Probabilistic graphical models (or graphical models for short) allow systems and businesses to address these challenges in a unified framework. This framework will help researchers and practitioners take advantage of the upcoming windfall in infrastructure investment to promote wise, efficient, and effective research and development in natural infrastructure for the coming decades.Many real-world problems in artificial intelligence, computer vision, robotics, computer systems, computational biology, and natural language processing require systems to reason about highly uncertain, structured or unstructured data, and draw global insights from local observations. This is intended to work across disciplines so that ecological, engineering, and social outcomes can be simultaneously addressed, and focuses on scientifically rigorous monitoring for learning and evidence-based conservation. We present a strategic framework for designing monitoring for the next generation of natural infrastructure and NBS projects. However, the interdisciplinary nature of such projects, and the long time scales at which ecological variables operate, pose a challenge for conventional infrastructure monitoring approaches. Efficient and scientifically sound monitoring of ecological and engineering parameters are essential for "proving" NBS and natural infrastructure projects, and for refining them across time. As the planning and implementation of nature-based solutions projects accelerates with massive infrastructure spending worldwide, the global community runs the risk of missing key opportunities for scientific learning and evidence-based synthesis on the efficacy of these promising methods. Their capacity to create "win-win" situations for humans and the environment is justifiably cause for much excitement in the field of environmental sustainability, but empirical evidence is urgently needed to support current approaches and improve future applications. Nature-based solutions (NBS) and Natural Infrastructure offer a powerful tool for simultaneously addressing global infrastructure needs while potentially restoring and maintaining important ecosystems. The knowledge centre will be complimented by a glossary of terms and a library of recommended scientific reading (a glossary of freshwater terms is already available on the Freshwater Information Platform). Content-wise this will include for example the presentation of best practice restoration projects, technical guidance, the development of regional scalability plans, large-scale upscaling strategies, Cost-Benefit-Analysis, interactive maps on benefits of restoration measures, community and sector involvement as well as institutional arrangement strategies.Īdditionally, the MERLIN Academy will host a knowledge centre that will include short videos and animations, manuals and state-of-the-art guidance documents related to for example spatial configuration of restoration measures within a catchment, benefits of different restoration types on ecosystem services and biodiversity, design standards for certain nature-based solution restoration methods, synergies of nature-based solutions/restoration with various sectors, a governance framework for successful restoration as well as financing strategies for restoration on different spatial scales or sector-specific strategies for restoration synergies. Technically, the courses of the MERLIN Academy will include live webinars, online training workshops, recorded e-learning sessions (webinars or presentations) accessible for users at any time.
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