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SPATIAL DATA MINING TOOLBOX FOR MAPPING SUITABILITY OF LANDFILL SITES USING NEURAL NETWORKS
Spatial data mining GIS neural networks ArcGIS toolbox landfill suitability analysis
2016/10/14
Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suita...
Long-term trends in f0 F2 over Grahamstown using Neural Networks
Solar activity magnetic activity
2015/9/25
Many authors have claimed to have found long-term trends in f0 F2 , or the lack thereof, for different stations. Such investigations usually involve gross assumptions about the variation of f0 F2 with...
Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity
Spatial Panel Models Networks
2015/9/24
This paper considers a class of GMM estimators for general dynamic panel models,
allowing for cross sectional dependence due to spatial lags and due to unspecified common shocks. We signiᤙ...
Data analysis of Permanent GPS networks in Italy and surrounding region:application of a distributed processing approach
Crustal deformations Satellite geodesy Data processing Plate boundaries, motion, and tectonics
2015/9/8
We describe the procedures used to combine into a uniform velocity solution the observations of more than 80 continuous GPS stations operating in the central Mediterranean in the 1998-2004 time interv...
An improved seismicity picture of the Southern Tyrrhenian area by the use of OBS and land-based networks: the TYDE experiment
Ocean bottom seismograph integrated seismic networks southern Tyrrhenian Sea
2015/9/7
The problem of large location uncertainties for seismicity occurring in the Southern Tyrrhenian
Sea have been partially exceeded during the implementation of the long-term scientific mission of
the ...
From bottom landers to observatory networks
landers multidisciplinary long term observatories global change
2015/9/7
For a long time, deep-sea investigation relied on autonomous bottom landers. Landers can vary in size from 200 kg
weight to more than 2 t for the heaviest scientific landers and are used during explo...
The coseismic ground deformations of the 1997 Umbria-Marche earthquakes:a lesson for the development of new GPS networks
GPS IGM95 Umbria-Marche earthquakes
2015/9/1
After the occurrence of the two main shocks Mw=5.7 (00.33 GMT) and Mw=6.0 (09:40 GMT) on September 26, 1997, which caused severe damages and ground cracks in a wide area of the Umbria Marche region, t...
Using neural networks to study the geomagnetic field evolution
Geomagnetic Field Geomagnetic Observatory Neural Networks (NN) time series time prediction
2015/9/1
study their time evolution in years. In order to find the best NN for the time predictions, we tested many different
kinds of NN and different ways of their training, when the inputs and targets are ...
Rapid response seismic networks in Europe:lessons learnt from the L'Aquila earthquake emergency
Rapid response seismic networks open data archives seismology
2015/8/27
The largest dataset ever recorded during a normal fault seismic sequence was acquired during the 2009 seismic emergency triggered by the damaging earthquake in L'Aquila (Italy). This was possible thro...
Focal mechanisms in the southern Aegean from temporary seismic networks – implications for the regional stress field and ongoing deformation processes
Focal mechanisms the southern Aegean temporary seismic networks the regional stress field ongoing deformation processes
2015/1/6
The lateral variation of the stress field in the southern Aegean plate and the subducting Hellenic slab is determined from recordings of seismicity obtained with the CYCNET and EGELADOS networks in th...
Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models
Time-dependent prediction degredation assessment neural-networks-based TEC forecasting models
2009/11/12
An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation...
Modelling lava flows by Cellular Nonlinear Networks (CNN):preliminary results
lava flows Cellular Nonlinear Networks (CNN) preliminary results
2009/11/9
The forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real top...
We invoke a metric to quantify the correlation between any two earthquakes. This provides a simple and straightforward alternative to using space-time windows to detect aftershock sequences and obviat...
Multiscaling of porous soils as heterogeneous complex networks
Multiscaling porous soils heterogeneous complex networks
2009/11/2
In this paper we present a complex network model based on a heterogeneous preferential attachment scheme to quantify the structure of porous soils. Under this perspective pores are represented by node...