scholarly journals Statistical Time-Series Analysis of Interferometric Coherence from Sentinel-1 Sensors for Landslide Detection and Early Warning

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6799
Author(s):  
Marios Tzouvaras
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Statistical Significance ◽  
Early Warning Systems ◽  
Time Of Day ◽  
Landslide Occurrence ◽  
Geotechnical Investigations ◽  
Wide Range ◽  
Sar Imagery

Landslides are one of the most destructive natural hazards worldwide, affecting greatly built-up areas and critical infrastructure, causing loss of human lives, injuries, destruction of properties, and disturbance in everyday commute. Traditionally, landslides are monitored through time consuming and costly in situ geotechnical investigations and a wide range of conventional means, such as inclinometers and boreholes. Earth Observation and the exploitation of the freely available Copernicus datasets, and especially Sentinel-1 Synthetic Aperture Radar (SAR) images, can assist in the systematic monitoring of landslides, irrespective of weather conditions and time of day, overcoming the restrictions arising from in situ measurements. In the present study, a comprehensive statistical analysis of coherence obtained through processing of a time-series of Sentinel-1 SAR imagery was carried out to investigate and detect early indications of a landslide that took place in Cyprus on 15 February 2019. The application of the proposed methodology led to the detection of a sudden coherence loss prior to the landslide occurrence that can be used as input to Early Warning Systems, giving valuable on-time information about an upcoming landslide to emergency response authorities and the public, saving numerous lives. The statistical significance of the results was tested using Analysis of Variance (ANOVA) tests and two-tailed t-tests.

scholarly journals Investigation on Surface Tilting in the Failure Process of Shallow Landslides

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2662
Author(s):  
Shifan Qiao ◽  
Chaobo Feng ◽  
Pengkun Yu ◽  
Junkun Tan ◽  
Taro Uchimura ◽  
...  
Keyword(s):  
Early Warning ◽  
Slip Surface ◽  
Failure Process ◽  
Field Tests ◽  
Early Warning Systems ◽  
Field Monitoring ◽  
Landslide Monitoring ◽  
Warning Systems ◽  
Tilt Sensors

In recent decades, early warning systems to predict the occurrence of landslides using tilt sensors have been developed and employed in slope monitoring due to their low cost and simple installation. Although many studies have been carried out to validate the efficiency of these early warning systems, few studies have been carried out to investigate the tilting direction of tilt sensors at the slope surface, which have revealed controversial results in field monitoring. In this paper, the tilting direction and the pre-failure tilting behavior of slopes were studied by performing a series of model tests as well as two field tests. These tests were conducted under various testing conditions. Tilt sensors with different rod lengths were employed to investigate the mechanism of surface tilting. The test results show that the surface tilting measured by the tilt sensors with no rods and those with short rods located above the slip surface are consistent, while the tilting monitored by the tilt sensors with long rods implies an opposite rotational direction. These results are important references to understand the controversial surface tilting behavior in in situ landslide monitoring cases and imply the correlation between the depth of the slip surface of the slope and the surface tilting in in situ landslide monitoring cases, which can be used as the standard for tilt sensor installation in field monitoring.

Performance analysis of regional landslide early warning based on soil moisture simulations

2020 ◽  
Author(s):  
Adrian Wicki ◽  
Manfred Stähli
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Performance Analysis ◽  
Early Warning ◽  
Soil Profile ◽  
Forecast Skill ◽  
Soil Moisture Dynamics ◽  
Landslide Early Warning ◽  
Moisture Dynamics

