Space Weather Prediction using Soft Computing Techniques

Transcription

1 Space Weather Prediction using Soft Computing Techniques José Manuel Costa Casas Fernandes Instituto Superior Técnico, Lisboa, Portugal June 2015 Abstract Nowadays, with the accessibility to satellites data, is possible to predict solar energetic particles (SEP) events with intensities harmful to our society. This dissertation was done in collaboration with the Belgian Institute for Space Aeronomy. Its aim was to evaluate the capability of soft computing methods to classify SEP events, predict the intensity of SEP events and compare the performances of the models with the SEP forecasting tool developed in the scope of the EU FP7 COMESEP project. The soft computing methods used were classification trees, regression trees and fitting, pattern and cascade forward neural networks. The data were obtained from solar cycles 23 and 24. The variables considered were solar flares intensities, heliographic longitudes, coronal mass injections velocities and widths. The performance of the SEP events classification models derived surpassed the ones of the COMESEP forecasting tool. The obtained results using neural networks were, 74, 29% accuracy, 88, 24% sensitivity and 31, 82% false alarm rate. Regarding the intensity peak flux prediction models, they performed equivalently to the COMESEP forecasting tool, but presented more variability. This can be due to the low amount of available data. It was concluded that the use of soft computing methods could enhance the performance of the present COMESEP forecasting tool. Keywords: SEP events, Soft computing, COMESEP, Neural networks, Decision trees 1. Introduction Until now, Earth is the only known planet where life as we know it manages to exist. This is due to an improbable confluence of factors, which have to do with the distance to the Sun and the amount of energy that we receive from it. Space weather can be defined as, the consequences of solar activity that are felt on near Earth space or on ground level with a negative impact on technological systems, human activity and health. The effects of solar activity on Earth depend of the distance to the Sun, Earth magnetic field and atmosphere [1]. Solar activity shows peaks approximately every 11 years. During the peak period, the risks to our increasingly vulnerable technology dependent societies are extremely concerning, due to the possibility of a solar strike that may cause a power and communications blackout for an undefined period of time. Besides this, currently there is no proper line of action defined or capacity to shield the planet against a large solar strike with potential disastrous consequences [12]. Solar activity designates all the phenomena that occur in the Sun or on its surface. During the history it was observed that there is a pattern in which the Sun s activity reaches a maximum before decreasing again. This pattern began to be observed around It is referred to as a solar cycle its duration is approximately 11 years. Currently the Sun is on the 24 th solar cycle, counting from the first observed cycle around This cycle began in 2008 and should end around 2019 with an activity peak somewhere in the middle. Among the solar activity some of the relevant phenomena that need to be understood are solar flares (SFs) and coronal mass ejections (CMEs). They are the main reason why solar energetic particle events, geomagnetic storms and geomagnetic induced currents occur [1]. Solar Flare (SF) - Release of large amounts of magnetic energy in the Sun. They can be described as a sudden brightening observed over the Sun s surface. The wave length of the emitted radiation may go from radio waves until gamma-rays. They are often, but not always, followed by coronal mass ejection. So- 1

2 lar flares are extremely hard to predict due to their dependency on the solar magnetic field [1, 7]. Coronal Mass Ejection (CME) - Ejections of mass and electromagnetic radiation from the solar corona. They are seen as bright shapes moving from the Sun s corona at speeds that in some special cases may reach 2000 Km/s. When these ejections reach the interplanetary medium they are called interplanetary coronal mass ejection (ICME). The ejected material is plasma, composed primarily of electrons and protons, but may also contain small quantities of heavier elements [1, 2]. The aim of this work is the classification and the prediction of space weather events. It was proposed by the team of EU FP7 COMESEP (Coronal Mass Ejections and Solar Energetic Particles: forecasting the space weather impact) project and more specifically by the Belgian Institute for Space Aeronomy (BIRA). The COMESEP project objective was to develop an European space weather alert system that utilizes data from x-ray solar flares and coronal mass ejections to forecast the risk of solar energetic particles radiation storms and magnetic storms [1]. The COMESEP project group, BIRA, has cooperated in the problem understanding and has provided the necessary data for all the analysis done in this work. COMESEP s alert system forecasts solar energetic particle storms based on a risk analysis. This analyses are done using statistic models that obtain the probability of correct classification and prediction of solar energetic particle events (SEP). To enhance the results, in this work soft computing methods are used to derive the models. The COMESEP SEP forecast tool utilizes data from SFs and CMEs to predict the