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Environmental monitoring --- Environmental geography --- Geographic information systems

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Computational intelligence techniques have enjoyed growing interest in recent decades among the earth and environmental science research communities for their powerful ability to solve and understand various complex problems and develop novel approaches toward a sustainable earth. This book compiles a collection of recent developments and rigorous applications of computational intelligence in these disciplines. Techniques covered are divided into three categories - classical intelligence techniques, probabilistic and transforms intelligence techniques, and hybrid intelligence techniques. Further topics given treatment in this volume include meteorology, atmospheric modeling, climate change, water resources engineering, and hydrological modeling. By linking computational intelligence techniques with earth and environmental science oriented problems, this book promotes synergistic activities among scientists and technicians working in areas such as data mining and machine learning. We believe that a diverse group of academics, scientists, environmentalists, meteorologists, and computing experts with a common interest in computational intelligence techniques within the earth and environmental sciences will find this book to be of great value.

Computational intelligence. --- Environmental economics. --- Environmental sciences. --- Evolutionary computation. --- Earth sciences --- Environmental sciences --- Computational intelligence --- Earth & Environmental Sciences --- Geology --- Geology - General --- Environmental Sciences --- Data processing --- Data processing. --- Intelligence, Computational --- Environment. --- Physical geography. --- Climate change. --- Environment, general. --- Earth System Sciences. --- Climate Change. --- Artificial intelligence --- Soft computing --- Climatic changes. --- Changes, Climatic --- Changes in climate --- Climate change --- Climate change science --- Climate changes --- Climate variations --- Climatic change --- Climatic changes --- Climatic fluctuations --- Climatic variations --- Global climate changes --- Global climatic changes --- Climatology --- Climate change mitigation --- Teleconnections (Climatology) --- Environmental science --- Science --- Environmental aspects --- Geography --- Global environmental change --- Balance of nature --- Biology --- Bionomics --- Ecological processes --- Ecological science --- Ecological sciences --- Environment --- Environmental biology --- Oecology --- Population biology --- Ecology

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Computational intelligence techniques have enjoyed growing interest in recent decades among the earth and environmental science research communities for their powerful ability to solve and understand various complex problems and develop novel approaches toward a sustainable earth. This book compiles a collection of recent developments and rigorous applications of computational intelligence in these disciplines. Techniques covered are divided into three categories - classical intelligence techniques, probabilistic and transforms intelligence techniques, and hybrid intelligence techniques. Further topics given treatment in this volume include meteorology, atmospheric modeling, climate change, water resources engineering, and hydrological modeling. By linking computational intelligence techniques with earth and environmental science oriented problems, this book promotes synergistic activities among scientists and technicians working in areas such as data mining and machine learning. We believe that a diverse group of academics, scientists, environmentalists, meteorologists, and computing experts with a common interest in computational intelligence techniques within the earth and environmental sciences will find this book to be of great value.

Meteorology. Climatology --- Geology. Earth sciences --- Environmental protection. Environmental technology --- milieukunde --- machine learning --- milieu --- duurzame ontwikkeling --- geografie --- milieubeheer --- klimaatverandering

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Throughout the history of philosophy, there have been many espousals of the thesis that logic is normative for reasoning. For example, Kant declares the sollen claim that logic concerns how we ought (sollen) to think. Similarly, Frege also maintains that logic is normative for thinking based on the descriptive adequacy of logical laws and their constitutivity of thoughts. Carnap also adopts a normative conception of logic. Following the same line of reasoning, given the development of inductive logic, it is normative for an agent to reason according to the logical laws of the inductive logical system. However, the view that logic has normative force on reasoning meets contemporary adversaries such as Gilbert Harman, John Broome, and Gillian Russell. Harman's objections rest on his pessimistic attitudes towards the plausibility of inductive logic and his moral relativism. Broome's objection points out the failure of one possible bridge principle. Russell's objections are based on her reliance on fuzzy predicates and on a static hierarchy of degrees of normativity of different theories. In this thesis, the main goal is to argue against the objections and to re-establish the normativity of logic. As a reply to Harman, we present historical inductive logical systems developed by Carnap, Williamson, and nonmonotonic logics developed in AI. We also try to reconceptualise the process of inductive inference and promote an inductive logical system qua nonmonotonic probabilistic logic. We adopt a moral objectivism view which is compatible with our views. As for the reply to Broome, we argue that a different formalisation of the bridge principle can resolve Broome's puzzle. And to Russell, we reply that the static hierarchy is not sustainable in view of some counterexamples, and her rejection of the binary view towards predicates hasn't been well-defended. The upshot is that inductive logic is normative for reasoning.