerometers have been used to monitor these parameters. Accelerometers measure accelerations which are double-integrated to get displacements. These double integrated displacements are then used for computing the inter-storey drift ratios and locating permanent damage. Displacements obtained by double-integration and inter-storey drift ratios by subtraction of these displacements, are often erroneous and unreliable and direct measurement of displacement is preferred. Direct measurement of displacement is required at a number of points along the height of the building. For example, for computing inter-storey drift ratios, measurements of displacement at both the floor level and roof level are required. Such points on buildings and other engineering structures of vertical profile are termed as mid-height points in this thesis. While GPS has been used for deformation monitoring of engineering structures and to assist in damage assessment during and after extreme loading events, its use has been limited to roof top installations. / This research is an attempt to measure displacements at mid-height locations of engineering structures of vertical profile using GPS. (For complete abstract open document).

Most bridges in Taiwan are simple beam bridge , and most of them have been used more than twenty years , even some of them are used fifty years.In recent years , there are many floods in Taiwan rivers . There are often high-velocity,flow,and entrainment of a large number of sediment from happening.This situation for the bridge itself and the safety of people cause a great threat , bridge health examination become an important issue after Typhoon Morakot. In this study , we explored modern measurement technique for bridge deformation monitoring , and it can also used bridge health examination. The cross bridge measurement and modeling is focused on the vicinity area around Da-Jia Bridge of Highway 1 based on Angle-Distance monitoring survey, Leveling, close-range photogrammetry, and 3D laser scanning technologies. The 3D point cloud model of bridge is constructed for the purpose of comparing the accuracy between four technologies. Moreover, the discussion and investigation is also conducted for at least three bridges span and two-side of the bridge deformation monitoring. P29 to P25 bridge pier deflection measurements of the monitoring points for each completed total of five point and the deformation monitoring accuracy is analyzed base on four technologies. Finally, development of the technical specification draft for bridge deformation monitoring is to aim the goal of technologically advanced, economical, and safe application in the technology of deformation monitoring of bridges. At present, the draft of technical specification applies only to the measurements of structure, settlement, displacement, and tilt for simple beam-type bridge. The bridge deformation monitoring after the flood season can reflect the degree of deformation or the deformation trend.

<p>This thesis contains the development of a deformation monitoring software based on undifferenced GPS observations. Software like this can be used in alarm systems placed in areas where the earth is unstable. Systems like this can be used in areas where people are in risk of getting hurt, like in earthquake zones or in land slide areas, but they can also be useful when monitoring the movements in buildings, bridges and other artefacts.</p><p>The main hypotheses that are tested are whether it is possible to detect deformations with undifferenced observations and if it is possible to reach the same accuracy in this mode as when working in a traditional mode where the observations are differenced.</p><p>The development of a deformation monitoring software based on undifferenced GPS observations is presented. A complete mathematical model is given as well as implementation details. The software is developed in Matlab together with a GPS observation simulator. The simulator is mainly used for debugging purposes.</p><p>The developed software is tested with both simulated and real observations. Results from tests with simulated observations show that it is possible to detect deformations in the order of a few millimetres with the software. Calculations with real observations give the same results. Further, the result from calculations in static mode indicates that the commercial software and the undifferenced software diverge a few millimetres, which probably depends on different implementations of the tropospheric corrections. In kinematic mode the standard deviation is about 1 millimetre larger in the undifferenced mode than in the double differenced mode. An initial test with different observation weighting procedures indicates that there is a lot of potential to improve the result by applying correct weights to the observations. This is one of the aims in the future work within this project.</p><p>This thesis are sponsored by the Swedish Research Council for Enviroment, Agricultural Sciences and Spatial Planning, FORMAS within the framework “Monitoring of construction and detection of movements by GPS ref no. 2002-1257"</p>

The thesis deals with testing of the automatic measurement system, namely of the system for monitoring civil engineering structures Trimble 4D Control. A summary of the basic principles of deformation monitor is given at the beginning of the study and is followed by an overview of the system´s structure. The activities carried out under the pilot project (beginning with the installation of the system and finishing by the evaluation of the measured data) are described. The project demonstrated by the functionality, reliability and usability of the systam for monitoring of the structures.