probability of occurrence and the intensity peak flux of SEP events for proton energies > 10 MeV and > 60 MeV, resultant from SFs of class M or larger. The objective of our work is to developing soft computing models with better performance than the COMESEP SEP forecasting tool. These models should also use SF and CME inputs and predict the occurrence and intensity of SEP events. Nowadays, there is a wide research in the field of space weather and some of these works already achieved good results in the prediction of events in this field of study. Most of the research done in this field of study began in the second half of the 80 s and is still being perfected today. The research performed extends across 11 diverse areas of science and topics of study. Regarding the utilization of soft computing techniques Gabriel and Patrick [5] utilized x-ray flare data from solar cycles 20, 21, 22 and 23 to develop binary classification neural networks models using different types of networks. The objective of these neural networks models was the forecast of a SEP event 48 hours in advance to the time it would have started. For this they derived models that used as inputs 3 hours averages of x-ray flare data over a 120 hours period. Valach, et al [13] described some analyses done with neural networks for the forecast of SEP events. They compared the performances of several neural networks using different combination of inputs to classify if a SEP event was severe, medium, weak or no event at all. The inputs utilized were different combinations of the following data: heliographic coordinates of the location on the solar disk where the X-ray flare occurred, flare class, the increase of high-energy proton flux to energy > 10 MeV and information on whether the flare was accompanied by a solar radio burst of type II and/or IV. All the data utilized were collected during solar cycle 23. One of the most promising works in the field of SEP events forecast systems was published in 2011 by Núñez [10]. The system described in his work is based on a dual-model approach for predicting well and poorly connected SEP events (E > 10 MeV) and their log 10 peak intensity. These forecasts are based on data from soft X-ray fluxes, differential proton fluxes (E = MeV) and integral proton fluxes (E > 10 MeV). After the data become available, the system starts by verifying the connectivity of the data to select which of the models will be used. The well-connected model utilizes an ad hoc formula to calculate a fluctuation similarity. This value is then compared with a predefined threshold and if the value is bigger an alert is made. The poorlyconnected model is an ensemble of 24 regression trees, where each one predicts n (n = 1,..., 24) hours ahead the intensity of poorly-connected SEP events. If any of the 24 predicted intensities surpasses the Space Weather Prediction Centre threshold an alert is made. In addition to these models there is a high-level module for analysis and inference of the alerts made by the two previous models. This module works like a filter that uses some empiric rules to test if the predictions are correct. The final forecast of the system results from this module accepts or not the alerts of the previous models. The results of this system are the best known for this kind of forecasting systems. It has an overall sensitivity for the forecast of well and poorly connected events of 80, 72%, a false alarm rate of 33, 99% and a RMS error for the log 10 peak intensity of 0,

3 2. Background The ideology behind the modelling of a system or process consists in the utilization of a group of mathematical equations and/or an empiric formulation that reproduces that system or process behaviour. According to this formulation, a model that is submitted to the same initial conditions of the system or process that is being modelled should react in a similar way to that system or process, resulting in a similar output. Soft computing is utilized in the developing of computational smart systems. It emerged from the need that intelligent systems had of combine knowledge, techniques and methodologies from different sources to solve complex problems. These systems try model some human capacities like expertise knowledge within a restrict domain, self-adaptation and learn in a changing environment. They contrast with hard computing by accepting some degree of imprecision and uncertainty [6] Data Partition One of the primary methodologies utilized in soft computing is the division of the initial data set into three distinct sets called training, test and validation data sets. Each of these sets has its importance for the correct derivation of soft computing models. There are varies ways of proceed to the division of the initial data set into these three categories, like the holdout and the k-folds methods among other. The k-fold cross-validation is a technique that evaluates the model capacity to generalize from an independent data set. In this technique the original data are randomly partitioned into k equal size sub samples. One of the sub samples is retained for testing the model and the remaining (k 1) are used for training. This process is repeated k times (k folds), resulting that each of the k sub samples was used exactly once for test. The k results from the folds can then be averaged to produce a single performance estimation Decision trees Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. A decision tree can be used to model sequential decision