Choose an application

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

Globally, fire regimes are being altered by changing climatic conditions and land use changes. This has the potential to drive species extinctions and cause ecosystem state changes, with a range of consequences for ecosystem services. Accurate prediction of the risk of forest fires over short timescales (weeks or months) is required for land managers to target suppression resources in order to protect people, property, and infrastructure, as well as fire-sensitive ecosystems. Over longer timescales, prediction of changes in forest fire regimes is required to model the effect of wildfires on the terrestrial carbon cycle and subsequent feedbacks into the climate system.This was the motivation to publish this book, which is focused on quantifying and modelling the risk factors of forest fires. More specifically, the chapters in this book address four topics: (i) the use of fire danger metrics and other approaches to understand variation in wildfire activity; (ii) understanding changes in the flammability of live fuel; (iii) modeling dead fuel moisture content; and (iv) estimations of emission factors.The book will be of broad relevance to scientists and managers working with fire in different forest ecosystems globally.

fire danger rating --- fire management --- fire regime --- fire size --- fire weather --- Portugal --- critical LFMC threshold --- forest/grassland fire --- radiative transfer model --- remote sensing --- southwest China --- acid rain --- aerosol --- biomass burning --- forest fire --- PM2.5 --- direct estimation --- meteorological factor regression --- moisture content --- time lag --- forest fire driving factors --- forest fire occurrence --- random forest --- forest fire management --- China --- Cupressus sempervirens --- fire risk --- fuels --- fuel moisture content --- mass loss calorimeter --- Seiridium cardinale --- vulnerability to wildfires --- disease --- alien pathogen --- allochthonous species --- introduced fungus --- drying tests --- humidity diffusion coefficients --- wildfire --- prescribed burning --- modeling --- drought --- flammability --- fuel moisture --- leaf water potential --- plant traits --- climate change --- MNI --- fire season --- fire behavior --- crown fire --- fire modeling --- senescence --- foliar moisture content --- canopy bulk density --- fire danger --- fire weather patterns --- RCP --- FWI system --- SSR --- occurrence of forest fire --- machine learning --- variable importance --- prediction accuracy --- epicormic resprouter --- eucalyptus --- fire severity --- flammability feedbacks --- temperate forest --- n/a

Choose an application

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

This Special Issue comprises 11 papers that outline the advances in research on various aspects of climate change impacts on hydrologic extremes, including both drivers (temperature, precipitation, and snow) and effects (peak flow, low flow, and water temperature). These studies cover a broad range of topics on hydrological extremes, including hydro-climatic controls, trends, homogeneity, nonstationarity, compound events and associated uncertainties, for both historical and future climates.

Research & information: general --- Geography --- regional flood frequency analysis --- flood-related attribute --- region of influence --- flood region revision process --- Canadian annual maximum flow --- extreme precipitation --- LARS-WG --- CMIP5 --- spatiotemporal changes --- climate change --- climatic controls --- multiple linear regression --- permafrost region --- streamflow extremes --- trend analysis --- variable importance analysis --- extreme events --- hydrology --- concurrent --- Colorado River basin --- heatwaves --- drought --- flooding --- low flows --- multi-purpose reservoir --- functional volume --- uncertainties --- Monte Carlo method --- hydrological extremes --- simulation-optimization model --- optimal storage volume --- simulation model --- retention volume --- transformation of flood discharges --- CMIP6 --- extreme --- SWAT --- flood --- IHA --- global warming --- Malaysia --- Kelantan --- peak flows --- predictor --- predictand --- snow water equivalent --- annual maximum flow --- western Canada --- uncertainty --- riverine flooding --- coastal flooding --- compound flooding --- projected IDF curves --- design storm --- Stephenville Crossing --- snow --- trends --- Yakima River basin --- cascade reservoirs --- design flood --- nonstationary conditions --- equivalent reliability --- most likely regional composition --- dependence structure --- glacier ablation --- North Cascade Range --- salmon --- glacier mass balance --- heat wave

Choose an application

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

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

Choose an application

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

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.

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

Choose an application

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

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