[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117485-en":3,"doc-seo-117485-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117485,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Investigating Tropical Cyclogenesis Drivers with Causal Analysis and Machine Learning - Master’s Thesis","Tropical cyclones threaten life, ecosystems, and infrastructure worldwide, while tropical cyclogenesis (TCG) remains difficult to predict due to complex environmental interactions and limited observational data. The thesis addresses weaknesses of large-scale Genesis Potential Indices (GPIs), proposing a framework that combines causal discovery with machine learning. It identifies key causal relationships among atmospheric and oceanic variables using high-resolution data, then evaluates prediction models through both pixel-wise and basin-wide analyses.","Investigating Tropical Cyclogenesis Drivers with Causal Analysis and Machine Learning  \nTesi di Laurea Magistrale in  \nComputer Science and Engineering - Ingegneria Informatica  \nAuthor: Alice Arneodo  \nStudent ID: 101267  \nAdvisor: Prof. Marcello Restelli  \nCo-advisors: Dott. Paolo Bonetti, Dott. Filippo Dainelli, Dott. Guido Ascenso, Prof. Andrea Castelletti  \nAcademic Year: 2023-24  \ni  \nAbstract  \nTropical cyclones are intense atmospheric systems that pose significant threats to life, ecosystems, and infrastructures worldwide. Tropical Cyclogenesis (TCG) occurs when a tropical disturbance intensifies into a fully-formed cyclone. The difficulty of the prediction of TCG is due to the complex interplay of environmental variables and the limited number of data available. Large-scale indices like Genesis Potential Indices (GPIs) are commonly used to estimate the likelihood of TCG. However, their predictive skill is often limited by regional discrepancies and computational complexity. This thesis proposes a novel approach to improve TCG prediction by leveraging causal discovery and machine learning techniques. The methodology identifies key causal relationships between various atmospheric and oceanic variables and their role in TCG prediction in both pixel-wise and basin-wide analyses. High-resolution observational data and causal discovery algorithms are used to uncover the underlying drivers of TCG. The results highlight significant regional variations in causal factors and emphasize the importance of localized environmental conditions. The performance of the proposed machine learning-based prediction models is compared with the traditional Emanuel-Nolan GPI index, demonstrating superior accuracy in predicting TCG in some ocean basins, such as North West Pacific. The key contribution of this thesis is the identification of critical causal factors for TCG, developing a novel machine learning framework for cyclone prediction to validate the efficacy of the selected causal relevant variables. These findings suggest that machine learning models can improve TCG forecasting accuracy, with promising results that could enhance difficult tasks such as disaster risk management and future climate predictions. Ultimately, this research represents a step toward more reliable, region-specific forecasting of tropical cyclones, offering potential benefits for mitigation and adaptation strategies in cyclone-prone regions.  \nKeywords: tropical cyclones; tropical cyclogenesis; genesis potential index; machine learning; feature selection; neural network; transfer entropy; causality; causal discovery  \nAbstract in lingua italiana  \nI cicloni tropicali sono sistemi atmosferici intensi che rappresentano minacce significative per la vita, gli ecosistemi e le infrastrutture a livello globale. La genesi del ciclone tropicale (TCG) si verifica quando una perturbazione tropicale si intensifica trasformandosi in un ciclone completamente formato. La difficoltà nella previsione della TCG è dovuta alla complessa interazione tra le variabili ambientali e alla quantità limitata di dati disponibili. Indici su larga scala come i Genesis Potential Indices (GPIs) sono comunemente utilizzati per stimare la probabilità di TCG. Tuttavia, la loro capacità predittiva è spesso limitata da discrepanze regionali e dalla complessità computazionale. Questa tesi propone un approccio innovativo per migliorare la previsione del TCG, sfruttando tecniche di causal discovery e di machine learning. La metodologia identifica le principali relazionicausali tra varie variabili atmosferiche e oceaniche e il loro ruolo nella previsione del TCG, sia in analisi pixel-per-pixel che su scala di bacino. Vengono utilizzati dati osservativi ad alta risoluzione e algoritmi di causal discovery per svelare i fattori sottostanti del TCG. I risultati evidenziano significative variazioni regionali nei fattori causali e sottolineano l’importanza delle condizioni ambientali locali. Le prestazioni dei","cbCailVItIt9DRcs","https://ap.wps.com/l/cbCailVItIt9DRcs","pdf",5326578,1,122,"English","en",105,"# Introduction\n## Background\n## Machine Learning\n## Causal Inference\n## Feature Selection via Information Theory","[{\"question\":\"Why is tropical cyclogenesis (TCG) hard to predict?\",\"answer\":\"TCG prediction is challenging because multiple environmental variables interact in complex ways and because available data are limited.\"},{\"question\":\"How does the thesis improve upon Genesis Potential Indices (GPIs)?\",\"answer\":\"It leverages causal discovery and machine learning to uncover underlying drivers, aiming to reduce limitations caused by regional discrepancies and computational complexity.\"},{\"question\":\"What is the main contribution of the proposed approach?\",\"answer\":\"The thesis identifies critical causal factors for TCG and builds machine learning models that validate the usefulness of selected causally relevant variables for cyclone prediction.\"}]","Investigating Tropical Cyclogenesis Drivers with Causal Analysis and Machine Learning - Master’s Thesis | PDF",1785676124,307,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"investigating-tropical-cyclogenesis-drivers-with-causal-analysis-and-machine-learning-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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/investigating-tropical-cyclogenesis-drivers-with-causal-analysis-and-machine-learning-masters-thesis/117485/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is tropical cyclogenesis (TCG) hard to predict?","Question",{"text":75,"@type":76},"TCG prediction is challenging because multiple environmental variables interact in complex ways and because available data are limited.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis improve upon Genesis Potential Indices (GPIs)?",{"text":80,"@type":76},"It leverages causal discovery and machine learning to uncover underlying drivers, aiming to reduce limitations caused by regional discrepancies and computational complexity.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main contribution of the proposed approach?",{"text":84,"@type":76},"The thesis identifies critical causal factors for TCG and builds machine learning models that validate the usefulness of selected causally relevant variables for cyclone prediction.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]