problems in a uncertainty environment. A decision tree describes graphically the decisions to be made, the events that may occur, and the outcomes associated with combinations of decisions and events. Probabilities are assigned to the events, and values are determined for each outcome. A major goal of using decision trees is to determine the best decisions. A decision tree model divides the input space of a data set into mutually exclusive regions and assigns to each region a label, a value or an action that characterize its data points. A decision tree is a tree structure consisting of root, internal and external nodes connected by branches. The root (starting) node is the node which receives the inputs to be analysed. An internal node is a binary decision-making unit that evaluates a decision function to determine which node to visit next. An external node, also known as a terminal node, has no consecutive nodes and is associated with a label or value that characterizes the given input data. In general, a decision tree is employed as follows. First the input data are presented to the starting node of the decision tree. Depending on the result of a decision function used by an internal node, the tree will branch to one of its next nodes. This is repeated until a terminal node is reach and a label or value is assigned to the given input data [6]. A tree structure may be applicable to any number of variables [8]. Decision trees used for classification problems are often called classification trees, each terminal node of these trees contains a label that indicates the predicted class of a given input vector. In the same way, decision trees used for regression problems are often called regression trees, in this case the terminal node may be an equations that specify the predicted output value of a given input vector. There are different algorithms for divided the inputs space and consequently the nodes functions [6] Neural Networks A neural network is a structure that tries to mimic the neural connections of the human brain using a certain number of nodes connected through links. Neural networks can be utilized for system modelling and identification. This is done by finding an appropriate network architecture (number of nodes, nodes functions parameters and number of layers) utilizing a set of input/output data tuples. A neural network is composed by a set of nodes connected by directed links. Each of the nodes performs a static node function on its incoming signals and generates a single node output. The nodes are grouped into layers, a layer is a set of nodes that receive the same signals and usually are not connected to each other. The links specify the direction of the signals flow from one node to another. The overall input/output behaviour of a network is determined by a collection of nodes functions. A node s output depends of its inputs only, there are no dynamics or internal states associated with the nodes. The parameters of a network are distributed into the nodes parameters set. Usually a node s function is a parametric function with modifiable parameters, that is differentiable 3

4 except at a finite number of points. A parametric function output depends of the inputs of its inputs and a set of fix predefined parameters. To obtain the best performance from a neural network the set of parameters of the nodes functions needs to be optimized. A learning rule specifies how these parameters should be updated to minimize a prescribed error measure, the most usual one is the mean square error between the network s output and a desired output. The basic learning rule for neural networks is the steepest descent method [4] in which the gradient vector is derived by successive invocations of the chain rule. In 1986 Rumelhart, et al [11] used this procedure to find the gradient in a multi-layer neural network, their procedure was called the back-propagation learning rule [6]. The back-propagation learning rule consists on using the output error to recalculated all the parameters of the nodes functions. Neural networks are usually classified into two categories, feedforward and recurrent and may be used to solve both classification and regression problems. 3. Implementation This work was done in collaboration with BIRA. The main objective is to study the relevance of the utilization of soft computing techniques for the evaluation of SEP events. This evaluation has two stages: the first is the binary classification of the occurrence of SEP events; the second is obtaining regression models that allow the prediction of the intensity peak flux of SEP events. The techniques considered were classification trees, regression trees and neural networks. For each of the previously referred techniques various models with different parameters were derived, to test which would be the best combination of parameters to achieve the desire results. For model validation k-fold cross-validation was utilized SEP events related data The same data that BIRA used were also utilized for derive and validate the models. The input data consists of the intensity and heliographic longitude of solar flares and the velocity and width of coronal mass ejections. Input variables Solar Flare Intensity - It is also known as the solar flare peak flux in watts per square meters obtained by the GOES satellite [3]. This variable becomes available since the occurrence of a solar flare. The model should react only to M or bigger class solar flares. Therefore, only