Le système Géocube est un réseau de capteurs GPS conçu et développé par le Laboratoire d'OptoÉléctronique de Métrologie et d'Instrumentation (LOEMI) de l'Institut National de l'Information Géographique et Forestière (IGN) et maintenu par le même laboratoire et l'entreprise Ophelia- Sensors qui s'occupe de son industrialisation. Il a comme objectif de mesurer les déformations du sol avec une précision millimétrique. Ce réseau de capteurs a la particularité d'être à la fois très peu énergivore, d’un faible coût de revient, simple d’installation et d’utilisation. Il est donc bien adapté à l’usage dans un environnement difficile, comme les volcans. Ce système a déjà été testé avec succès lors d’une précédente thèse sur le glacier d’Argentière et sur un glissement de terrain proche de Super-Sauze en France. La première partie de cette thèse porte sur l’optimisation du système de calcul du Géocube pour l'adapter à des réseaux de tailles plus importantes horizontalement et verticalement en vue de son utilisation dans un contexte volcanique. Cela passe, d’abord, par l’intégration d’une stratégie pour l’estimation du biais troposphérique dans le filtre de Kalman qui constitue le coeur du logiciel de calcul du Géocube. Cette amélioration est ensuite validée en utilisant les données de quelques réseaux GNSS permanents nationaux et internationaux. La deuxième partie consiste à étudier l’apport d’un réseau dense de Géocubes à l’étude du volcanisme à travers une expérience conduite sur le flanc sud-est de l’Etna, où cinq Géocubes ont été déployés entre le 12 Juillet 2016 et le 10 Juillet 2017. Les résultats obtenus et les enseignements tirés de cette expérimentation sont discutés et analysés. Enfin, nous validons les résultats obtenus avec les Géocubes en appliquant une technique PSI (Persistent Scatterer InSAR) sur des interférogrammes RADAR calculés à partir des données des satellites Sentinel-1A/B et qui couvrent la période de déploiement des Géocubes sur l’Etna. Ces deux méthodes (GPS et RADAR) se sont avérées complémentaires puisque le RADAR apporte la densité spatiale des mesures et le système Géocube la précision et la continuité temporelle. / The Geocube system is a network of wireless GPS sensors designed and developed by the Laboratory of Opto-Electronics, Metrology and Instrumentation (LOEMI) of the National Institute of Geographical and Forest Information (IGN) and maintained by the same laboratory and Ophelia-Sensors, the company responsible for its industrialization. Its purpose is to measure ground deformations with millimetre accuracy. This sensor network has the particularity of being very low in energy consumption, low cost, easy to install and easy to use. It is suited for use in harsh environments, such as volcanoes. This system has already been successfully tested in a previous works on the Argentière glacier and a Super-Sauze landslide in France. The first part of this thesis deals with the optimization of the Geocube system for larger networks, horizontally and vertically, in order to use it in a volcanic context. First, a new strategy to estimate the tropospheric bias has been implemented into the Kalman filter (the heart of the Geocube processing software) in real time and in post-processing. This improvement is then validated using data from some national and international permanent GNSS networks. The second part consists in studying the contribution of a dense Geocubes network to the study of volcanism through an experiment conducted on the southeastern flank of Etna, where five Geocubes were deployed between July, 12th 2016 and July, 10th 2017. The results obtained from this experiment are discussed and analysed. Finally, the results obtained with Geocubes are validated by applying a PSI (Persistent Scatterer InSAR) technique on RADAR interferograms calculated from Sentinel-1A/B satellite data covering the period of deployment of the Geocubes on Etna. These two methods (GPS and RADAR) turned out to be complementary since RADAR provides the spatial density of measurements and the Geocube system provides accuracy and temporal continuity.

