scholarly journals Dealing with non-stationarity in sub-daily stochastic rainfall models

2018 ◽  
Vol 22 (11) ◽  
pp. 5919-5933 ◽  
Author(s):  
Lionel Benoit ◽  
Mathieu Vrac ◽  
Gregoire Mariethoz
Keyword(s):  
Real Data ◽  
Unsupervised Classification ◽  
Careful Examination ◽  
Model Parameters ◽  
Critical Importance ◽  
Radar Images ◽  
Weather Generators ◽  
Stochastic Weather Generators

Abstract. Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeller. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.

scholarly journals Dealing with non-stationarity in sub-daily stochastic rainfall models

2018 ◽  
Author(s):  
Lionel Benoit ◽  
Mathieu Vrac ◽  
Gregoire Mariethoz
Keyword(s):  
Real Data ◽  
Unsupervised Classification ◽  
Careful Examination ◽  
Model Parameters ◽  
Critical Importance ◽  
Radar Images ◽  
Weather Generators ◽  
Stochastic Weather Generators

Abstract. Understanding the stationarity properties of rainfall is critical when using stochastic weather generators. Rainfall stationarity means that the statistics being accounted for remain constant over a given period, which is required for both inferring model parameters and simulating synthetic rainfall. Despite its critical importance, the stationarity of precipitation statistics is often regarded as a subjective choice whose examination is left to the judgement of the modeler. It is therefore desirable to establish quantitative and objective criteria for defining stationary rain periods. To this end, we propose a methodology that automatically identifies rain types with homogeneous statistics. It is based on an unsupervised classification of the space–time–intensity structure of weather radar images. The transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the context of sub-daily stochastic rainfall models. Results of a synthetic case study show that the proposed approach is able to reliably identify synthetically generated rain types. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm. This highlights the need for a careful examination of the temporal stationarity of precipitation statistics when modelling rainfall at high resolution.

scholarly journals Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data

2020 ◽  
Vol 12 (3) ◽  
pp. 759
Author(s):  
Jūratė Sužiedelytė Visockienė ◽  
Eglė Tumelienė ◽  
Vida Maliene
Keyword(s):  
Remote Sensing ◽  
Invasive Plant ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Economic Losses ◽  
Classification Methods ◽  
Earth Remote Sensing ◽  
Heracleum Sosnowskyi

H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper.

scholarly journals Parameter Estimation of a Reliability Model of Demand-Caused and Standby-Related Failures of Safety Components Exposed to Degradation by Demand Stress and Ageing That Undergo Imperfect Maintenance

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
S. Martorell ◽  
P. Martorell ◽  
A. I. Sánchez ◽  
R. Mullor ◽  
I. Martón
Keyword(s):  
Parameter Estimation ◽  
Nuclear Power ◽  
Likelihood Estimation ◽  
Real Data ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Reliability Model ◽  
Imperfect Maintenance ◽  
Maintenance Policies

One can find many reliability, availability, and maintainability (RAM) models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD) of safety equipment: (1) by demand-caused and (2) standby-related failures. It proposes a maximum likelihood estimation (MLE) approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.

scholarly journals DETECTION OF LANDSLIDES WITH SAR POLARYMETRY: CASE STUDY OF SOUTH-EASTERN COLOMBIA

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 177-184
Author(s):  
N. A. Correa-Muñoz ◽  
C. A. Murillo-Feo
Keyword(s):  
Principal Component ◽  
Unsupervised Classification ◽  
Bare Soil ◽  
Surface Scattering ◽  
Total Power ◽  
Detection Model ◽  
Radar Images ◽  
Dispersion Mechanism ◽  
Forested Areas

<p><strong>Abstract.</strong> SAR polarimetry (PolSAR) is a method that can be used to investigate landslides. Polarimetric scattering power decomposition allows to separate the total power received by the SAR antenna, which is divided in surface scattering power, double bounce scattering and volume scattering power. Polarimetric indices are expected to serve for landslide recognition, because landslides’ scattering properties are different from those of the surrounding forested areas. The surface scattering mechanism is mainly caused by rough surfaces like bare soil and agricultural fields, so we hope that this will be the predominant dispersion mechanism in landslides. In a study area located in south-western Colombia, we used dual-Pol provided by ESA’s Sentinel-1 satellites and quad-pol from NASA’s UAVSAR aerial platform. Using C-band and L-band radar images, we analysed the interaction between radar signals and landslides. First, with dual-pol we found backscatter calibrate coefficients over four GRD radar images acquired between 2015 and 2017. The analysis gave an average backscatter value of &amp;minus;14.47&amp;thinsp;dB for VH polarisation and &amp;minus;8.40&amp;thinsp;dB for VV polarisation. Then, using H-a decomposition for quad-pol data, we validated the high relationship between entropy and alpha parameter, which has the highest contribution to the first axis in a principal component analysis. These results were used to obtain an unsupervised classification of landslides, that separated the Colombian Geological Service landslide inventory in three classes characterized by the mechanism of dispersion. These results will be combined with InSAR parameters, morphometric parameters and optical spectral indexes to obtain a local detection model of landslides.</p>

Unsupervised classification of radar images based on hidden Markov models and generalized mixture estimation

2000 ◽  
Author(s):  
Roger Fjortoft ◽  
Jean-Marc Boucher ◽  
Yves Delignon ◽  
Rene Garello ◽  
Jean-Marc Le Caillec ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Unsupervised Classification ◽  
Radar Images

scholarly journals Detection and Two-Step Classification of Erosional Damages with Help of Soil Sample Analysis and Satellite Images

Geografie ◽  
1997 ◽  
Vol 102 (1) ◽  
pp. 17-30
Author(s):  
Jaromír Kolejka ◽  
Jásim K. Shallal
Keyword(s):  
Cluster Analysis ◽  
Soil Sample ◽  
Satellite Images ◽  
Surface Soil ◽  
Satellite Image ◽  
Unsupervised Classification ◽  
Sample Analysis ◽  
Free Fields

Surface soil data have been processed using the unsupervised classification (cluster analysis). Three soil categories with different erosional characteristics have been detected: heavily, moderately and slightly/no damaged soils. The supervised satellite image classification (MLC) was based on the data taken from case study areas in the proximity of classified soil sample sites on the vegetation free-fields.

scholarly journals Electrical transient modeling for appliance characterization

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Mohamed Nait-Meziane ◽  
Philippe Ravier ◽  
Karim Abed-Meraim ◽  
Guy Lamarque ◽  
Jean-Charles Le Bunetel ◽  
...  
Keyword(s):  
Steady State ◽  
Classification Accuracy ◽  
Sudden Change ◽  
Real Data ◽  
Model Parameters ◽  
Suitable Model ◽  
Turn On ◽  
Short Period ◽  
The One

AbstractTransient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.

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