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The present study aims to investigate the association of X-ray solar flares with the magnetic complexity of sunspot groups in active regions for the period from 1996 to 2023 (solar cycles 23 to 24 and ascending phase of solar cycle 25). Statistically, we have found that (1) the total 37,166 sunspots are affiliated with the 5412 active regions. Following the Hale magnetic complexity categorization method, a total occurring sunspot groups, comprised of 31.01% α, 57.13% β, 0.01% γ, 0% δ, 0.027% γδ, 0.54% βδ, 8.45% βγ, and 2.83% βγδ magnetic groups. (2) We also found that a total of 44,667 solar flares were observed in selected time intervals. Out of that total of 44,667 solar flares, only 24,031 (60.40%) flares have their solar active region, remaining flares have an unknown solar origin. Further investigation also shows that, out of 24,031 solar flares only 23,531 solar flares are associated with sunspot groups of different magnetic complexity (according to Hale magnetic classification). These sunspot groups are affiliated with the 2499 active regions. The investigated flares (sunspot-associated) database consists of 34.11% B-, 57.87% C-, 7.36% M-, and 0.63% X-class flares. Finally, we found that a higher percentage of the intense flares (M- and X-Class) are associated with the βγδ type sunspot groups in solar cycles 23 and 24. We also observed that maximum X-class flares are associated with βγδ sunspot groups during the peak of solar cycles 23 and 24. Whereas maximum B- and C-class flares are associated with β sunspot groups during the ascending phase of each solar cycle. [ABSTRACT FROM AUTHOR]

In order to deeply analyze the drought and flood disaster response patterns of Tuojiang River Basin under the background of climate anomalies, this paper reconstructs the time series of drought and flood disaster of Tuojiang River Basin in the Qing Dynasty, and analyzed its related data with mathematical statistics, mutation test, power spectrum cycle analysis, ADF and Granger causality test, etc., to explore the occurrence patterns of drought and flood disasters in the Tuojiang River Basin in the Qing Dynasty and its climate driving force factors. The results show that, during the Qing Dynasty, 208 drought and flood disaster occurred in the Tuojiang River Basin, with an average of one drought and flood disasters occurring every 1.29 years. There were more flood disasters than drought disasters, with a total of 21 persistent flood and drought disasters and an overall high level of severity of disasters. Drought and flood disasters were interspersed frequently, and there were also drought and flood disasters coexisting during the same year. Drought and flood disasters were concentrated in a chain-like distribution from 1800 to 1910.The number of disasters peaked in the middle of the Qing Dynasty, and mega-disasters were concentrated in the middle and late Qing Dynasty. The year 1863 was an important mutation point for drought and flood disasters, after which the number of disasters increased significantly. Drought and flood disasters in the Qing Dynasty were significantly correlated with the average annual sunspot number, and continuous disaster events were more frequent around sunspot extreme years.The change cycle of drought and flood disasters is about 2.5 a, which is strongly consistent with the ENSO cycle. The number of drought and flood disasters in the Tuo River Basin was significantly higher during ENSO event years. With a lag period of 1a, sunspot activity and ENSO events are the main climatic triggers of drought and flood disasters changes. The research results not only fill the research gap of drought and flood disasters mechanism in the Tuojiang River Basin under climate change, but also enrich the knowledge of spatial and temporal distribution pattern and regularity of drought and flood disasters in the Yangtze River Basin. It provides a useful reference for strengthening the scientific use of water resources and disaster prevention and mitigation, and also provides an important reference for the flood and drought prevention and ecological civilization construction in the Tuojiang River Basin. [ABSTRACT FROM AUTHOR]

