Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART's active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Geomagnetic storms are primarily driven by the rapid transfer of energy from the solar wind to the magnetosphere. The mechanism of energy transfer involves the merging of the interplanetary magnetic field to the geomagnetic field in a process known as magnetic reconnection. This leads to an influx of energetic, charged particles into the magnetosphere so that current systems are enhanced. Specifically, an increase in the equatorial ring current leads to a decrease in the surface field. Geomagnetic storms are thus characterized by a strong decline in the horizontal components of the geomagnetic field, lasting from several hours to days. The intensity of a storm is described by the disturbed storm-time index, which is essentially a measure of the deviation from the typical quiet day variation along the equator. Severe storms can lead to the disruption of high frequency (HF) communications as a consequence of a strongly perturbed ionosphere. By the same token, the global positioning system (GPS) can become highly unreliable during magnetically disturbed conditions, yielding distance errors as large as 50 meters. The impact of geomagnetic activity and other solar-driven processes on technology systems are collectively known as space weather. Magnetic field sensing thus forms an important part of space weather forecasting and is vital to space science research as a means of improving our understanding of solar wind-magnetosphere interactions. This study examines the use of magnetometers built as SQUIDs (Superconducting Quantum Interference Devices) for monitoring the geomagnetic field for space weather forecasting purposes. A basic theory of superconductivity is presented and subsequently the key aspects governing the operation of SQUIDs are discussed. Space weather is also introduced with respect to the various processes on the sun that perturb the magnetosphere and hence the geomagnetic field. The method of analysis was basically to Fourier-transform the data using the Wiener-Khintchine theorem. A systematic approach to Fourier analysis is thus presented, demonstrating the superiority of the Wiener-Khintchine theorem in noise reduction. The suitability of SQUID magnetometers for space science research is demonstrated by a comparative study between SQUID and fluxgate datasets for magnetic storms during 2011. Strong correlation was observed between the frequency content of the SQUID and fluxgate signals. This result supports South Africa’s SQUID project, currently undertaken as a collaborative effort between SANSA Space Science and the Department of Electrical and Electronic Engineering at Stellenbosch University. This thesis thus lays a foundation for future research involving advanced magnetometry using SQUIDs. / AFRIKAANSE OPSOMMING: Geomagnetiese storms word hoofsaaklik gedryf deur die vinnige oordrag van energie van die sonwind na die magnetosfeer. Die meganisme van energie oordrag behels die samesmelting van die interplanetêre magneetveld met die geomagneetveld, in 'n proses wat bekend staan as magnetiese heraansluiting. Dit lei tot 'n instroming van energieke elektries-gelaaide deeltjies, tot in die magnetosfeer, met die gevolg dat magnetosferiese elektriese stroomstelsels versterk word. 'n Toename in die ekwatoriale ringstrome lei spesifiek tot 'n afname in die horisontale komponent van die geomagnetiese veld. Geomagnetiese storms word dus gekenmerk deur 'n sterk afname in die horisontale komponent van die geomagnetiese veld, ‘n afname wat etlike ure tot dae kan duur. Die intensiteit van 'n storm word beskryf deur die storm-tyd versteurings indeks , 'n maatstaf van die afwyking van die tipiese stil dag magnetiese variasie langs die ewenaar. Ernstige storms kan lei tot die ontwrigting van hoë frekwensie (HF) kommunikasie as 'n gevolg van 'n erg versteurde ionosfeer. Soortgelyk kan die Globale Posisionering Stelsel (GPS) hoogs onbetroubaar word tydens magneties versteurde toestande, en posisiefoute so groot as 50 meter veroorsaak. Die impak van geomagnetiese aktiwiteit en ander sonkrag gedrewe prosesse op tegnologie is gesamentlik bekend as ruimteweer. Magneetveldmetinge vorm dus 'n belangrike deel van ruimteweervoorspelling en is noodsaaklik vir ruimtewetenskaplike navorsing as 'n middel om die sonwind-magnetosfeer interaksies beter te verstaan. Hierdie studie ondersoek die gebruik van SQUID (Engels: Superconducting Quantum Interference Device) magnetometers vir die monitering van die geomagnetiese veld vir ruimteweervoorspellingsdoeleindes. ’n Basiese teorie van supergeleiding word aangebied, waarvolgens die sleutelaspekte van SQUIDs bespreek word. Ruimteweer word ook voorgestel in terme van die verskillende prosesse op die son wat die aarde se magnetosfeer en dus die geomagnetiese veld versteur. Die analisemetode wat hier gebruik word, is om die Fourier-transform van data met die Wiener-Khintchine theorema te bereken. A sistematiese metode vir Fourier-analise word aangebied, wat die superiorireit van die Wiener-Khintchine teorema vir ruisvermindering demonstreer. Die geskiktheid van SQUID magnetometers vir ruimtewetenskaplike navorsing word gedemonstreer deur ’n vergelykende studie tussen SQUID- en vloedhek-datastelle vir magnetiese storms gedurende 2011. Sterk korrelasie is waargeneem tussen die frekwensie-inhoud van die SQUID- en vloedhekseine. Hierdie resultate ondersteun Suid-Afrika se SQUID-projek, wat tans as ’n samewerkingspoging tussen SANSA Space Science en die Departement Elektriese en Elektroniese Ingenieurswese aan die Universiteit van Stellenbosch bedryf word. Hierdie tesis lê ’n fondasie vir toekomstige navorsing oor gevorderde magnetometrie met SQUIDs.