Solar activity prediction studies and services in NAOC

Solar activity forecasting uses numerous methods. Some methods employed are purely numerological, whereas others utilize readily available, well‐established mathematical techniques. There often is, however, no physical basis for some of these methods. Within this arena, however, there is a small subset, called “precursors,” which does have a physical basis. We briefly describe the physical basis of these methods and relate them to solar dynamo physics. Further, we develop techniques to reduce one major source of uncertainty or “error” in these solar activity predictions. This uncertainty is in the timing of the solar cycle. The importance of solar activity timing is apparent when one considers that past solar cycles have periods varying from 8 to 17 years. This makes the timing of the solar cycle only second in importance in solar activity prediction to obtaining the amplitude of any cycle. Two solar phenomena are utilized to obtain a “time mark” for each solar cycle. These are the “polar field reversal” and the march of activity centers towards the equator. This latter phenomenon is often referred to as the “butterfly diagram.” These timings essentially provide a “phasing” of the cycle. With this, we provide a preliminary prospective on solar cycle 24.

We derive two principal components (PCs) of temporal magnetic field variations over the solar cycles 21–24 from full disk magnetograms covering about 39% of data variance, with σ = 0.67. These PCs are attributed to two main magnetic waves travelling from the opposite hemispheres with close frequencies and increasing phase shift. Using symbolic regeression analysis we also derive mathematical formulae for these waves and calculate their summary curve which we show is linked to solar activity index. Extrapolation of the PCs backward for 800 years reveals the two 350-year grand cycles superimposed on 22 year-cycles with the features showing a remarkable resemblance to sunspot activity reported in the past including the Maunder and Dalton minimum. The summary curve calculated for the next millennium predicts further three grand cycles with the closest grand minimum occurring in the forthcoming cycles 26–27 with the two magnetic field waves separating into the opposite hemispheres leading to strongly reduced solar activity. These grand cycle variations are probed by α − Ω dynamo model with meridional circulation. Dynamo waves are found generated with close frequencies whose interaction leads to beating effects responsible for the grand cycles (350–400 years) superimposed on a standard 22 year cycle. This approach opens a new era in investigation and confident prediction of solar activity on a millenium timescale.

The Sun exhibits centennial-scale activity variations and sometimes encounters grand solar minimum when solar activity becomes extremely weak and sunspots disappear for several decades. Such an extreme weakening of solar activity could cause severe climate, causing massive reductions in crop yields in some regions. During the past decade, the Sun’s activity has tended to decline, raising concerns that the Sun might be heading for the next grand minimum. However, we still have an underdeveloped understanding of solar dynamo mechanisms and hence precise prediction of near-future solar activity is not attained. Here we show that the 11-year solar cycles were significantly lengthened before the onset of the Maunder Minimum (1645–1715 CE) based on unprecedentedly high-precision data of carbon-14 content in tree rings. It implies that flow speed in the convection zone is an essential parameter to determine long-term solar activity variations. We find that a 16 year-long cycle had occurred three solar cycles before the onset of prolonged sunspot disappearance, suggesting a longer-than-expected preparatory period for the grand minimum. As the Sun has shown a tendency of cycle lengthening since Solar Cycle 23 (1996–2008 CE), the behavior of Solar Cycle 25 can be critically important to the later solar activity.

Solar activity could have significant impacts on various Earth and near-Earth space systems, such as satellite communication and power grid systems. The prediction of solar activity and active solar events plays a major role when preparing for these disturbances. Various satellite-based instruments constantly observe the Sun. However, only a few ground-based solar instruments could provide versatile enough information for the space weather prediction. Metsähovi Radio Observatory of Aalto University (Finland) has a unique collection of millimetre (8 mm) solar radio maps over the past 40 years, and even denser observational solar radio data catalogue since 2011. About 75–80 % days yearly are covered nowadays. This gives opportunity to make statistical estimation of solar flare occurrence based on solar radio maps. In this study, we had 2253 days when both solar radio map and GOES (Geostationary Operational Environmental Satellites) classified solar flare were observed. In this work, we used solar flare classification done by the Space Weather Centre (SWC) of the National Oceanic and Atmospheric Administration (NOAA). The data were observed between 1 January 2011 and 12 September 2022. Our study shows that the maximum intensity of radio brightenings is a good indicator to tell which kind of GOES classified solar flare could be expected to happen. The article presents that intense radio brightening is needed to produce a certain GOES classified solar flare.

