Development of a neural network based model for predicting the occurrence of spread F within the Brazilian sector
- Authors: Paradza, Masimba Wellington
- Date: 2009
- Subjects: Neural networks (Computer science) , Ionosphere , F region
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5460 , http://hdl.handle.net/10962/d1005245 , Neural networks (Computer science) , Ionosphere , F region
- Description: Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position (latitude, magnetic declination and inclination). Twelve years of spread F data measured during 1978 to 1989 inclusively at the equatorial site Fortaleza and low latitude site Cachoeira Paulista are used in the development of an input space and NN architecture for the NN models. Spread F data that is believed to be related to plasma bubble developments (range spread F) were used in the development of the models while those associated with narrow spectrum irregularities that occur near the F layer (frequency spread F) were excluded. The results of the models show the dependency of the probability of spread F as a function of local time, season and latitude. The models also illustrate some characteristics of spread F such as the onset and peak occurrence of spread F as a function of distance from the equator. Results from these models are presented in this thesis and compared to measured data and to modelled data obtained with an empirical model developed for the same purpose.
- Full Text:
- Date Issued: 2009
- Authors: Paradza, Masimba Wellington
- Date: 2009
- Subjects: Neural networks (Computer science) , Ionosphere , F region
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5460 , http://hdl.handle.net/10962/d1005245 , Neural networks (Computer science) , Ionosphere , F region
- Description: Spread F is a phenomenon of the ionosphere in which the pulses returned from the ionosphere are of a much greater duration than the transmitted ones. The occurrence of spread F can be predicted using the technique of Neural Networks (NNs). This thesis presents the development and evaluation of NN based models (two single station models and a regional model) for predicting the occurrence of spread F over selected stations within the Brazilian sector. The input space for the NNs included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position (latitude, magnetic declination and inclination). Twelve years of spread F data measured during 1978 to 1989 inclusively at the equatorial site Fortaleza and low latitude site Cachoeira Paulista are used in the development of an input space and NN architecture for the NN models. Spread F data that is believed to be related to plasma bubble developments (range spread F) were used in the development of the models while those associated with narrow spectrum irregularities that occur near the F layer (frequency spread F) were excluded. The results of the models show the dependency of the probability of spread F as a function of local time, season and latitude. The models also illustrate some characteristics of spread F such as the onset and peak occurrence of spread F as a function of distance from the equator. Results from these models are presented in this thesis and compared to measured data and to modelled data obtained with an empirical model developed for the same purpose.
- Full Text:
- Date Issued: 2009
Forecasting solar cycle 24 using neural networks
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Date Issued: 2009
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Date Issued: 2009
Geomagnetically induced current characteristics in southern Africa
- Authors: Ngwira, Chigomezyo Mudala
- Date: 2009
- Subjects: Magnetic Observatory (South African Council for Scientific and Industrial Research) Geomagnetism -- Africa,Southern Computer networks -- Africa, Southern Magnetospheric currents
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5469 , http://hdl.handle.net/10962/d1005254
- Description: Geomagnetically induced currents (GICs), resulting from adverse space weather, have been demonstrated to cause damage to power transformers at mid-latitudes. There is growing concern over possible GIC effects in the Southern African network due to its long power lines. Previous efforts to model the electric field associated with GICs in the Southern Africa region used a uniform ground conductivity model. In an effort to improve the modelling of GICs, GIC data together with Hermanus Magnetic Observatory geomagnetic field data were used to obtain a multilayered ground conductivity model. This process requires a definition of the network coefficients, which are then used in subsequent calculations. This study shows that GIC computed with the new network coefficients and the multilayered ground conductivity model improves the accuracy of GIC modelling. Then GIC statistics are derived based on the recordings of the geomagnetic field at Hermanus, the new network coefficients and ground conductivity model. The geoelectric field is modelled using the plane wave method. The properties of the geomagnetic field, their time derivatives and local geomagnetic indices were investigated to determine their characteristics in relation to the GIC. The pattern of the time derivatives of the horizontal geomagnetic field closely follow the rate of change of the north-south geomagnetic component rather than the east-west component. The correlation between the GIC and the local geomagnetic field indices was also investigated. The results show that there is a higher correlation between the GIC and the east-west components of the geomagnetic local indices than between the GIC and the north-south components. This corresponds very well with the orientation of the power lines feeding the power transformers at the South African Grassridge electrical substation GIC site. Thus, the geoelectric field driving the GIC at Grassridge is north-south oriented. Further, it is shown that the geomagnetic observation sites have a strong directional preference with respect to the Grassridge GIC site.
- Full Text:
- Date Issued: 2009
- Authors: Ngwira, Chigomezyo Mudala
- Date: 2009
- Subjects: Magnetic Observatory (South African Council for Scientific and Industrial Research) Geomagnetism -- Africa,Southern Computer networks -- Africa, Southern Magnetospheric currents
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5469 , http://hdl.handle.net/10962/d1005254
- Description: Geomagnetically induced currents (GICs), resulting from adverse space weather, have been demonstrated to cause damage to power transformers at mid-latitudes. There is growing concern over possible GIC effects in the Southern African network due to its long power lines. Previous efforts to model the electric field associated with GICs in the Southern Africa region used a uniform ground conductivity model. In an effort to improve the modelling of GICs, GIC data together with Hermanus Magnetic Observatory geomagnetic field data were used to obtain a multilayered ground conductivity model. This process requires a definition of the network coefficients, which are then used in subsequent calculations. This study shows that GIC computed with the new network coefficients and the multilayered ground conductivity model improves the accuracy of GIC modelling. Then GIC statistics are derived based on the recordings of the geomagnetic field at Hermanus, the new network coefficients and ground conductivity model. The geoelectric field is modelled using the plane wave method. The properties of the geomagnetic field, their time derivatives and local geomagnetic indices were investigated to determine their characteristics in relation to the GIC. The pattern of the time derivatives of the horizontal geomagnetic field closely follow the rate of change of the north-south geomagnetic component rather than the east-west component. The correlation between the GIC and the local geomagnetic field indices was also investigated. The results show that there is a higher correlation between the GIC and the east-west components of the geomagnetic local indices than between the GIC and the north-south components. This corresponds very well with the orientation of the power lines feeding the power transformers at the South African Grassridge electrical substation GIC site. Thus, the geoelectric field driving the GIC at Grassridge is north-south oriented. Further, it is shown that the geomagnetic observation sites have a strong directional preference with respect to the Grassridge GIC site.
- Full Text:
- Date Issued: 2009
Predictability of Geomagnetically Induced Currents using neural networks
- Authors: Lotz, Stefanus Ignatius
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
- Full Text:
- Date Issued: 2009
- Authors: Lotz, Stefanus Ignatius
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
- Full Text:
- Date Issued: 2009
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