information related to these solar flares was extracted from the flares list shared by COME- SEP project group. The solar flares list provides the intensity of the solar flare in the normal classification format. Before these values could be utilized it was necessary to convert them to W.m 2 using the conversion shown in Table 1. This was done using the following procedure: if in the solar flares list file the flare was classified as being of class M2, its peak flux should be calculated as Classification Peak Flux (W.m 2 ) M X Table 1: Starting peak flux values for M and X class solar flares. Solar Flare Heliographic Longitude - It is represented by the letter E (East) or W (West) followed by the angle between the plane of the meridian of a given point in the Sun surface and the plane of the zero meridian. Like the solar flare intensity, this variable is also available since the occurrence of a solar flare. It was also extracted from the solar flares list, although the letters E and W were replaced, respectively, by a negative sign (-) and a positive sign (+). This was done so that the values could be used as input variables. CME Velocity - This is the velocity of the CME in kilometres per hour measured at the L1 libration point. Depending on the velocity of the CME, this information may only be available some minutes before the SEPs impacts the Earth. The values of this variable were directly extracted from the CMEs list file. CME Width - It is measured as the position angle extent in the sky plane [9]. This variable is also measured at L1 libration point. Like the velocity, it was directly extracted from the CMEs list SEP Events Classification The pre-processing of the data from the files supplied by BIRA, resulted in a data set with 467 tuples. This set was divided in two sets, the first set was composed by data from solar cycle 23 to be used for training and test of the models and the second set corresponded to data from solar cycle 24 utilized for validation of the models. The data from solar cycle 23 was partitioned utilizing the k-folds method. The binary classification analysis was done considering k = 10, resulting in 10 training sets with a mean size of 389 tuples 4

5 where 106 (27%) tuples corresponded to the occurrence of a SEP event. The test set mean size was of 43 tuples where 12 (28%) tuples represented observed SEP events. Using the 10 training and test sets several classification trees, regression trees, one hidden layer and two hidden layers neural networks were derived and their test results compared with each other Classification trees The derivation of classification tree models used the Gini s diversity index as optimization criterion. In regard to the stopping rules, it was used the node purity and the minimum number of branch node observations which was set to 10 observations. The minimum number of leaf node observations was not fixed (changing from 1 to 15), this way 15 classification trees were derived for each of the 10 training sets Regression trees The process utilized for deriving regression tree models was similar, but instead of the Gini s diversity index it was used the mean-squared error. These trees contrary to the classification trees did not provided a direct binary classification as output, because of this a threshold needed to be applied to the output. Having in mind the desired binary classification it seems logical to start by defining the threshold value for 0, 5. This way if the regression tree output was bigger than 0, 5 the classification was considered 1 (occurrence) otherwise it was 0 (non-occurrence). Other thresholds were also analysed Neural networks Both one and two hidden neural networks were analysed. Using pattern recognition for one hidden layer networks and both pattern recognition and cascade forward for two hidden layers networks. The optimization techniques utilized for update the networks weights were, the Levenberg-Marquardt back-propagation optimization algorithm together with the mean-squared error, for evaluate the networks performance during the training process. Here similarly to what was done in the decision trees, just one of the parameter was changed. In this case instead of the minimum number of leaf node observations, the number of nodes in each hidden layer was made variable. For neural networks with one hidden layer, the number of hidden layer nodes was changed from 1 to 10 for each training set. Regarding neural networks with 2 hidden layers, the number of nodes was not a single value, but, a combination of two values, the number of nodes in each of the two layers. This way, every combination of nodes in each layer was tested with number of nodes varying from 1 to 10. As for regression trees, the neural networks also did not provided a direct binary classification, for this reason a threshold was also applied to the outputs. For all the methodologies described were obtained models considering two different input combinations, the first utilized only the flares intensity and flares longitude, the second utilized all four input variables referred previously Intensity Peak Flux Prediction It is possible to obtain the intensity peak flux for different values of energy although the energy level (E) of 10 MeV was the only level considered because of its relevance for human health. Instead of the real value of the intensity peak flux, the log 10 of the intensity was utilized, because it achieved better prediction results. From the data files was