Advanced interpretation of subsidence in Murcia (SE Spain) using A-DInSAR data – modelling and validation

Abstract. Subsidence is a natural hazard that affects wide areas in the world causing important economic costs annually. This phenomenon has occurred in the metropolitan area of Murcia City (SE Spain) as a result of groundwater overexploitation. In this work aquifer system subsidence is investigated using an advanced differential SAR interferometry remote sensing technique (A-DInSAR) called Stable Point Network (SPN). The SPN derived displacement results, mainly the velocity displacement maps and the time series of the displacement, reveal that in the period 2004–2008 the rate of subsidence in Murcia metropolitan area doubled with respect to the previous period from 1995 to 2005. The acceleration of the deformation phenomenon is explained by the drought period started in 2006. The comparison of the temporal evolution of the displacements measured with the extensometers and the SPN technique shows an average absolute error of 3.9±3.8 mm. Finally, results from a finite element model developed to simulate the recorded time history subsidence from known water table height changes compares well with the SPN displacement time series estimations. This result demonstrates the potential of A-DInSAR techniques to validate subsidence prediction models as an alternative to using instrumental ground based techniques for validation.

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Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

Symmetry ◽

10.3390/sym13081544 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1544

Author(s):

Ashis Kumar Mandal ◽

Rikta Sen ◽

Saptarsi Goswami ◽

Basabi Chakraborty

Keyword(s):

Time Series ◽

Wind Direction ◽

Power Plants ◽

Prediction Models ◽

Short Term Memory ◽

Meteorological Data ◽

Absolute Error ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term basis. To build the multivariate time series models, we considered all possible combinations of temperature, humidity, and wind direction variables along with GHI as inputs and developed seven multivariate models, while in the univariate model, we considered only GHI variability. We collected the meteorological data for Guntur from 1 January 2016 to 31 December 2016 and built 12 datasets, each containing variability of GHI, temperature, humidity, and wind direction of a month. We then constructed the models, each of which measures up to 2 h ahead of forecasting of GHI. Finally, to measure the symmetry among the models, we evaluated the performances of the prediction models using root mean square error (RMSE) and mean absolute error (MAE). The results indicate that, compared to the univariate method, each multivariate LSTM performs better in the very short-term GHI prediction task. Moreover, among the multivariate LSTM models, the model that incorporates the temperature variable with GHI as input has outweighed others, achieving average RMSE values 0.74 W/m2–1.5 W/m2.

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Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error

The American Statistician ◽

10.1080/00031305.2016.1148631 ◽

2016 ◽

Vol 70 (3) ◽

pp. 285-292 ◽

Cited By ~ 9

Author(s):

Nicholas G. Reich ◽

Justin Lessler ◽

Krzysztof Sakrejda ◽

Stephen A. Lauer ◽

Sopon Iamsirithaworn ◽

...

Keyword(s):

Time Series ◽

Prediction Models ◽

Mean Absolute Error ◽

Time Series Prediction ◽

Absolute Error

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DEVELOPMENT OF STREAMFLOW DROUGHT INDICES IN THE MORAVA RIVER BASIN

Proceedings of the XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management ◽

10.15407/uhmi.conference.01.14 ◽

2020 ◽

Author(s):

Ondrej Ledvinka ◽

◽

Pavel Coufal ◽

Keyword(s):

Time Series ◽

Kendall Test ◽

Drought Indices ◽

Drought Period ◽

Left Hand ◽

Low Flows ◽

Current Period ◽

Catchment Size ◽

Morava River ◽

The Stability

The territory of Czechia currently suffers from a long-lasting drought period which has been a subject of many studies, including the hydrological ones. Previous works indicated that the basin of the Morava River, a left-hand tributary of the Danube, is very prone to the occurrence of dry spells. It also applies to the development of various hydrological time series that often show decreases in the amount of available water. The purpose of this contribution is to extend the results of studies performed earlier and, using the most updated daily time series of discharge, to look at the situation of the so-called streamflow drought within the basin. 46 water-gauging stations representing the rivers of diverse catchment size were selected where no or a very weak anthropogenic influences are expected and the stability and sensitivity of profiles allow for the proper measurement of low flows. The selected series had to cover the most current period 1981-2018 but they could be much longer, which was considered beneficial for the next determination of the development direction. Various series of drought indices were derived from the original discharge series. Specifically, 7-, 15- and 30-day low flows together with deficit volumes and their durations were tested for trends using the modifications of the Mann– Kendall test that account for short-term and long-term persistence. In order to better reflect the drivers of streamflow drought, the indices were considered for summer and winter seasons separately as well. The places with the situation critical to the future water resources management were highlighted where substantial changes in river regime occur probably due to climate factors. Finally, the current drought episode that started in 2014 was put into a wider context, making use of the information obtained by the analyses.

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Evaluation of Process Control Schemes for Sour Water Strippers in Petroleum Refining

Processes ◽

10.3390/pr9020363 ◽

2021 ◽

Vol 9 (2) ◽

pp. 363

Author(s):

Chii-Dong Ho ◽

Yih-Hang Chen ◽

Chao-Min Chang ◽

Hsuan Chang

Keyword(s):

Feedback Control ◽

Prediction Models ◽

Feedforward Control ◽

Absolute Error ◽

Dynamic Responses ◽

Control Schemes ◽

Online Measurements ◽

Measurement Delay ◽

Water Feed ◽

Sour Water

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).