The need for advancements in geospatial monitoring of structures has evolved naturally as structures have become larger, more complex, and technology has continued to rapidly develop. Greater building heights generally lead to greater challenges for surveyors, limiting the practical use of traditional measurement methods. For this reason, a new complimentary method was developed and implemented to support elevation monitoring activities during construction of the Salesforce Tower in San Francisco, California. While some studies have explored the use of strain gauges to monitor strain development within individual members, the primary contribution of this work is that it presents a practical and proven to be implementable approach to estimating elevation changes throughout a multi-story reinforced concrete core wall tower during construction while utilizing strain measurements acquired at intermittent levels. Construction in urban landscapes has the potential to impact existing infrastructure. Identifying and mitigating any associated construction impacts is critical to public safety and construction progress. The development of Automated Motorized Total Stations (AMTS) has provided an effective means to monitor deformations in structures adjacent to construction activity. AMTS provides real time results so that movements may be immediately identified and addressed. However, the design, implementation, management, and analysis of these systems has frequently been problematic. Inadequate monitoring specifications have led to systems that fail to perform as intended even when project requirements were satisfied. A collection of monitoring specifications and AMTS projects have been reviewed to identify why certain problems have occurred and recommendations have been made to increase the probability of success on monitoring projects. A deformation monitoring approach that defines location specific threshold values based on a statistical analysis of baseline measurements is also presented in this dissertation. Identifying potential causes for monitoring specifications to fail to perform as intended and a deformation monitoring approach that defines location specific threshold values are secondary contributions of this dissertation.

This thesis contains the development of a deformation monitoring software based on undifferenced GPS observations. Software like this can be used in alarm systems placed in areas where the earth is unstable. Systems like this can be used in areas where people are in risk of getting hurt, like in earthquake zones or in land slide areas, but they can also be useful when monitoring the movements in buildings, bridges and other artefacts. The main hypotheses that are tested are whether it is possible to detect deformations with undifferenced observations and if it is possible to reach the same accuracy in this mode as when working in a traditional mode where the observations are differenced. The development of a deformation monitoring software based on undifferenced GPS observations is presented. A complete mathematical model is given as well as implementation details. The software is developed in Matlab together with a GPS observation simulator. The simulator is mainly used for debugging purposes. The developed software is tested with both simulated and real observations. Results from tests with simulated observations show that it is possible to detect deformations in the order of a few millimetres with the software. Calculations with real observations give the same results. Further, the result from calculations in static mode indicates that the commercial software and the undifferenced software diverge a few millimetres, which probably depends on different implementations of the tropospheric corrections. In kinematic mode the standard deviation is about 1 millimetre larger in the undifferenced mode than in the double differenced mode. An initial test with different observation weighting procedures indicates that there is a lot of potential to improve the result by applying correct weights to the observations. This is one of the aims in the future work within this project. This thesis are sponsored by the Swedish Research Council for Enviroment, Agricultural Sciences and Spatial Planning, FORMAS within the framework “Monitoring of construction and detection of movements by GPS ref no. 2002-1257" / QC 20101108

