Essays about: "modeling landscape use"
Showing result 1 - 5 of 22 essays containing the words modeling landscape use.
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1. Dagens och morgondagens hållbara landskapsarkitektur : klimatberäkning som vägvisare i designprocessen
University essay from SLU/Dept. of Landscape Architecture, Planning and Management (from 130101)Abstract : I dag är behovet av att hitta lösningar på klimatfrågan mer brådskande än någonsin och frågan kring hur vi ska rädda klimatet är en av de största i vår samtid. Senast 2045 ska Sverige inte ha några nettoutsläpp av växthusgaser. READ MORE
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2. Low-No code Platforms for Predictive Analytics
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. READ MORE
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3. Query Search VS ChatAI: : The nature of users’ discourse of two search paradigms
University essay from Uppsala universitet/InformationssystemAbstract : Internet search has been marked by the dominant use of query search, specifically Google, since the mid-1990s. The public release of the AI-based search tool, chatGPT, powered by a recent innovation in deep learning known as large language models (LLMs), marks a paradigm shift in internet search technology. READ MORE
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4. Bluegreengrey (BGG) solution for future climate and flood-resilient urban drainage network by enhancing the natural hydrological cycle
University essay from Lunds universitet/Avdelningen för Teknisk vattenresursläraAbstract : A loop of urbanization, increasing impervious surface in urban areas, increasing temperature, rainfall, surface runoff and flooding, and lack of new spaces have been adding challenges to urban drainage systems for decades. The climate change impacts are exposing the existing vulnerable conventional urban drainage systems to more challenges in the future. READ MORE
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5. Hybrid Deep Learning Model for Cellular Network Traffic Prediction : Case Study using Telecom Time Series Data, Satellite Imagery, and Weather Data
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Cellular network traffic prediction is a critical challenge for communication providers, which is important for use cases such as traffic steering and base station resources management. Traditional prediction methods mostly rely on historical time-series data to predict traffic load, which often fail to model the real world and capture surrounding environment conditions. READ MORE