<p>In mountainous regions, rainfall-triggered landslides pose a serious risk to people and infrastructure, particularly due to the short time interval between activation and failure and their widespread occurrence. Landslide early warning systems (LEWS) have demonstrated to be a valuable tool to inform decision makers about the imminent landslide danger and to move people or goods at risk to safety. While most operational LEWS are based on empirically derived rainfall exceedance thresholds, recent studies have demonstrated an improvement of the forecast quality after the inclusion of in-situ soil moisture measurements.</p><p>The use of in-situ soil moisture sensors bears specific limitations, such as the sensitivity to local conditions, the disturbance of the soil profile during installation, and potential data quality issues and inhomogeneity of long-term measurements. Further, the installation and operation of monitoring networks is laborious and costly. In this respect, making use of modelled soil moisture could efficiently increase information density, and it would further allow to forecast soil moisture dynamics. On the other hand, numerical simulations are restricted by assumptions and simplifications related to the soil hydraulic properties and the water transfer in the soil profile. Ultimately, the question arises how reliable and representative landslide early warnings based on soil moisture simulations are compared to warnings based on measurements.</p><p>To answer this, we applied a state-of-the-art one-dimensional heat and mass transfer model (CoupModel, Jansson 2012) to generate time series of soil water content at 35 sites in Switzerland. The same sites and time period (2008-2018) were used in a previous study to compare the temporal variability of in-situ measured soil moisture to the regional landslide activity (currently under review in <em>Landslides</em>). The same statistical framework for soil moisture dynamics analysis, landslide probability modelling and landslide early warning performance analysis was applied to the modelled and the measured soil moisture time series. This allowed to directly compare the forecast skill of modelling-based with measurements-based landslide early warning.</p><p>In this contribution, we will highlight three steps of model applications: First, a straight-forward simulation to all 35 sites without site-specific calibration and using reference soil layering only, to assess the forecast skill as if no prior measurements were available. Second, a model simulation after calibration at each site using the existing soil moisture time series and information on the soil texture to assess the benefit of a thorough calibration process on the forecast skill. Finally, an application of the model to additional sites in Switzerland where no soil moisture measurements are available to assess the effect of increasing the soil moisture information density on the overall forecast skill.</p>

scholarly journals Analysis of Rhythmic Patterns Produced by Spinal Neural Networks

2007 ◽  
Vol 98 (5) ◽  
pp. 2807-2817 ◽  
Author(s):  
Y. Mor ◽  
A. Lev-Tov
Keyword(s):  
Time Series ◽  
Statistical Significance ◽  
Wavelet Coherence ◽  
Motor Patterns ◽  
Experimental Conditions ◽  
Wide Range ◽  
Continuous Frequency ◽  
Pattern Generators ◽  
The Fourier Transform ◽  
Short Time

A network of spinal neurons known as central pattern generator (CPG) produces the rhythmic motor patterns required for coordinated swimming, walking, and running in mammals. Because the output of this network varies with time, its analysis cannot be performed by statistical methods that assume data stationarity. The present work uses short-time Fourier (STFT) and wavelet-transform (WT) algorithms to analyze the nonstationary rhythmic signals produced in isolated spinal cords of neonatal rats during activation of the CPGs. The STFT algorithm divides the time series into consecutive overlapping or nonoverlapping windows and repeatedly applies the Fourier transform across the signal. The WT algorithm decomposes the signal using a family of wavelets varying in scale, resulting in a set of wavelet coefficients presented onto a continuous frequency range over time. Our studies revealed that a Morlet WT algorithm was the tool of choice for analyzing the CPG output. Cross-WT and wavelet coherence were used to determine interrelations between pairs of time series in time and frequency domain, while determining the critical values for statistical significance of the coherence spectra using Monte Carlo simulations of white-noise series. The ability of the cross-Morlet WT and cross-WT coherence algorithms to efficiently extract the rhythmic parameters of complex nonstationary output of spinal pattern generators over a wide range of frequencies with time is demonstrated in this work under different experimental conditions. This ability can be exploited to create a quantitative dynamic portrait of experimental and clinical data under various physiological and pathological conditions.

scholarly journals Simulated or measured soil moisture: Which one is adding more value to regional landslide early warning?

2021 ◽  
Author(s):  
Adrian Wicki ◽  
Per-Erik Jansson ◽  
Peter Lehmann ◽  
Christian Hauck ◽  
Manfred Stähli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Prediction Models ◽  
Numerical Models ◽  
Forecast Model ◽  
Early Warning Systems ◽  
Transfer Model ◽  
Statistical Framework ◽  
Landslide Early Warning

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWS). However, it is still unclear which source of information – numerical models or in-situ measurements – are of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019 using a physically-based 1D soil moisture transfer model (CoupModel). A common parametrization set was defined for all sites except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in-situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and ROC analysis was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different CoupModel parametrizations, to various distances between simulation sites and landslides, and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance-dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

scholarly journals A Study on the Slope Failure Monitoring of a Model Slope by the Application of a Displacement Sensor

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Sungyong Park ◽  
Hyuntaek Lim ◽  
Bibek Tamang ◽  
Jihuan Jin ◽  
Seungjoo Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Slope Failure ◽  
Early Warning Systems ◽  
Displacement Sensor ◽  
Scale Model ◽  
Economic Losses ◽  
Real Time Monitoring ◽  
Landslide Occurrence ◽  
Model Slope