Global Navigation Satellite System (GNSS) is nowadays a well-established and popular choice for various survey missions. Earlier studies indicate that BeiDou in combination with Global Positioning System (GPS), Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) and Galileo contributes to a lower uncertainty in 3D. Earlier studies indicates that GNSS achieves good enough quality and is reliable for deformation monitoring. The purpose of the study is to examine the potential of BeiDou using static deformation monitoring in 3D at the millimeter level, both individually and in combination with other satellite systems. The study detects deviations in a local network and by connecting to an external reference station using single- and double frequency as well as broadcast- and precise ephemeris. Data were collected using static measurements for three sessions within 2 days. The observation time for session 1 was 9 h and for sessions 2 and 3 was 4 h, respectively. The measurements were carried out using 3 points with the average baseline length of 791 m. A simulated deformation was applied at 2 occasions were each displacement was 5 mm in plane and 4,8 mm in height. Data was processed in Leica Infinity. The measured deformation was compared with the true displacement and with the rest of the satellite constellations. The result of the study shows that BeiDou in combination with GPS/GLONASS/Galileo in a local network achieves deviations between 0,2–1,0 mm in plane and 0,1–1,2 mm in height for every setting. Regarding processing with only BeiDou in a local network with broadcast ephemeris and the B1 frequency, the result indicates deviations between 0,2–1,9 mm in plane and 0,4–1,0 mm in height. Further processing with precise ephemeris the deviations was calculated between 0,2–1,8 mm in plane and 0,9–4,6 mm in height. Larger deviations were obtained using the external reference station MAR6. The outcomes of this study indicate that there is a possibility to use BeiDou individually for deformation monitoring if broadcast- and precise ephemeris with frequency B1 are used. BeiDou in combination with GPS/GLONASS/Galileo indicates deviations at millimeter level (&lt;1,2 mm) in 3D. BeiDou as a complement achieves equivalent deviations in comparison to GPS/GLONASS/Galileo. The conclusion indicates that BeiDou as a complement is useful for static deformation monitoring. Further conclusions indicate that an external reference station should not be used for deformation monitoring. BeiDou can, when using B1 frequency and precise ephemeris, detect millimeter displacements for shorter sessions. / Global Navigation Satellite System (GNSS) är idag ett väletablerat och populärt val vid diverse mättekniska uppdrag. Tidigare studier tyder på att BeiDou i kombination med Global Positioning System (GPS), Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) och Galileo bidrar till en lägre osäkerhet i 3D. Tidigare studier visar att GNSS uppnår tillräckligt hög kvalitet för att anses tillförlitligt vid deformationsövervakning. Syftet med studien är att undersöka BeiDou och dess potential vid statisk deformationsövervakning i 3D på millimeternivå, både enskilt och i kombination med andra satellitsystem. Vidare detekteras skillnader i ett lokalt nätverk och med anslutning mot en extern referensstation med enkel- och flerfrekvens samt utsändaoch precisa bandata. Data samlades in via statisk mätning under två dagar, i tre sessioner, där session 1 uppgick till 9 timmar och session två samt tre till fyra timmar vardera. Mätningarna genomfördes på tre punkter med en genomsnittlig baslinjelängd på 791 m. En simulerad deformation pågick under två tillfällen där vardera rörelsen var 5 mm i plan och 4,8 mm i höjd. Data bearbetades i Leica Infinity. Den mätta deformationen jämfördes mot den faktiska förflyttningen samt mot övriga satellitkonstellationer. Studiens resultat visar att BeiDou i kombination med GPS/GLONASS/Galileo i ett lokalt nätverk erhöll avvikelser mellan 0,2–1,0 mm i plan och 0,1–1,2 mm i höjd för samtliga inställningar. Angående bearbetning med BeiDou enskilt i ett lokalt nätverk beräknat med utsända bandata och frekvensen B1 erhöll resultatet avvikelser på 0,2–1,9 mm i plan och 0,4–1,0 mm i höjd. Vid efterbehandling med precisa bandata beräknades avvikelserna till 0,2–1,8 mm i plan och 0,9–4,6 mm i höjd. Större avvikelser erhölls vid bearbetning mot den externa referensstationen. Studiens slutsatser visar att möjligheten finns att använda BeiDou enskilt för deformationsövervakning med både utsända- och precisa bandata och frekvensen B1. BeiDou i kombination med GPS/GLONASS/Galileo visar avvikelser på millimeternivå (&lt;1,2 mm) i 3D. I jämförelse med GPS/GLONASS/Galileo erhåller mätningar med BeiDou som komplement ingen signifikant avvikelse. Slutsatsen tyder på att BeiDou som komplement uppnår likvärdig kvalitet som GPS/GLONASS/Galileo och är användbart vid statisk deformationsövervakning. Ytterligare slutsatser tyder på att anslutning mot en extern referensstation inte bör användas. BeiDou med enkelfrekvensen B1 med precisa bandata har även potential att detektera förflyttningar på millimeternivå vid kortare sessioner.

