[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119458-en":3,"doc-seo-119458-105":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},119458,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Improving sub-seasonal drought forecasting via machine learning to leverage climate data at different spatial scales - Thesis","Drought represents one of the most dangerous natural extremes, driving substantial societal and economic impacts in Europe. This thesis develops machine-learning approaches to improve sub-seasonal drought forecasting by integrating climate information across different spatial scales. The work introduces a climate state intelligence component based on Niño index phase analysis, evaluates multiple neural and learning models, and benchmarks results against baseline and naïve forecasts. It further analyzes how climate and local atmospheric variables influence forecast performance.","POLITECNICO DI MILANO  \nGeoinformatics Engineering Master of Science School of Civil Environmental and Land Management Engineering  \nImproving sub-seasonal drought forecasting via machine learning to leverage climate data at different spatial scales  \nSupervisor: Prof. Marcello Restelli  \nCo-Supervisors: Dr. Claudia Bertini,  \nProf. Matteo Giuliani  \nThesis by: Francesco Bosso Student ID: 965085  \nAcademic Year 2021 / 2022  \nTable of Content  \nAbstract ............................................................................................... 5  \nAbstract – Ita....................................................................................... 6  \n1 Introduction...................................................................................... 7  \n1.1 Context .......................................................................................................... 7  \n1.2 Motivation and research questions ................................................................ 7  \n1.3 Proposed approach and original contributions .............................................. 8  \n1.4 Outline of contents ........................................................................................ 9  \n2 State of art ...................................................................................... 10  \n2.1 Weather and precipitation forecasting at sub-seasonal lead-times.............. 10  \n2.2 Droughts ...................................................................................................... 15  \n2.2.1 Background on droughts .................................................................................. 15  \n2.2.2 Drought types ................................................................................................... 16  \n2.2.3 Drought indices ................................................................................................ 18  \n2.2.4 Droughts over Europe ......................................................................................21  \n2.2.5 Drought forecasting .........................................................................................25  \n2.3 Teleconnection patterns and climate indices ............................................... 29  \n3 Data and case study ....................................................................... 37  \n3.1 Case study.................................................................................................... 37  \n3.2 Dataset description ...................................................................................... 39  \n3.2.1 Climate indices................................................................................................. 39  \n3.2.2 Global variables ...............................................................................................40  \n3.2.3 Local variables .................................................................................................41  \n3.3 Data pre-processing and cleaning................................................................ 44  \n4 Methodology ................................................................................... 47  \n4.1 Niño Index Phase Analysis and Climate State Intelligence ........................ 49  \n4.2 Machine Learning algorithms ..................................................................... 54  \n4.2.1 Extreme Learning Machine.............................................................................. 54  \n4.2.1.1 Case study application ...........................................................................................56  \n4.2.2 Feed-Forward Neural Network ........................................................................ 57  \n4.2.2.1 Case study application ...........................................................................................60  \n4.2.3 Convolutional Neural Network........................................................................ 67  \n4.2.3.1 Case study application ............................","cbCaiuuBLWYLyszI","https://ap.wps.com/l/cbCaiuuBLWYLyszI","pdf",8308985,1,113,"English","en",105,"# Abstract\n## Introduction\n## State of Art\n## Data and Case Study\n## Methodology\n## Results and Discussion\n## Conclusions\n## References","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses improving the forecasting of sub-seasonal droughts, which are major natural hazards with significant economic costs.\"},{\"question\":\"How does the proposed approach use climate data?\",\"answer\":\"It leverages climate data at different spatial scales and incorporates climate state intelligence via Niño index phase analysis.\"},{\"question\":\"Which machine learning methods are evaluated?\",\"answer\":\"The thesis evaluates Extreme Learning Machine, Feed-Forward Neural Network, and Convolutional Neural Network, and compares their performances.\"}]","Improving sub-seasonal drought forecasting via machine learning to leverage climate data at different spatial scales - Thesis | 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