Search for dissertations about: "data modelling"
Showing result 1 - 5 of 1882 swedish dissertations containing the words data modelling.
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1. Flood Hazard Assessment in Data-Scarce Basins : Use of alternative data and modelling techniques
Abstract : Flooding is of great concern world-wide, causing damage to infrastructure, property and loss of life. Low-income countries, in particular, can be negatively affected by flood events due to their inherent vulnerabilities. Moreover, data to perform studies for flood risk management in low-income regions are often scarce or lacking sufficient quality. READ MORE
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2. Reliable Information Exchange in IIoT : Investigation into the Role of Data and Data-Driven Modelling
Abstract : The concept of Industrial Internet of Things (IIoT) is the tangible building block for the realisation of the fourth industrial revolution. It should improve productivity, efficiency and reliability of industrial automation systems, leading to revenue growth in industrial scenarios. READ MORE
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3. Disinformative and Uncertain Data in Global Hydrology : Challenges for Modelling and Regionalisation
Abstract : Water is essential for human well-being and healthy ecosystems, but population growth and changes in climate and land-use are putting increased stress on water resources in many regions. To ensure water security, knowledge about the spatiotemporal distribution of these resources is of great importance. READ MORE
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4. Information Needs for Water Resource and Risk Management : Hydro-Meteorological Data Value and Non-Traditional Information
Abstract : Data availability is extremely important for water management. Without data it would not be possible to know how much water is available or how often extreme events are likely to occur. The usually available hydro-meteorological data often have a limited representativeness and are affected by errors and uncertainties. READ MORE
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5. Environmental Modelling : Learning from Uncertainty
Abstract : Environmental models are important tools; however uncertainty is pervasive in the modeling process. Current research has shown that understanding and representing these uncertainties is critical when decisions are expected to be made from the modeling results. READ MORE