[ES] Los sistemas de alerta temprana en tiempo real son instrumentos claves para vigilar posibles desastres geológicos como terremotos, tsunamis, actividades volcánicas, hundimiento del terreno o deslizamientos de ladera. Durante las últimas décadas, el número de personas afectadas por los diversos desastres geológicos ha aumentado de forma sustancial. Las consecuencias negativas de estos desastres afectan a la población y a las infraestructuras con diferentes niveles de gravedad, pudiendo llegar a tener un impacto elevado en pérdidas humanas y económicas. Sin embargo, los sistemas de alerta temprana tienen la capacidad de proporcionar avisos adecuados y confiables, lo que puede llevar a minimizar las pérdidas humanas y económicas. El método de Posicionamiento Puntual Preciso en tiempo real (RT-PPP) desempeña un papel importante como parte de los sistemas de alerta temprana; debido a su capacidad para proporcionar seguimiento en tiempo real, cobertura global y su capacidad de obtención de mediciones precisas en tiempo real adquiridas por un solo receptor. A pesar de esto, el método (RT-PPP) utiliza productos para la corrección de la órbita y los relojes de los satélites (productos SSR) que son sensibles de los errores de la tecnología GNSS. Como consecuencia, estos errores pueden afectar la disponibilidad y fiabilidad de los sistemas de alerta temprana basados en la técnica RT-PPP. Debido a estos errores, se pueden llegar a generar avisos falsos, algunos de estos errores son: largos tiempos de inicialización, falta de continuidad y exactitud en los resultados, mala calidad de corrección de órbita y reloj de los satélites, mala resolución de la ambigüedad, etc. Además, la mala geometría de los satélites y la latencia de los productos SSR afectan gravemente el rendimiento del posicionamiento PPP en tiempo real. Este trabajo de investigación, se enfoca, en una primera parte, en el análisis de los efectos y los métodos de mitigación de la latencia de los productos de corrección en tiempo real. El International GNSS Service (IGS) proporciona productos oficiales para materializar la técnica de PPP en tiempo real, estos productos contienen correcciones para las órbitas y los relojes de los satélites que se generan como combinación de los calculados en los diferentes centros de cálculo repartidos por el mundo. Este proceso de combinación aumenta la latencia y, por tanto, su impacto en la solución RT-PPP, afectando el desempeño de cualquier sistema de alerta temprana basada en RT-PPP. Así, en esta tesis, se usará el enfoque de Aprendizaje Automático para resolver el problema de la latencia, intentando predecir los valores de las correcciones en los productos SSR para el tiempo de la latencia. Se han utilizado los modelos de Support Vector Regression (SVR) y de media móvil integrada autorregresiva (ARIMA) para la predicción, necesitando, en el proceso, la implantación de ventanas deslizantes para entrenar y parametrizar los modelos de aprendizaje automático. En cuanto al desempeño del sistema RT-PPP de alerta temprana, este trabajo de investigación ha evaluado, estadísticamente, varios modelos de aprendizaje automático, entre ellos los métodos de Árbol de decisión, Random Forest, Máquina de vectores de soporte (SVM), K vecinos más cercanos, Regresión logística, y el modelo de boosting extremo por gradientes (XGB). El análisis de los resultados indica que los modelo de XGB y Random Forest muestran los resultados más coherentes y precisos con 97 y 99 porciento de precisión. Asimismo, el modelo XGB muestra menos tendencia a iniciar falsas alarmas con un 2,48 por ciento en comparación con el 16,28 por ciento del modelo Random Forest.A partir de los resultados de la investigación, se derivan un conjunto de pruebas estadísticas para evaluar el desempeño de los sistemas de alerta temprana establecidos. Estas pruebas estadísticas pueden evaluar la capacidad de los modelos de aprendizaje automático utilizados con a la detecciónde deformaciones. / [CA] Els sistemes d'alerta primerenca en temps real són instruments claus per vigilar possibles desastres geològics com ara terratrèmols, tsunamis, activitats volcàniques, enfonsament del terreny o lliscaments de vessant. Durant les darreres dècades, el nombre de persones afectades pels diversos desastres geològics ha augmentat de manera substancial. Les conseqüències negatives d'aquests desastres afecten la població i les infraestructures amb diferents nivells de gravetat i poden arribar a tenir un impacte elevat en pèrdues humanes i econòmiques. Tot i això, els sistemes d'alerta primerenca tenen la capacitat de proporcionar avisos adequats i fiables, la qual cosa pot portar a minimitzar les pèrdues humanes i econòmiques. El mètode de Posicionament Puntual Precís en temps real (RT-PPP) té un paper important com a part dels sistemes d'alerta primerenca; a causa de la seva capacitat per proporcionar seguiment en temps real, cobertura global i la seva capacitat d'obtenció de mesuraments precisos en temps real adquirits per un sol receptor.Tot i això, el mètode RT-PPP utilitza productes per a la correcció de l'òrbita i els rellotges dels satèl·lits (productes SSR) que són sensibles als errors de la tecnologia GNSS. Com a conseqüència, aquests errors poden afectar