[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117487-en":3,"doc-seo-117487-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117487,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","An Open Machine Learning Framework for Residential Water Consumption Estimation - Tesi di Laurea Magistrale","Global water management faces mounting pressure from environmental and anthropic factors, while urbanization increases residential demand and makes sustainable planning more critical. A major barrier is the lack of reliable, high-resolution data on daily residential water consumption, limiting policy design, loss reduction, and targeting of high-demand areas. Many countries either lack monitoring systems or collect data that is incomplete and unreliable, while census/survey records have low spatial and temporal detail. This thesis estimates daily residential water use using only public data and machine learning, validating CNN-based building feature extraction and XGBoost prediction.","An open machine learning framework for residential water consumption estimation  \nTesi di Laurea Magistrale in  \nComputer Science and Engineering  \nIngegneria Informatica  \nAuthor: Loris Panza  \nStudent ID: 967915  \nAdvisor: Prof. Andrea Francesco Castelletti  \nCo-advisors: Prof. Dr. Andrea Cominola (TU Berlin), M.Sc. Wenjin Hao, M.Sc. Siling Chen (TU Berlin)  \nAcademic Year: 2021-2022  \ni  \nAbstract  \nWater management is a critical topic in various parts of the world due to different environmental and anthropic factors affecting future water security. Globalization and demographic pressure are increasing water demand in highly urbanized regions, making water management in such areas critical for sustainable development. Despite infrastructure investments and technological advances, managing a city’s water resources remains a complex task due to various issues. One of the most significant issues is certainly the lack of reliable data on residential water consumption, which limits our knowledge on current and future water demands. Such data is essential for designing and implementing effective water management policies and programs, from reducing any losses to identifying high-demand areas or developing water conservation programs. Unfortunately, many countries lack adequate monitoring systems, and even when they exist, the collected data is often incomplete or unreliable. On the other hand, public water consumption data collected through censuses and surveys have low spatial resolution and temporal frequency, limiting the accuracy of analysis and progress in sustainable water resource management. This thesis proposes an innovative approach to address the lack of data: the daily water consumption of residential structures is estimated using only public data and machine learning techniques. After a careful analysis of different Convolutional Neural Networksand training techniques, two architectures were chosen: one for filtering Google Street View images of building’s facades and another for estimating the relative height. Combining that information with the building area and socio-demographic data, XGBoost reaches the best performance in predicting daily water consumption among various machine learning algorithms evaluated. The work demonstrated the validity of the machine learning techniques developed in the methodology overcoming the accuracy of approximative formulas and highlighting the potential of public data in supporting sustainable water management.  \nKeywords: Water Demand, Open Data, Sustainability, Machine Learning, Deep Learning  \nAbstract in lingua italiana  \nLa gestione dell’acqua rappresenta un tema critico in varie zone del mondo dovuto adiversi fattori ambientali ed antropici. All’incapacità di accedere e distribuire l’acqua, sono soprattutto i paesi a basso reddito che non hanno la capacità economica di effettuare gli investimenti necessari per infrastrutture adatte. La globalizzazione e la crescente pressione demografica aumentano la domanda nelle regioni più urbanizzate, rendendo lagestione dell’acqua più rilevante ai fini di uno sviluppo sostenibile. Nonostante i progressi tecnologici, la gestione idrica di una città rimane ancora un compito complesso dovuto aproblematiche di diversa natura. Tra queste una delle più significative è la mancanza didati affidabili sui consumi d’acqua a livello residenziale. Essi sono fondamentali per progettare e implementare politiche e programmi efficaci per la gestione idrica: dalla riduzione di eventuali perdite all’identificazione di aree ad alta richiesta. Purtroppo, molti paesinon dispongono di sistemi di monitoraggio adeguati e i consumi idrici pubblici rilevati tramite censimento e sondaggi hanno una risoluzione spaziale e una frequenza temporale bassa che limita la precisione dell’analisi e i progressi nella gestione sostenibile delle risorseidriche. In tale contesto, il lavoro di tesi propone un approccio innovativo nella risoluzione della mancanza di dati: si è ri","cbCairfE6rI16tMe","https://ap.wps.com/l/cbCairfE6rI16tMe","pdf",9866164,1,130,"English","en",105,"# 1 Introduction\n# 2 State of art the review\n## 2.1 Water demand determinants\n## 2.2 Social sensing and remote sensing data\n## 2.3 Deep learning for socio-economic features extraction\n## 2.4 CNN: feature extraction and end-to-end learning\n## 2.5 Water demand estimation with RS data\n## 2.6 Research challenges\n# 3 Methodology\n## 3.1 Methodology pipeline\n## 3.2 Theoretical background on Neural Networks and Convolutional Neural Networks\n## 3.3 GSV image acquisition\n## 3.4 GSV image selection: Places365-VGG16\n## 3.5 CNN for building height estimation\n## 3.5.1 GSV images preprocessing\n## 3.5.2 Baseline model\n## 3.5.3 Pretrained models: VGG16, ResNet50 and Places36","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses the lack of reliable data on residential water consumption, which restricts accurate analysis and effective sustainable water management policies.\"},{\"question\":\"How is daily residential water consumption estimated?\",\"answer\":\"Daily consumption is estimated using public data combined with machine learning, including CNN-based extraction from Google Street View images and building information.\"},{\"question\":\"Which model achieves the best prediction performance?\",\"answer\":\"XGBoost achieves the best performance for predicting daily water consumption among the evaluated machine learning algorithms.\"}]","An Open Machine Learning Framework for Residential Water Consumption Estimation - 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