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using learning algorithms as Extreme Learning Machine (ELM) is that training data should cover the entire domain of process parameters to achieve accurate generalization of the trained model to new
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will examine how the configuration, connectivity and condition of these dynamic water systems, and their surrounding land cover, influence environmental buffering, biodiversity and social benefits
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usability and accuracy, as well as conducting field tests to validate their effectiveness. Additionally, the research will explore the economic viability of these sensors to enhance real-time data collection
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due to a lack of resource. With some water-hungry sectors (such as data centres and other high-tech industries) prioritised for significant growth in water stressed regions, these challenges are set to
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function of urban blue spaces influence perceptions. It will subsequently explore and evaluate the types of information and knowledge required to improve the understanding and appreciation of urban blue