[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118660-en":3,"doc-seo-118660-105":30,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118660,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","IONOSPHERIC SCINTILLATION FORECASTING USING MACHINE LEARNING","This study develops a machine learning approach to forecast GNSS amplitude scintillation severity using historical GNSS scintillation monitoring receiver data. Amplitude scintillation arises from ionospheric electron density irregularities and manifests as fluctuations in received GNSS signal power, commonly quantified by the S4 index. Because real-time S4 data is not always available, the model predicts low, medium, or high severity levels from time- and space-related factors, comparing multiple ML methods. XGBoost achieved the best performance with 77% prediction accuracy on a balanced dataset.","IONOSPHERIC SCINTILLATION FORECASTING USING MACHINE LEARNING  \narXiv :2409 .00118v1 [ ee ss . SP] 28 Aug 2024  \nSultan Halawa, Maryam Alansaari, Maryam Sharif, AmelAlhammadi, Ilias Fernini  \nSharjah Academy for Astronomy Space Sciences and Technology University of Sharjah, Sharjah, United Arabic Emirates  \nABSTRACT  \nThis study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the ionosphere cause 􀀃uctuations in GNSS signal power. These 􀀃uctuations can be measured using the S4 index, but real-time data is not always available. The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation, categorizing it into low, medium, or high severity levels based on various time and space-related factors. Among six different ML models tested, the XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset. This work underscores the effectiveness of machine learning in enhancing the reliability and performance of GNSS signals and navigation systems by accurately predicting amplitude scintillation severity.  \nIndex Terms— Ionosphere, Scintillation, GNSS, Machine Learning  \n1. INTRODUCTION  \nIonospheric scintillation in Global Navigation Satellite System (GNSS) signals is 􀀃uctuations in the ionospheric electron density. Such variation in the ionosphere refractive index results in rapid 􀀃uctuations in the amplitude and phase of GNSS signals as they travel through the ionosphere [1] . The transit of GNSS signals in the ionosphere creates deviationsin this layer’s refractive index, causing remarkable changes in the amplitude and phase of the signals [1] . The scintillation in amplitude is evident in signi􀀂cant changes in the signal noise ratio (SNR) of the GNSS signals, which, in turn, affects the GNSS system performance stability, reliability, and accuracy. In addition, amplitude scintillation is a serious menace to the quality of services provided by the GNSS satellites, often negatively affecting the signal quality by the 􀀂nal users. Therefore, the primary means of quantifying the amplitude scintillation, especially in the evaluation of GNSS signals, is by using the S4 index. It measures the severity of the amplitude 􀀃uctuations very accurately. The S4 Index is a quantitative measure derived by dividing the standard deviation of the  \nsignal power by the mean signal power [2] . Not only does it provide a means of measure, but it is also widely used in operational applications and academic research. S4 index data is produced continuously by specialized scintillation monitoring receivers that constantly track the SNR of GNSS signals. This, in turn, provides continual access to real-time S4 index data, which allows fast identi􀀂cation and investigation of scintillation events [3] .  \nIn regions lacking a Satellite-Based Augmentation System (SBAS) or real-time ground correction, accurate ionospheric scintillation forecasting is crucial due to reliance on model-based GNSS corrections. Limited regional data availability can lead to inaccuracies in these models, compounded by the dynamic and nonlinear nature of ionospheric behavior in􀀃uenced by solar and geomagnetic activities. To address this, advanced forecasting models are necessary to improve prediction accuracy, especially in data-scarce areas. This research aims to develop and recommend predictive models for ionospheric scintillation, contributing to better precision and reliability of satellite-based systems in regions without realtime correction systems.  \nThree major recent advances signi􀀂cantly contribute to machine learning for ionospheric scintillation. The work in paper [4] explores the potential of historical data from a single G.P.S. scintillation monitoring receiver to train vario","cbCaiqTMiikEhqbV","https://ap.wps.com/l/cbCaiqTMiikEhqbV","pdf",97691,1,5,"English","en",105,"# Abstract\n# Introduction\n## Motivation and problem context\n## Role of S4 index and data availability\n## Related work and study differences","[{\"question\":\"Which machine learning model performed best and how was it evaluated?\",\"answer\":\"Among six tested models, XGBoost performed best, reaching 77% prediction accuracy when trained on a balanced dataset, and was selected as the most effective approach.\"}]","IONOSPHERIC SCINTILLATION FORECASTING USING MACHINE LEARNING | PDF",1785684777,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":28},"ionospheric-scintillation-forecasting-using-machine-learning","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ionospheric-scintillation-forecasting-using-machine-learning/118660/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine learning model performed best and how was it evaluated?","Question",{"text":76,"@type":77},"Among six tested models, XGBoost performed best, reaching 77% prediction accuracy when trained on a balanced dataset, and was selected as the most effective approach.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":21,"slug":130},19,"General","general"]