Many causalities and economic losses are caused by natural disasters, such as landslides and slope failures, every year. This suggests that there is a need for an early warning system to mitigate casualties and economic losses. Most of the studies on early warning systems have been carried out by predicting landslide-prone areas, but studies related to the prediction of landslide occurrence time points by the real-time monitoring of slope displacement are still insufficient. In this study, a displacement sensor and an Internet of Things (IoT) monitoring system were combined together, to monitor slope failure through cutting experiments of a real-scale model slope. Real-time monitoring of the slope movement was performed simultaneously via a low-cost, efficient, and easy-to-use IoT system. Based on the obtained displacement data, an inverse displacement analysis was performed. Finally, a slope instrumentation standard was proposed based on the slope of the inverse displacement for early evacuation before slope failure.

Risk-based modeling of early warning systems for pollution accidents

2002 ◽  
Vol 46 (3) ◽  
pp. 41-49 ◽  
Author(s):  
W.M. Grayman ◽  
R.M. Males
Keyword(s):  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Ohio River ◽  
Warning Systems ◽  
Simulation Techniques ◽  
Wide Range ◽  
Contamination Event ◽  
Probabilistic Nature ◽  
The Impact

An early warning system is a mechanism for detecting, characterizing and providing notification of a source water contamination event (spill event) in order to mitigate the impact of contamination. Spill events are highly probabilistic occurrences with major spills, which can have very significant impacts on raw water sources of drinking water, being relatively rare. A systematic method for designing and operating early warning systems that considers the highly variable, probabilistic nature of many aspects of the system is described. The methodology accounts for the probability of spills, behavior of monitoring equipment, variable hydrology, and the probability of obtaining information about spills independent of a monitoring system. Spill Risk, a risk-based model using Monte Carlo simulation techniques has been developed and its utility has been demonstrated as part of an AWWA Research Foundation sponsored project. The model has been applied to several hypothetical river situations and to an actual section of the Ohio River. Additionally, the model has been systematically applied to a wide range of conditions in order to develop general guidance on design of early warning systems.

scholarly journals Potential of Pan-European Seasonal Hydrometeorological Drought Forecasts Obtained from a Multihazard Early Warning System

2020 ◽  
Vol 101 (4) ◽  
pp. E368-E393 ◽  
Author(s):  
Samuel Jonson Sutanto ◽  
Henny A. J. Van Lanen ◽  
Fredrik Wetterhall ◽  
Xavier Llort
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Water Cycle ◽  
Warning System ◽  
Meteorological Drought ◽  
Early Warning Systems ◽  
Drought Indices ◽  
Drought Forecasting ◽  
Wide Range ◽  
Drought Early Warning

Abstract Drought early warning systems (DEWS) have been developed in several countries in response to high socioeconomic losses caused by droughts. In Europe, the European Drought Observatory (EDO) monitors the ongoing drought and forecasts soil moisture anomalies up to 7 days ahead and meteorological drought up to 3 months ahead. However, end users managing water resources often require hydrological drought warning several months in advance. To answer this challenge, a seasonal pan-European DEWS has been developed and has been running in a preoperational mode since mid-2018 under the EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) project. The ANYWHERE DEWS (AD-EWS) is different than other operational DEWS in the sense that the AD-EWS provides a wide range of seasonal hydrometeorological drought forecasting products in addition to meteorological drought, that is, a broad suite of drought indices that covers all water cycle components (drought in precipitation, soil moisture, runoff, discharge, and groundwater). The ability of the AD-EWS to provide seasonal drought predictions in high spatial resolution (5 km × 5 km) and its diverse products mark the AD-EWS as a preoperational drought forecasting system that can serve a broad range of different users’ needs in Europe. This paper introduces the AD-EWS and shows some examples of different drought forecasting products, the drought forecast score, and some examples of a user-driven assessment of forecast trust levels.

Seasonal forecasting of groundwater levels in Europe using Pastas time series models and ECMWF SEAS5 forecasts

2021 ◽  
Author(s):  
Raoul Collenteur ◽  
Steffen Birk
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Groundwater Level ◽  
Early Warning Systems ◽  
Seasonal Forecasts ◽  
Groundwater System ◽  
Groundwater Levels ◽  
Informed Decisions ◽  
Groundwater Drought ◽  
Forecast Quality

