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Most scholars link the origin of politics to the formation of human societies, but in this innovative work, Tilo Schabert takes it even further back: to our very births. Drawing on mythical, philosophical, religious, and political thought from around the globe-including America, Europe, the Middle East, and China-The Second Birth proposes a transhistorical and transcultural theory of politics rooted in political cosmology. With impressive erudition, Schabert explores the physical fundamentals of political life, unveiling a profound new insight: our bodies actually teach us politics. Schabert traces different figurations of power inherent to our singular existence, things such as numbers, time, thought, and desire, showing how they render our lives political ones-and, thus, how politics exists in us individually, long before it plays a role in the establishment of societies and institutions. Through these figurations of power, Schabert argues, we learn how to institute our own government within the political forces that already surround us-to create our own world within the one into which we have been born. In a stunning vision of human agency, this book ultimately sketches a political cosmos in which we are all builders, in which we can be at once political and free.

Political science --- Civilization. --- Anthropological aspects. --- Philosophy. --- human, history, historical, academic, scholarly, politics, political, poli sci, philosophy, philosophical, theory, theoretical, society, social studies, birth, mythical, mythology, religious, religion, faith, belief, global, international, america, europe, middle east, eastern, western, china, asia, transcultural, culture, government, power.

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The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

Research & information: general --- water resources management --- landslide --- dammed lake --- flood risk --- time-varying parameter --- GR4J model --- changing environments --- temporal transferability --- western China --- cascade hydropower reservoirs --- multi-objective optimization --- TOPSIS --- gravitational search algorithm --- opposition learning --- partial mutation --- elastic-ball modification --- Snowmelt Runoff Model --- parameter uncertainty --- data-scarce deglaciating river basin --- climate change impacts --- generalized likelihood uncertainty estimation --- Yangtze River --- cascade reservoirs --- impoundment operation --- GloFAS-Seasonal --- forecast evaluation --- small and medium-scale rivers --- highly urbanized area --- flood control --- whole region perspective --- coupled models --- flood-risk map --- hydrodynamic modelling --- Sequential Gaussian Simulation --- urban stormwater --- probabilistic forecast --- Unscented Kalman Filter --- artificial neural networks --- Three Gorges Reservoir --- Mahalanobis-Taguchi System --- grey entropy method --- signal-to-noise ratio --- degree of balance and approach --- interval number --- multi-objective optimal operation model --- feasible search space --- Pareto-front optimal solution set --- loss–benefit ratio of ecology and power generation --- elasticity coefficient --- empirical mode decomposition --- Hushan reservoir --- data synthesis --- urban hydrological model --- Generalized Likelihood Uncertainty Estimation (GLUE) --- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) --- uncertainty analysis --- NDVI --- Yarlung Zangbo River --- machine learning model --- random forest --- Internet of Things (IoT) --- regional flood inundation depth --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- artificial intelligence --- machine learning --- multi-objective reservoir operation --- hydrologic forecasting --- uncertainty --- risk

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.

water resources management --- landslide --- dammed lake --- flood risk --- time-varying parameter --- GR4J model --- changing environments --- temporal transferability --- western China --- cascade hydropower reservoirs --- multi-objective optimization --- TOPSIS --- gravitational search algorithm --- opposition learning --- partial mutation --- elastic-ball modification --- Snowmelt Runoff Model --- parameter uncertainty --- data-scarce deglaciating river basin --- climate change impacts --- generalized likelihood uncertainty estimation --- Yangtze River --- cascade reservoirs --- impoundment operation --- GloFAS-Seasonal --- forecast evaluation --- small and medium-scale rivers --- highly urbanized area --- flood control --- whole region perspective --- coupled models --- flood-risk map --- hydrodynamic modelling --- Sequential Gaussian Simulation --- urban stormwater --- probabilistic forecast --- Unscented Kalman Filter --- artificial neural networks --- Three Gorges Reservoir --- Mahalanobis-Taguchi System --- grey entropy method --- signal-to-noise ratio --- degree of balance and approach --- interval number --- multi-objective optimal operation model --- feasible search space --- Pareto-front optimal solution set --- loss–benefit ratio of ecology and power generation --- elasticity coefficient --- empirical mode decomposition --- Hushan reservoir --- data synthesis --- urban hydrological model --- Generalized Likelihood Uncertainty Estimation (GLUE) --- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) --- uncertainty analysis --- NDVI --- Yarlung Zangbo River --- machine learning model --- random forest --- Internet of Things (IoT) --- regional flood inundation depth --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- artificial intelligence --- machine learning --- multi-objective reservoir operation --- hydrologic forecasting --- uncertainty --- risk