Two primary solar-activity indicators–sunspot numbers (SNs) and sunspot areas (SAs) in the time interval from November 1874 to December 2012 – are used to determine the chaotic and fractal properties of solar activity. The results show that (1) the long-term solar activity is governed by a low-dimensional chaotic strange attractor, and its fractal motion shows a long-term persistence on large scales; (2) both the fractal dimension and maximal Lyapunov exponent of SAs are larger than those of SNs, implying that the dynamical system of SAs is more chaotic and complex than SNs; (3) the predictions of solar activity should only be done for short- to mid-term behaviors due to its intrinsic complexity; moreover, the predictability time of SAs is obviously smaller than that of SNs and previous results.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Accuracy of data and information on various types of land use and land cover (LULC) is crucial in watershed management planning. The development of remote sensing technology in predicting LULC has been widely used, such as the Support Vector Machine (SVM) and Random Trees (RT) methods. Both methods are part of the model of machine learning and belong to the category of supervised learning. This method can predict, study, and recognize data patterns from the results of training data. The aim of this research is the use of SVM and RT methods for the identification of LULC. Based on the SVM method analysis results, the forest area is 211.61 km2, coffee plantations are 32.92 km2, shrubs are 16.85 km2, built-up/bare land is 3.40 km2, the water body is 3.17 km2, and cloud/shadow is 6.79 km2. While the RT method obtained a forest area of 215.51 km2, coffee plantations covering an area of 29.32 km2, shrubs are covering an area of 16.32 km2, built-up/bare land surrounding an area of 7.22 km2, water body covering an area of 2.62 km2, and cloud/shadow covering an area of 3.75 km2. Overall, the percentage accuracy of the SVM method of 87%, and the RT of 86%. The success rate of modeling SVM (AUC = 0.73) and RT (AUC = 0.75) based on the results of data validation using the area under the receiver operating characteristic curve (AUC) was considered fair good.

The specifications of the forests in Kalimantan are part of the vegetation type which is characteristic of dry lowland forests that are rich in biodiversity. The Kalimantan forest area is known as an area that has the highest vascular biodiversity in the world. Until 2014, the rate of forest loss as a source of biodiversity was always high every year. The National Polar-Orbiting Suomi Partnership (Suomi NPP) is a new generation of weather satellite imagery from NASA which currently continues to develop application algorithms for environmental monitoring. The support vector machine (SVM) method is a one-class learning machine method with artificial neural network methods that can recognize patterns from input or examples given and also include supervised learning. This study aims to analyze the effect of each parameter on the SVM method and the combination that can produce the highest accuracy, and analyze the ability of the SVM method for making hotspot maps based on the number of forest fire areas in East Kalimantan. The results of the study showed that the Support Vector Machine (SVM) method on the spectral data produced a total of 85%.

On 6 February 2011, the two Solar TErrestrial Relations Observatory (STEREO) spacecraft reached 180° separation and began imaging the entire far-side hemisphere of the Sun in extreme ultraviolet light (EUV). Here, we compare STEREO’s observations of far-side solar activity, as evidenced by bright regions in the EUV images, to predictions of far-side active regions from helioseismology using National Solar Observatory Global Oscilla-tion Network Group (GONG) observations. GONG produces seismic Carrington maps of strong magnetic field regions, labeling far-side regions with a probability ≥ 70%. By visual comparison of these GONG maps with STEREO EUV Carrington maps, we determine whether or not solar activity is observed at the locations of the predicted active regions. For data from February – June 2011, we find that for 139 of 157 comparisons activity is observed in EUV at the predicted site, yielding an 89% success rate. For 18 comparisons, no activity was seen at the predicted region (11% false predictions). We also investigated GONG’s success at predicting large active regions before they appear at the east limb as viewed from Earth. Of 15 such east-limb active regions, eight were predicted by GONG at least once in the seven days preceding their Earth-side appearance. STEREO B observations of activity in the days preceding the appearance of the other seven large East-limb active regions indicated that, while three were possibly too small for GONG to make a prediction, four seemed as large and active as other active regions that had been predicted successfully by GONG.

