scholarly journals Spatio-temporal modeling of wireless users internet access patterns using self-organizing maps

Author(s):  
Saeed Moghaddam ◽  
Ahmed Helmy
2011 ◽  
Vol 8 (2) ◽  
pp. 3047-3083 ◽  
Author(s):  
R. Ley ◽  
M. C. Casper ◽  
H. Hellebrand ◽  
R. Merz

Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.


2011 ◽  
Vol 15 (9) ◽  
pp. 2947-2962 ◽  
Author(s):  
R. Ley ◽  
M. C. Casper ◽  
H. Hellebrand ◽  
R. Merz

Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.


Author(s):  
FARIDA ZEHRAOUI ◽  
YOUNÉS BENNANI

Spatio-temporal connectionist networks comprise an important class of neural models that can deal with patterns distributed in both time and space. In this article, we present new models of self-organizing maps for sequence clustering and classification. We have introduced the temporal dynamics in these maps and we have proposed several new models based on covariance matrices computation. In the first models, the inputs are modeled using its associated covariance matrix. These models, used in speaker recognition, do not take into account the order of the vectors in the sequence. To overcome this drawback, we have proposed new models, which introduce the temporal dynamics in the covariance matrix associated to the input sequences. In order to obtain a network that can learn new knowledge without forgetting the previous learned ones, we have introduced the plasticity and stability properties into one proposed temporal model using the adaptive resonance theory paradigm.


2018 ◽  
Vol 642 ◽  
pp. 56-62 ◽  
Author(s):  
Nenad M. Zarić ◽  
Isidora Deljanin ◽  
Konstantin Ilijević ◽  
Ljubiša Stanisavljević ◽  
Mirjana Ristić ◽  
...  

2015 ◽  
Vol 117 ◽  
pp. 180-186 ◽  
Author(s):  
Isidora Deljanin ◽  
Davor Antanasijević ◽  
Gordana Vuković ◽  
Mira Aničić Urošević ◽  
Milica Tomašević ◽  
...  

2019 ◽  
Vol 11 (3) ◽  
pp. 655-676
Author(s):  
Kiyoumars Roushangar ◽  
Farhad Alizadeh ◽  
Jan Adamowski ◽  
Seyed Mehdi Saghebian

Abstract This study utilized a spatio-temporal framework to assess the dispersion and uncertainty of precipitation in Iran. Thirty-one rain gauges with data from 1960 to 2010 were selected in order to apply the entropy concept and study spatio-temporal variability of precipitation. The variability of monthly, seasonal and annual precipitation series was studied using the marginal disorder index (MDI). To investigate the intra-annual and decadal distribution of monthly and annual precipitation values, the apportionment disorder index (ADI) and decadal ADI (DADI) were applied to the time series. The continuous wavelet transform was used to decompose the ADI time series into time-frequency domains. The decomposition of the ADI series into different zones helped to identify the dominant modes of variability and the variation of those modes over time. The results revealed the high disorderliness in the amount of precipitation for different temporal scales based on disorder indices. Based on the DI outcome for all rain gauges, a self-organizing map (SOM) was trained to find the optimum number of clusters (seven) of rain gauges. It was observed from the clustering that there was hydrologic similarity in the clusters apart from the geographic neighborhood.


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