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The book presents recent studies covering the aspects of challenges in predictive modelling and applications. Advanced numerical techniques for accurate and efficient real-time prediction and optimal management in coastal and hydraulic engineering are explored. For example, adaptive unstructured meshes are introduced to capture the important dynamics that operate over a range of length scales. Deep learning techniques enable rapid and accurate modelling simulations and pave the way towards both real-time forecasting and overall optimisation control over time, thus improving profitability and managing risk. The use of data assimilation techniques incorporates information from experiments and observations to reduce uncertainties in predictions and improve predictive accuracy. Targeted observation approaches can be used for identifying when, where, and what types of observations would provide the greatest improvement to specific model forecasts at a future time. Such targeted observations are important as they will allow the most effective use of available monitoring resources. The combination of deep learning and data assimilation enables a rapid and accurate response in emergencies. The technologies discussed here can be also used to determine the sensitivity of outputs to various operational conditions in engineering and management, thus providing reliable information to both the public and policy-makers

numerical modelling --- unstructured meshes --- finite volume --- North Sea --- salinity --- deep learning --- martinez boundary salinity generator --- Sacramento–San Joaquin Delta --- residence time --- exposure time --- transport time scale --- hyper-tidal estuary --- singular value decomposition --- data assimilation --- ocean models --- observation strategies --- ocean forecasting systems --- ocean Double Gyre --- 4D-Var --- ROMS --- MEOF --- initial ensemble --- ensemble spread --- LETKF --- n/a --- Sacramento-San Joaquin Delta

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Geophysical, environmental, and urban fluid flows (i.e., flows developing in oceans, seas, estuaries, rivers, aquifers, reservoirs, etc.) exhibit a wide range of reactive and transport processes. Therefore, identifying key phenomena, understanding their relative importance, and establishing causal relationships between them is no trivial task. Analysis of primitive variables (e.g., velocity components, pressure, temperature, concentration) is not always conducive to the most fruitful interpretations. Examining auxiliary variables introduced for diagnostic purposes is an option worth considering. In this respect, tracer and timescale methods are proving to be very effective. Such methods can help address questions such as, "where does a fluid-born dissolved or particulate substance come from and where will it go?" or, "how fast are the transport and reaction phenomena controlling the appearance and disappearance such substances?" These issues have been dealt with since the 19th century, essentially by means of ad hoc approaches. However, over the past three decades, methods resting on solid theoretical foundations have been developed, which permit the evaluation of tracer concentrations and diagnostic timescales (age, residence/exposure time, etc.) across space and time and using numerical models and field data. This book comprises research and review articles, introducing state-of-the-art diagnostic theories and their applications to domains ranging from shallow human-made reservoirs to lakes, river networks, marine domains, and subsurface flows

residence time --- Three Gorges Reservoir --- tributary bay --- density current --- water level regulation --- marina --- water renewal --- transport timescales --- return-flow --- macro-tidal --- wind influence --- floating structures --- San Francisco Estuary --- Sacramento–San Joaquin Delta --- water age --- transport time scales --- hydrodynamic model --- tidal hydrodynamics --- stable isotopes --- reactive tracers --- tailor-made tracer design --- hydrogeological tracer test --- kinetics --- partitioning --- Mahakam Delta --- age --- exposure time --- return coefficient --- CART --- source water fingerprinting --- floodplain --- turbulence --- ADCP measurement --- wave bias --- Reynolds stress --- transport process --- passive tracers --- terrestrial dissolved substances --- Pearl River Estuary --- shallow lake --- meteorological influence --- sub-basins --- Delft3D --- partial differential equations --- boundary conditions --- geophysical and environmental fluid flows --- reactive transport --- interpretation methods --- diagnostic timescales --- age distribution function --- radionuclide --- tracer --- data collection --- antimony 125 (125Sb) --- tritium (3H) --- dispersion --- modeling --- English Channel --- North Sea --- Biscay Bay --- timescale --- transport --- hydrodynamic --- ecological --- biogeochemical --- coastal --- estuary --- flushing time --- shallow reservoir --- numerical modeling --- Lagrangian transport modelling --- coupled wave–ocean models --- ocean drifters --- wave-induced processes --- model skills --- n/a --- Sacramento-San Joaquin Delta --- coupled wave-ocean models

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This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.

Technology: general issues --- History of engineering & technology --- truck dispatching --- mining equipment uncertainties --- orebody uncertainty --- discrete event simulation --- Q-learning --- grinding circuits --- minerals processing --- random forest --- decision trees --- machine learning --- knowledge discovery --- variable importance --- mineral prospectivity mapping --- random forest algorithm --- epithermal gold --- unstructured data --- blast impact --- empirical model --- mining --- fragmentation --- mine worker fatigue --- random forest model --- health and safety management --- stockpiles --- operational data --- mine-to-mill --- geostatistics --- ore control --- mine optimization --- digital twin --- modes of operation --- geological uncertainty --- multivariate statistics --- partial least squares regression --- oil sands --- bitumen extraction --- bitumen processability --- mine safety and health --- accidents --- narratives --- natural language processing --- random forest classification --- hyperspectral imaging --- multispectral imaging --- dimensionality reduction --- neighbourhood component analysis --- artificial intelligence --- mining exploitation --- masonry buildings --- damage risk analysis --- Bayesian network --- Naive Bayes --- Bayesian Network Structure Learning (BNSL) --- rock type --- mining geology --- bluetooth beacon --- classification and regression tree --- gaussian naïve bayes --- k-nearest neighbors --- support vector machine --- transport route --- transport time --- underground mine --- tactical geometallurgy --- data analytics in mining --- ball mill throughput --- measurement while drilling --- non-additivity --- coal --- petrographic analysis --- macerals --- image analysis --- semantic segmentation --- convolutional neural networks --- point cloud scaling --- fragmentation size analysis --- structure from motion --- n/a --- gaussian naïve bayes

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This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners.

Technology: general issues --- History of engineering & technology --- truck dispatching --- mining equipment uncertainties --- orebody uncertainty --- discrete event simulation --- Q-learning --- grinding circuits --- minerals processing --- random forest --- decision trees --- machine learning --- knowledge discovery --- variable importance --- mineral prospectivity mapping --- random forest algorithm --- epithermal gold --- unstructured data --- blast impact --- empirical model --- mining --- fragmentation --- mine worker fatigue --- random forest model --- health and safety management --- stockpiles --- operational data --- mine-to-mill --- geostatistics --- ore control --- mine optimization --- digital twin --- modes of operation --- geological uncertainty --- multivariate statistics --- partial least squares regression --- oil sands --- bitumen extraction --- bitumen processability --- mine safety and health --- accidents --- narratives --- natural language processing --- random forest classification --- hyperspectral imaging --- multispectral imaging --- dimensionality reduction --- neighbourhood component analysis --- artificial intelligence --- mining exploitation --- masonry buildings --- damage risk analysis --- Bayesian network --- Naive Bayes --- Bayesian Network Structure Learning (BNSL) --- rock type --- mining geology --- bluetooth beacon --- classification and regression tree --- gaussian naïve bayes --- k-nearest neighbors --- support vector machine --- transport route --- transport time --- underground mine --- tactical geometallurgy --- data analytics in mining --- ball mill throughput --- measurement while drilling --- non-additivity --- coal --- petrographic analysis --- macerals --- image analysis --- semantic segmentation --- convolutional neural networks --- point cloud scaling --- fragmentation size analysis --- structure from motion