possible to calculate the log 10 intensity peak flux E 10 MeV of 94 events. This is considered a relatively short data set for apply soft computing techniques, which may influence the results. The data were divided in two sets, one with 80 tuples related to solar cycle 23 that was utilized the training and test processes and other set with 14 tuples associated with solar cycle 24 used for the validation process. The solar cycle 23 data set was partitioned in training sets and a test sets. This was done utilizing the k-folds considerations for k = 10. The utilization of k = 10 allowed training sets with 72 tuples associated with each set and test sets with 8 tuples. Next will be described the processes and methods utilized to derived the prediction models. One of these analyses required the division of the output (log 10 intensity peak flux) domain in four classes. Two different divisions were considered in that case. The first divided the domain in four intervals, restringing the number of tuples in each interval to be equal (uniform distribution). The second also divided the domain in four intervals, but the values considered for the intervals were some values of the log 10 intensity peak flux proposed by BIRA (COMESEP distribution). The histograms of these divisions can be examine in figure 1 The derivation of the prediction models was done considering two distinct analysis. One which used the inputs previously described (first analysis) and another which utilized the outcome of a classification model as input in addition to the inputs previously refereed (second analysis). The output of both these analysis was the value of the logarithmic intensity peak flux. The modelling methods considered in both analysis were regression trees, fitting neural networks and cascade neural networks. 5

6 Figure 2: Prediction model with an extra input from a classification model and binary classification. Figure 1: Histograms of uniform (on top) and COMESEP (on bottom) classes distributions. The regression tree models were obtained using the mean-square error as optimization criterion. The stopping rules considered were the node purity, the minimum number of branch node observations and the minimum number of leaf node observations. The minimum number of branch node observations was fix to 10, the minimum number of leaf node observations was changed from 1 to 15 resulting in 15 different models for each of the 10 training sets. Three types of neural networks were analysed, fitting networks with 1 and 2 hidden layer and cascade networks with 2 hidden layers. The best performances were obtained using the Levenberg- Marquardt back propagation optimization algorithm for updating the network weights and the meansquared error for evaluation of the networks performance during the training process. To evaluate which would be the best network configuration, the number of nodes of the hidden layers was changed from 1 to 10. For the second analysis it was considered an extra input in addition to the inputs previously described utilized in the first analysis. This extra input corresponded to the outcome of a classification model that beforehand classified the inputs in one of the four classes of the uniform distribution or the COMESEP distribution. To try improve the performances it were also added 4 binary classification models (one for each class) before the classification model, as represent by figure 2. These models received the inputs and performed a binary classification, each of the outputs of these models was them considered as an input variable of the classification model. To study the influence of the inputs there were carried out three analyses with different input variables. One that used only the flare intensity, other which used the flare intensity together with the CME velocity and other which used all four input variables. 4. Results After the training processes described previously the models obtained were tested, so that the model with the best performance would be identified SEP events classification results Due to the way that the initial data was partitioned the 10 training and test sets where independent from each other, this allowed the utilization of the cross-validation method. The mean results and standard deviations of the soft computing techniques with best performances with flare intensity and longitude as inputs variables are display in table 2. After identifying the method that obtained the best averaged test performance, the model with best performance from the 10 sets was utilized for validation of the mean results. This was done using the input data of the validation set and evaluating the model performance compared to the observed data values for this set. The performance results of the validation process are also shown in table 2. These results presented a lower Accuracy, HSS, TSS and specificity, but a better sensitivity while preserving the FAR. The decrease in these values was due to the solar cycle 23 data present a higher non-occurrence of SEP events observations, which lead the model to be better classifying the non-occurrence, while the validation set was evenly distributed between occurrences and nonoccurrences of SEP events. Overall the results were similar to the ones obtained during the test and the model was considered valid. The same test process was repeated for models that utilized all the four inputs previously described. The mean results and standard deviations of the 6