la disponibilitat i la fiabilitat dels sistemes d'alerta primerenca basats en la tècnica RT-PPP. A causa d'aquests errors, es poden arribar a generar avisos falsos, alguns d'aquests errors són: llargs temps d'inicialització, manca de continuïtat i exactitud als resultats, mala qualitat de correcció d'òrbita i rellotge dels satèl·lits, mala resolució de l'ambigüitat, etc. A més, la mala geometria dels satèl·lits i la latència dels productes SSR afecten greument el rendiment del posicionament PPP en temps real. Aquest treball de recerca s'enfoca, en una primera part, a l'anàlisi dels efectes i els mètodes de mitigació de la latència dels productes de correcció en temps real. L'International GNSS Service (IGS) proporciona productes oficials per materialitzar la tècnica de PPP en temps real, aquests productes contenen correccions per a les òrbites i els rellotges dels satèl·lits que es generen com a combinació dels calculats als diferents centres de càlcul repartits pel món. Aquest procés de combinació augmenta la latència i, per tant, el seu impacte en la solució RT-PPP, afectant l'exercici de qualsevol sistema d'alerta primerenca basada en RT-PPP. Així, en aquesta tesi, s'usarà l'enfocament d'aprenentatge automàtic (Machine Learning) per resoldre el problema de la latència, intentant predir els valors de les correccions en els productes SSR per al temps de la latència. S'han utilitzat els models de Support Vector Regression (SVR) i de mitjana mòbil integrada autoregressiva (ARIMA) per a la predicció, necessitant, en el procés, la implantació de finestres lliscants per entrenar i parametritzar els models d'aprenentatge automàtic. Els resultats de la investigació de la part de la latència han indicat que els models SVR i ARIMA podran mitigar la influència de la latència per als principals sistemes de navegació per satèl·lit (GPS i GLONASS) al voltant d'un vint per cent. El model SVR va mostrar una lleugera tendència a predir valors atípics; tot i això, el temps d'execució del SVR és significativament menor que el temps de processament del model ARIMA. Pel que fa a desenvolupament del sistema RT-PPP d'alerta primerenca, aquest treball de recerca ha avaluat, estadísticament, diversos models d'aprenentatge automàtic, entre ells els mètodes d'Arbre de Decisió, Random Forest, Màquina de Vectors de Suport (SVM), K veïns més propers, Regressió Logística, i el model de Boosting Extrem per gradients (XGB).L'anàlisi dels resultats indica que els models de XGB i Random Forest mostren els resultats més coherents i precisos amb 97i99 porcent de precisió respectivament. Així mateix, el model XGB mostra menys tendència a iniciar falses alarmes amb un 2,48% en comparació del 16,28% del model RF. / [EN] Real-Time Early Warning Systems are a critical approach implemented for monitoring geo-hazard disasters such as earthquakes, tsunamis, volcanic activities, and land subsidence. The Earth's population has experienced a substantial increasement, consequently exposing a growing number of people to the effects of various geo-hazard disasters. These influences could impact citizens and countries at different severity levels, reaching high costs in terms of human beings and economic losses. However, the early warning system's ability to initiate proper and reliable warnings significantly impacts in disaster cost reductions in terms of saving lives, reducing home and infrastructure damages, and mitigating economic losses. Real-Time Precise Point Positioning (RT-PPP) plays a significant role as part of the Early Warning Systems, due to its potential to provide real-time tracking and global coverage and its reliance on precise real-time measurements acquired from only one receiver. However, the RT-PPP approach applies State Space Representation (SSR) products that are highly sensitive to several GNSS error sources. As a result, the warning system's availability and reliability are negatively impacted. It may even be triggered to issue false warnings by factors such as long initialization times, convergence losses, due to poor quality of orbital and clock corrections, ambiguity resolutions, or/and multipath error. Furthermore, poor satellite geometry and the latency of SSR products severely affect the performance of real-time PPP positioning. In this research, we investigated the effect and mitigation of latency on real-time correction products. The International GNSS Services (IGS) provides official real-time products for RT-PPP; these products contain clock and orbit corrections, among others, and they are the main research concerns as the combination process increases the latency impact on both RT-PPP results and influences the early warning systems performance based on this positioning technique. In this research, investigations into the potentiality of using machine learning approaches to overcome latency problems were carried out. The research examines the Support Vector Regression (SVR) and Autoregressive Integrated Moving Average (ARIMA) machine learning models to predict the corrections broadcasted in SSR products that have a big capability in order to be used instead of the corrections impacted with latency The research results regarding latency showed that the SVR and ARIMA models could mitigate the latency