Sirekap is a mobile application that was built to help the public monitor and oversee the development of the 2024 elections held in Indonesia. The research aims to apply the Support Vector Machine algorithm in analyzing sentiment about the use of the Sirekap application in 2024. The Support Vector Machine method is used to classify user sentiment into classes, namely positive, negative, and very negative. The amount of data used is 15,000 data sourced from Sirekap application reviews on Google PlayStore, with more detailed research stages including data collection, data preprocessing, data labeling, visualization, word weighting, and testing and analysis. The results show that the Support Vector Machine algorithm provides an accuracy of 88% for the Sirekap 2024 application. These results are expected to help developers to develop further the Sirekap 2024 application in improving the quality of the application and providing better user comfort Based on the results of the sentiment analysis of Sirekap 2024 application users on the Google Play store using the Support Vector Machine (SVM) method, an accuracy rate of 88% was obtained in classifying the sentiment of reviews into positive, negative, and very negative. This shows that the Support Vector Machine method is quite accurate for sentiment analysis of Indonesian text data. Overall, most reviews are very negative with a percentage reaching 76.9%, followed by negative reviews at 12.6%, and the least are positive reviews at 11%.

In this paper, we develop a low-cost portable spectrometer based on Support Vector Machines (SVM) method. The purpose of the spectrometer development is for halal food research project. The portable spectrometer uses various light-emitting diodes (LEDs), light sensor and Arduino microcontroller. The initial research of the spectrometer is testing various oil, such as palm oil, olive oil, sesame oil, soybean oil, and lard. The waveform analysis of each oil can be distinguished by spectroscopy method. But, by using Support Vector Machines (SVM) method the various oil can be distinguished and classified better. The data measurement, classification, and graphical interface result show on the interface of Raspberry Pi based on Python 2.7. Thus, the spectrometer can be used for lard detection.

Validation of CAS’s final global ionospheric maps during different geomagnetic activities from 2015 to 2017

Beras Miskin (Raskin) Program is a program of social protection, as supporters of other programs such as nutrition improvement, healthy increase, education and productivity improvement of Poor Households. According to Badan Pusat Statistika, there were 14 criteria to determine a household is classified as poor households. Based on these criteria it will be classified of recipient households and non-recipient households of Beras Miskin (Raskin) Program by Support Vector Machine (SVM) method using LibSVM. The concept of classification by SVM is search for the best hyperplane which serves as a separator of two classes of data in the input space. Kernel function is used to convert the data into a higher dimensional space to allow a separation. LibSVM is a package program created by Chih-Chung Chang and Chih-Jen Lin from Department of Computer Science at National Taiwan University. The method used by LibSVM to obtain global solution of duality lagrange problem is decomposition method. To determine the best parameters of kernel function, used k-vold cross validation method and grid search algorithm. In this classification by SVM method using LibSVM, obtain the best accuracy value as 83,1933%, which is the kernel function Radial Basis Function (RBF). Keywords : Beras Miskin (Raskin) Program, Classification, Support Vector Machine (SVM), LibSVM, Kernel Function

Of the 829 active volcanoes distributed worldwide, 129 are located in Indonesia. Two recent eruptions of Mount Agung on Bali Island, Indonesia, on November 21, 2017, and January 11, 2018, produced massive ash, steam, and gas emissions. Rainwater carried these pyroclastic materials in cold lahars to southwestern parts of the island. Because explosive eruptions of Mount Agung during 1963–1964 produced voluminous ashfall and catastrophic pyroclastic flows, the monitoring of this volcano has been considered essential. Land cover (LC) mapping is one method commonly used to monitor the spread of materials in volcanic areas due to the inaccessibility of field data during ongoing eruptions. We analyzed multispectral data using two different classifiers: an artificial neural network (ANN) and a support vector machine (SVM). Landsat imagery was used to generate a LC map with four feature classes: rock and sand, vegetation, cloud, and shadow. The ANN method was more accurate than the SVM method, with classification accuracies of 94.67% and 97% for the first and second Mount Agung eruptions, respectively. The SVM classifier was better than ANN at classifying images taken prior to eruption, with an overall accuracy of 91.60%. Thus, both classifiers accurately distinguished eruption products and environmental features, and are suitable for LC classification in volcanic regions.