7 1 hidden layer NN with 7 hidden nodes T hreshold = 0, 39 Flare intensity and longitude ACC 78,48% 68,57% SD 5,85% - HSS 45,21% 36,99% SD 12,82% - TSS 44,02% 36,93% SD 11,85% - TPR 57,73% 64,71% SD 13,63% - SPC 86,29% 72,22% SD 9,33% - FAR 34,39% 31,25% SD 17,38% - Table 2: Best classification model accuracy, heidke skill score, true skill score, true positive rate (sensitivity), specificity, false alarm rate and standard deviation results for flare inputs. soft computing techniques with best performances in this case are display in table 3. 2 hidden layers NN with 4 hidden nodes in the first layer and 8 in the second T hreshold = 0, 34 All inputs ACC 83,80% 74,29% SD 4,10% - HSS 62,39% 48,95% SD 8,58% - TSS 67,27% 49,35% SD 9,01% - TPR 83,18% 88,24% SD 8,35% - SPC 84,09% 61,11% SD 5,41% - FAR 33,03% 31,82% SD 7,11% - Table 3: Best classification model accuracy, heidke skill score, true skill score, true positive rate (sensitivity), specificity, false alarm rate and standard deviation results for all inputs. From the 10 models tested the neural network model that presented the best averaged results was identified and validated utilizing data from solar cycle 24 for the threshold 0, 34. The results of the validation process are also presented in table 3. Once more the discrepancy between the solar cycle 23 and 24 data was noticed, as can be seem, for example, in the lower specificity value. Despite this, the sensitivity and FAR, which according to COMESEP project group are the most important measures of performance in this type of analysis, were very similar to the values obtained during the test and ensure the validity of the model. The results presented show that the utilization of the CME variables enhances the models performance. In other hand this variables are only measure near the Earth, which results in a shorter response time. Threshold 0, 3 Threshold 0, 5 Flare Inputs All Inputs Flare Inputs All Inputs ACC 91% 86% 92% 90% HSS 35% 30% 28% 38% TSS 30% 36% 19% 36% TPR 33% 47% 20% 40% FAR 50% 68% 33% 52% Table 4: Performance measures of the COMESEP SEPForecast tool for solar cycle 24 for two values of threshold. By comparing the validation results (solar cycle 24 data) with the ones from table 4, it can be seem that our soft computing models in general present better results regarding the classification of SEP events. This is concluded by the higher sensitivities and lower false alarm rates than the current COMESEP SEPForecast tool. Regarding other work in this field, the work published in 2011 by Núñez [10] is a big reference in SEP events forecast. Our test results for solar cycle 23 data using all variables are slightly better than the ones obtained by Núñez using solar cycle 23 data (80, 72% sensitivity and 33, 99% false alarm rate). The main differences between Núñez model and ours is that his model utilizes other input variables and also classifies the occurrence of SEP events in poor or well-connected events, while our model simply classifies the occurrence or non-occurrence of the events Intensity peak flux prediction results In this analysis it was concluded that the introduction of the binary and classification models previously to the prediction models always improved the results. It was also concluded that in general the COMESEP distribution achieved better results than the uniform distribution, except for the models with only flare intensity and CME velocity as inputs where the uniform distribution results were slightly better. Table 5 shows the test performance results for the best prediction models with a extra 7

8 input from the classification model and binary models. Flare intensity Cascade NN with 2 hidden layers POR 1,15 1,01 SD 0,68 0,63 MSE 0,59 1,47 Intensity and velocity NN with 2 hidden layers POR 1,01 0,99 SD 0,41 0,58 MSE 0,36 1,19 All inputs Cascade NN with 2 hidden layers POR 1,03 1,04 SD 0,38 0,50 MSE 0,22 0,89 Table 5: Intensity peak flux prediction results of the best models. Prediction/observation ratio and mean squared error. From this table it was concluded that the results were better when all the flare and CME inputs were utilized. The best performances were obtained using fitting or cascade neural networks with 2 hidden layer, this indicated that the model complexity was too high to be modulated with only 1 hidden layer. The model with best test performance from the 10 sets together with the best classification model and respective binary models were utilized to the validation process, with data from solar cycle 24. Table 5 also shows the validation performance results. Analysing this table it was concluded that the validation results were in the same range of the test results which validated the models. The results presented seem to indicate that the introduction of classification models may enhance the performance of the prediction models for the intensity peak flux of SEP events. It can also be infer that the utilization of all the input variable do not have a significant impact on the performance. Comparing the validation results (solar cycle 24 data) with the ones from table 6 shows that our soft computing models are worse than the current COMESEP SEP Forecast tool in the prediction of the intensity peak flux of SEP events. While our models present a similar mean of ratio value, the Flare intensity Intensity and velocity POR 0,95 0,99 SD 0,38 0,33 Table 6: Performance measures of the COMESEP SEP Forecast tool for solar cycle 24 data. standard deviation is higher making our models overall less precise. These worst performances are attributed to the low quantity of data point available for training and test of the soft computing models. Also the range of intensities is not well covered in the data domain which means that the models presents more difficulty generalizing for values outside of the training range. 