influences for the primary navigation satellite systems GPS and GLONASS by around twenty percent. The SVR model showed a tendency to predict outliers; however, the execution time for the SVR is significantly faster than the ARIMA model processing time. Regarding the performance of the RT-PPP early warning system, the research statistically evaluates several machine learning models, including decision tree, random forest, support vector classifier, K nearest neighbors, logistic regression, and extreme gradient boosting models as machine learning approaches for establishing an early warning system. The extreme gradient boosting and random forest models were more accurate than the other utilized models, with 97 and 99 percent overall accuracy. At the same time, the extreme gradient boosting showed less tendency to initiate false alarms, with 2.48 percent compared to 16.28 percent for the random forest model. From the research findings, we derived a set of statistical assessments to evaluate the performance of the established early warning systems. These statistical assessments can evaluate the ability of the utilized machine learning models regarding deformation detections and the model's tendency to initiate false warnings. The study's results confirmed that extreme gradient boosting is the most effective machine learning technique for creating an early warning system. / Qafisheh, MWA. (2024). Establishing a Real-time Precise Point Positioning Early Warning System [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/206740

Persistent Scatterer Interferometric techniques are very powerful geodetic tools for land deformation monitoring that offer the typical advantages of the satellite remote sensing SAR (Synthetic Aperture Radar) systems : a wide coverage at a relatively high resolution. Those techniques are based on the analysis of a set of SAR images acquired over a given area. They overcome the decorrelation problem by identifying elements (in resolution cells) with a high quality returned SAR signal which remains stable in a series of interferograms. These techniques have been useful for the analysis of urban areas, where man-made objects produce good reflections that dominate over the background scattering, as well as in field areas where the density of infrastructures is more limited. Typically, PSI technique requires an approximate a priori temporal model for the detection of the deformation, even though characterizing the temporal evolution of a deformation is commonly one of the objectives of any study.This work is focused on a particular PSI technique, which is named Stable Point Network (SPN) and that it has been completely developed by Altamira Information in 2003. The work concisely outlines the main characteristics of this technique, and describes its main products: average deformation maps, deformation time series of the measured points, and the so-called maps of the residual topographic error, which are used to precisely geocode the PSI products. The main objectives of this PhD are the identification and analysis of the drawbacks of this processing chain, and the development of new tools and methodologies in order to overcome them. First, the performances of the SPN technique are examined and illustrated by means of practical cases (based on real test sites made with data coming from different sensors) and simulated scenarios.Thus, the main drawbacks of the technique are identified and discussed, such as the lack of automatic quality control parameters, the evaluation of the input data quality, the selection of good points for the measurements and the use of a functional model to unwrap the phases based on a linear deformation trend in time. Then, different enhancements are proposed. In particular, the automatic quality control of the coregistration procedure has been introduced through the analysis of the inter-pixel position of some natural point targets-like pixels identified within the images. The enhancements in the selection of the final points of measurements (the final PSI map) come by means of the analysis of the SAR signal signature of the strong targets presented within the image, in order to select only the center of the main lobe as point of measurement. The introduction of robustness within some critical steps of the technique is done by means of the analysis of the rotational of the estimates in close loops within a network of relative measurements, and by means of the implementation of a different integration methodology, which can be ran in parallel in order to compare it with the classical one. Finally, the main drawback of the technique, the use of a linear model for the detection of ground deformations, is addressed with the development of a new fitting methodology which allows possible change of trends within the analyzed time span. All those enhancements are evaluated with the use of real examples of applications and with simulated data. In particular, the new methodology for detecting non-linear ground deformations has been tested in the city of Paris, where a large stacking of ERS1/2 and ENVISAT SAR images are available. Those images are covering a very large time period of analysis at where some known non-linear ground deformations where occurring