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Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-1 SAR Data

Journal of Glaciology ◽

10.1017/s0022143000001878 ◽

1999 ◽

Vol 45 (150) ◽

pp. 370-383 ◽

Cited By ~ 7

Author(s):

Kim Morris ◽

Shusun Li ◽

Martin Jeffries

Keyword(s):

Time Series ◽

Sea Ice ◽

Large Scale ◽

Vector Fields ◽

Lateral Displacement ◽

Motion Vector ◽

Sar Interferometry ◽

East Siberian Sea ◽

Pack Ice ◽

Sar Data

Abstract Synthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

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Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

Journal of Alzheimer s Disease ◽

10.3233/jad-200906 ◽

2021 ◽

Vol 79 (4) ◽

pp. 1533-1546

Author(s):

Mithilesh Prakash ◽

Mahmoud Abdelaziz ◽

Linda Zhang ◽

Bryan A. Strange ◽

Jussi Tohka ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Prediction Models ◽

Predictive Performance ◽

Absolute Error ◽

Disease Stage ◽

Disease Assessment ◽

Disease Category ◽

Modal Data ◽

Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

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Development of a Novel Damage Detection Framework for Truss Railway Bridges Using Operational Acceleration and Strain Response

Vibration ◽

10.3390/vibration4020028 ◽

2021 ◽

Vol 4 (2) ◽

pp. 422-445

Author(s):

Md Riasat Azim ◽

Mustafa Gül

Keyword(s):

Damage Detection ◽

Damage Identification ◽

Numerical Study ◽

Damage Index ◽

Strain Analysis ◽

Time History ◽

Principal Component ◽

Element Model ◽

Railway Bridges ◽

Truss Bridge

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis.

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A Time History of Pre- and Post-Bomb Radiocarbon in the Barents Sea Derived from Arcto-Norwegian Cod Otoliths

Radiocarbon ◽

10.1017/s0033822200041515 ◽

2001 ◽

Vol 43 (2B) ◽

pp. 843-855 ◽

Cited By ~ 30

Author(s):

John M Kalish ◽

Reidar Nydal ◽

Kjell H Nedreaas ◽

George S Burr ◽

Gro L Eine

Keyword(s):

Time Series ◽

Ocean Circulation ◽

General Circulation ◽

Barents Sea ◽

Dissolved Inorganic Carbon ◽

Time History ◽

General Circulation Models ◽

Fine Tuning ◽

Birth Date ◽

The Barents Sea

Radiocarbon measured in seawater dissolved inorganic carbon (DIC) can be used to investigate ocean circulation, atmosphere/ocean carbon flux, and provide powerful constraints for the fine-tuning of general circulation models (GCMs). Time series of 14C in seawater are derived most frequently from annual bands of hermatypic corals. However, this proxy is unavailable in temperate and polar oceans. Fish otoliths, calcium carbonate auditory, and gravity receptors in the membranous labyrinths of teleost fishes, can act as proxies for 14C in most oceans and at most depths. Arcto-Norwegian cod otoliths are suited to this application due to the well-defined distribution of this species in the Barents Sea, the ability to determine ages of individual Arcto-Norwegian cod with a high level of accuracy, and the availability of archived otoliths collected for fisheries research over the past 60 years. Using measurements of 14C derived from Arcto-Norwegian cod otoliths, we present the first pre- and post-bomb time series (1919–1992) of 14C from polar seas and consider the significance of these data in relation to ocean circulation and atmosphere/ocean flux of 14C. The data provide evidence for a minor Suess effect of only 0.2‰ per year between 1919 and 1950. Bomb 14C was evident in the Barents Sea as early as 1957 and the highest 14C value was measured in an otolith core from a cod with a birth date of 1967. The otolith 14C data display key features common to records of 14C obtained from a Georges Bank mollusc and corals from the tropical and subtropical North Atlantic.

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Diesel Droplet Diffusion in Isotropic Turbulence With Digital Holographic Cinematography

Volume 2: Fora ◽

10.1115/fedsm2005-77423 ◽

2005 ◽

Author(s):

Balaji Gopalan ◽

Edwin Malkiel ◽

Jian Sheng ◽

Joseph Katz

Keyword(s):

Time Series ◽

High Speed ◽

Isotropic Turbulence ◽

Fluid Motion ◽

Three Dimensional ◽

Time History ◽

Sample Volume ◽

Velocity Autocorrelation Function ◽

Digital Cameras ◽

Velocity Autocorrelation

High-speed in-line digital holographic cinematography was used to investigate the diffusion of droplets in locally isotropic turbulence. Droplets of diesel fuel (0.3–0.9mm diameter, specific gravity of 0.85) were injected into a 37×37×37mm3 sample volume located in the center of a 160-liter tank. The turbulence was generated by 4 spinning grids, located symmetrically in the corners of the tank, and was characterized prior to the experiments. The sample volume was back illuminated with two perpendicular collimated beams of coherent laser light and time series of in-line holograms were recorded with two high-speed digital cameras at 500 frames/sec. Numerical reconstruction generated a time series of high-resolution images of the droplets throughout the sample volume. We developed an algorithm for automatically detecting the droplet trajectories from each view, for matching the two views to obtain the three-dimensional tracks, and for calculating the time history of velocity. We also measured the mean fluid motion using 2-D PIV. The data enabled us to calculate the Lagrangian velocity autocorrelation function.

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Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

Autex Research Journal ◽

10.1515/aut-2015-0018 ◽

2016 ◽

Vol 16 (2) ◽

pp. 43-50 ◽

Cited By ~ 8

Author(s):

Samander Ali Malik ◽

Assad Farooq ◽

Thomas Gereke ◽

Chokri Cherif

Keyword(s):

Neural Networks ◽

Linear Regression ◽

Multiple Linear Regression ◽

Tensile Properties ◽

Regression Models ◽

Prediction Models ◽

Research Work ◽

Absolute Error ◽

Knowledge Domain ◽

Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

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