5. Conclusions In this work were derived classification tree, decision tree and neural networks models to classify the occurrence and predict the intensity of SEP events. The objective was to compare the performances of the models obtained with the performance of the present COMESEP SEP Forecasting tool. The SEP events classification model obtained was able to classify the occurrence and nonoccurrence of SEP events with better performance. With 11, 71% less Accuracy but with more 18, 95% Heidke skill score, 13, 35% True skill score, 41, 24% Sensitivity and less 36, 18% False alarm rate, than the present COMESEP SEP Forecasting tool. This shows that soft computing methods, namely neural networks, can be a good alternative for the classification of SEP events when compared to other methods. It is concluded that the inputs utilized allowed a good classification of SEP events, comparable to other inputs used in related work published in this field. Still, some of the inputs, namely, the CME velocity and CME width can only be obtain near the Earth. Which diminishes the time between the prediction and the occurrence of the SEP event. This may leads to a small period to take any preventive action that could minimise the effect of the event before its occurrence. Regarding the prediction of the intensity peak flux of SEP events the models obtained in this work where not able to outperform the actual COMESEP SEP Forecasting tool, presenting similar capacity for the prediction of the intensities but with higher variability. This performances, with higher variability, may be due to the low amount of data points available for this analysis. Which resulted in a reduced training data set and in a consequent lack of ability of the models to generalize predictions from 8

9 unseen data Achievements The major achievement of the present work was the derivation of a neural network model that can achieve better classification results than the present COMESEP SEP Forecasting tool. The results of the neural network model are also similar to the results published by Núñez [10], that used other type of input variables, namely soft x- ray, differential and integral proton fluxes. Being Núñez s results some of the best regarding SEP events classification. Other achievement was that the prediction models, for the intensity peak flux E > 10 MeV, obtained similar results to the COMESEP SEP Forecasting tool, using a data set that is too small to allow good results from a soft computing method. This shows that the soft computing methods can possible achieve better results if a larger data set would be used Future Work As future work, it is proposed to obtain more data from solar cycle 24 and to proceed to a new derivation of neural networks model to the prediction of the intensity peak flux of SEP events. An expansion of the work done to other methods like fuzzy and support vector machine models, to obtain both the classification and prediction models for SEP events, should be evaluated. Finally, the introduction of other input variables such as differential proton fluxes, which are used in related work previous published in this field, to verify if this could enhance the models performances. Acknowledgements I would like to thank to the Belgian Institute for Space Aeronomy (BIRA) for providing the data files and reference files needed to this work. A special thanks to Norma Crosby and Mark Dierckxsans by the help provided. I also would like to thank to Professors Susana Vieira and Alexandra Moutinho, without their support and motivation this work would not have been possible. References [1] Belgian institute for space aeronomy (biraiasb), EU FP7 COMESEP (coronal mass ejections and solar energetic particles: forecasting the space weather impact) project. acessed March of solar flares. Cambridge University Press, [4] R. Fletcher and M. J. D. Powell. A rapidly convergent descent method for minimization. The Computer Journaly, 6: , [5] S. B. Gabriel and G. J. Patrick. Solar energetic particle events: phenomenology and prediction. Space science reviews, 107:55 62, [6] J. S. R. Jang, C. T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing: A Computational Approach to learning and machine intelligence. Prentice-Hall, Inc, Upper Saddle River, NJ, USA, [7] G. Kopp, G. Lawrence, and G. Rottman. The total irradiance monitor (TIM): Science results. Solar Physics, 20: , [8] W. Y. Loh. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1:14 23, [9] N. Mittal et. al. On some properties of coronal mass ejection in solar cycle 23. ISRN Astronomy and Astrophysics, 2011, [10] M. Núñez. Predicting solar energetic proton events (E > 10 MeV). Space Weather, 9(7), [11] D. E. Rumelhart et. al. Learning representations by back-propagating errors. Nature, 323: , [12] S. Tracton. The washington post. capitalweathergang/2011/03/space_ weather_what_you_need_to.html, accessed March [13] F. Valach et. al. Solar energetic particle flux enhancement as a predictor of geomagnetic activity in a neural network-based model. Space Weather, 7, [2] E. R. Christian. Nasa s cosmicopia. helios.gsfc.nasa.gov/cme.html, accessed March [3] T. H. Einar and A. G. Emslie. The physics 9