Space weather profoundly impacts Earth and its surrounding space environment, necessitating improved prediction to safeguard critical infrastructure such as communication and satellites. Solar flares can disrupt communications and pose radiation risks to airline passengers. While traditional methods offer rough estimates of solar activity trends, the potential of artificial intelligence in this domain warrants exploration. This study addresses this research gap by evaluating the performance of recurrent neural networks (RNNs) for sunspot forecasting and assessing the suitability of extreme gradient boosting (XGBoost) for solar event classification. Two publicly available datasets serve as the foundation for this research. To enhance algorithm performance through optimal hyperparameter selection, metaheuristic optimizers are employed. A unique contribution is the introduction of a modified particle swarm optimization algorithm, specifically tailored to the study's needs. Two experiments were conducted: In the first, RNNs predicted sunspot occurrence up to three steps ahead. The best-performing model, optimized using the introduced modified metaheuristic, achieved an impressive R 2 value of 0.840448, surpassing competing algorithms. In the second experiment, XGBoost models assessed solar flare severity, with the top model again optimized by the modified metaheuristic, achieving an accuracy of 0.981565. This novel approach highlights the potential for enhancing solar activity forecasting techniques and offers valuable insights into feature impacts on model decisions, thereby advancing our understanding of space weather. [ABSTRACT FROM AUTHOR]

Solar radio bursts are intense radio radiation sources that occur during the energy-release process and represent a hot topic in solar-physics and space-weather research. In this paper, we present a multimode prediction model for daily solar radio bursts. The model uses deep learning and machine learning to obtain data information from different dimensions and to establish the relationship between the characteristics of the solar active region on the solar surface and solar radio bursts. For this model, we use data from the Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) total solar magnetic map, the Royal Observatory of Belgium World Data Centre in Brussels, and NOAA sunspot parameters (including number, area, and type of sunspots) as inputs. The output results are then compared with the list of solar radio bursts recorded by the Radio Solar Telescope Network (RSTN) to determine whether solar radio bursts are present and to determine the key parameters for determining radio bursts. Based on 5449 days of observational data, we find that the prediction accuracy of the model is 0.898 ± 0.011, and that the number of sunspots is a key parameter in determining the occurrence of solar radio bursts. Specifically, when the number of sunspots is greater than 15, the probability of occurrence of solar radio bursts is greater than 90%. We have identified the key parameters and thresholds for determining solar radio bursts and highlighted the key parameters for space-weather prediction. In addition, the prediction model can also be used for predicting in other fields. [ABSTRACT FROM AUTHOR]

A huge collection of solar images to visualize sunspot are acquired by various solar observatories spread across the globe. This necessitates efficient tools for detecting and analyzing the sunspots encompassing diverse solar features. One such contribution is delivered in this work by exploiting the intrinsic intensity variations of solar images associated with sunspots and their attributes. The presented mechanism initially, pre-processes the acquired solar images by correcting the intensity variations introduced while profiling from the sun center to the limb followed by smoothening using a localized window. The resultant is then differenced from the global threshold that is obtained as a result of the statistical analysis computed over the probability distribution function of the input image. This arrangement offers higher discerning variations concerned with the local contextual structures related to sunspot, umbra, and penumbra. Also, it captures the major gradient variation between these regions that adds to the pixel heterogeneity surrounding them to finally render an automatic sunspot detection mechanism distinguishing the diverse solar regions. Receiver Operating Characteristics (ROC) investigation on annual solar images in Flexible Image Transport System (FITS) format reveals the presented method's efficacy. Also, Pearson correlation analysis of the evaluated sunspot numbers from the detected sunspots with the solar catalog reveals the scheme's detection closeness. Moreover, the model's simplicity analyzed along the time and space dimensions affirms its extension to real-time analysis • Fully automated adaptive thresholding sunspot detection framework delineating umbra & penumbra. • Local-Global thresholding method exploiting intensity variations across multiple levels. • ROC analysis in both manual and automatic modes affirms scheme's performance. • Higher correlation values attained for sunspot numbers with the HFC and SILSO catalogs. • Complexity analysis reveals the involved minimal operations without compromising performance. [ABSTRACT FROM AUTHOR]