<p>Groundwater level monitoring is an important way for water resource managers to obtain information on the state of the groundwater system and make informed decisions. In many countries around Europe the right to abstract groundwater (e.g., for drinking water or irrigation purposes) is bound to observed groundwater levels. In particular during and after periods of drought such rights to abstract groundwater may be temporarily denied. As climate change is expected to increase the frequency and intensity of hydrological extremes, severe drought events become more likely, potentially increasing the gap between groundwater demand and supply. An early warning system of a potential groundwater drought could help water managers make informed decisions in advance, to try and counteract the effects of drought. In this study we investigate the use of seasonal forecasts from the ECMWF SEAS5 system to forecast groundwater levels around Europe. The groundwater levels are simulated using a non-linear time series model using impulse response functions as implemented in Pastas (https://github.com/pastas/pastas). Forecasts are compared to groundwater level simulations based on historic meteorological data from the E-OBS database. The methods are tested on 10 long-term (30 years) groundwater level time series. The use of the Standardized Groundwater Index (SGI) is tested to assess the forecast quality and communicate results with decision makers. Bias-correction of the SEAS5 forecasts is found to be necessary to forecast groundwater levels at this local scale. Preliminary results show that the forecast quality depends on the memory effect of the groundwater system, which can be characterized by the auto-correlation of the time series. In addition, it is found that the groundwater levels forecasts have smaller ranges in spring then in the winter months. This may be explained by the fact that groundwater levels in spring are more dependent on evaporation than on precipitation and that forecast of the first are better than those of the latter. The results from this study may be used to improve early warning systems that forecast groundwater droughts.</p>

A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series

2018 ◽  
Vol 17 (6) ◽  
Author(s):  
Fang Zhang ◽  
Ang Shan ◽  
Yihui Luan
Keyword(s):  
Time Series ◽  
Microbial Community ◽  
Permutation Test ◽  
Statistical Significance ◽  
Theoretical Method ◽  
Similarity Analysis ◽  
Type I ◽  
Local Similarity ◽  
Biological Studies ◽  
Wide Range

Abstract In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim. However, all these methods require the assumption that the time series are independent and identically distributed. In this paper, we propose a new approach based on moving block bootstrap to approximate the statistical significance of local similarity scores for dependent time series. Simulations show that our method can control the type I error rate reasonably, while theoretical approximation and the permutation test perform less well. Finally, our method is applied to human and marine microbial community datasets, indicating that it can identify potential relationship among operational taxonomic units (OTUs) and significantly decrease the rate of false positives.

scholarly journals Loan asset indicators and commercial bank fragility in Kenya

2021 ◽  
Vol 29 (1) ◽  
pp. 25-36
Author(s):  
Albert Camus Onyango Bwire
Keyword(s):  
Commercial Bank ◽  
Early Warning ◽  
Statistical Significance ◽  
Early Warning Systems ◽  
Future Research ◽  
Research Approach ◽  
Exploratory Research ◽  
Warning Systems ◽  
Loan Portfolio ◽  
Lagged Dependent Variable

Purpose. To test the predictive ability of loan asset indicators on Commercial bank fragility in Kenya.  Design/Method/Research approach. The study adopted positivism research philosophy with exploratory research design. The study population was 42 Commercial banks in operation on 31st December 2015. Secondary data was collected from Central Bank of Kenya and analysed using Stata Statistics/Data analysis. Generalised Linear Model was used to establish the relationship between asset indicators and bank fragility. The concept of credit creation was explored as the genesis of bank fragility. This study is part of early warning systems in detecting bank fragility. Findings. The research found a direct relationship between a lagged dependent variable, loan portfolio growth, loan deposit ratio and bank fragility. Practical implications. Recommendations are followed on the basis of this study. At first, regulator develop a potential solution to control loan portfolio growth, cap loan deposit ratio and limit the level of non-performing loans. Banking practitioners should model monthly reporting requirements to ensure that banks are able to disclose the ratio and explain any significant changes. Secondly, since Non-performing loans can act as an incentive for bank managers to seek deposits and lend more thereby exacerbating the problem, banks with NPL to gross loans greater than an upper threshold determined by the regulator should not be allowed to attract more deposits. Thirdly, set the maximum level of loan deposit ratio to avoid expensive, sensitive and high-risk loan capital. Implementation of these recommendations will lead to secured social welfare. Originality/Value. The study examines the role of certain loan asset indicators on bank fragility and extends the discussion in the area of early warning systems and commercial bank instability in Kenya. Research limitations/Future Research. This research contributes to the discussion on bank fragility and early warning systems. The further research should review evidence from other jurisdiction with high numbers of distressed institutions to determine how many months or years before distress the three significant variables could predict fragility. Besides, there is need for research on insider loans as defined and why there was no statistical significance. Paper type. Empirical.

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