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In recent years, typhoons have struck the Philippines and Vanuatu; earthquakes have rocked Haiti, Pakistan, and Nepal; floods have swept through Pakistan and Mozambique; droughts have hit Ethiopia, Kenya, and Somalia; and more. All led to loss of life and loss of livelihoods, and recovery will take years. One of the likely effects of climate change is to increase the likelihood of the type of extreme weather events that seems to cause these disasters. But do extreme events have to turn into disasters with huge loss of life and suffering? 'Dull Disasters?' harnesses lessons from finance, political science, economics, psychology, and the natural sciences to show how countries and their partners can be far better prepared to deal with disasters.
Emergency management. --- Crisis management. --- Organizational resilience. --- Disaster victims. --- Victims of disasters --- Resilience of organizations --- Crises --- Management of crises --- Consequence management (Emergency management) --- Disaster planning --- Disaster preparedness --- Disaster prevention --- Disaster relief --- Disasters --- Emergencies --- Emergency planning --- Emergency preparedness --- Management --- Planning --- Preparedness --- Prevention --- Victims --- Organizational change --- Problem solving --- Conflict management --- Public safety --- First responders --- Emergency management --- extreme event --- time inconsistency --- natural disaster --- disaster risk finance --- pandemic --- planning --- behavioural psychology --- commitment device --- politics of disaster relief --- Decision-making --- Insurance --- Reinsurance --- Risk management
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This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water
natural hazards & --- artificial neural network --- flood routing --- the Three Gorges Dam --- backtracking search optimization algorithm (BSA) --- lag analysis --- artificial intelligence --- classification and regression trees (CART) --- decision tree --- real-time --- optimization --- ensemble empirical mode decomposition (EEMD) --- improved bat algorithm --- convolutional neural networks --- ANFIS --- method of tracking energy differences (MTED) --- adaptive neuro-fuzzy inference system (ANFIS) --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- disasters --- flood prediction --- ANN-based models --- flood inundation map --- ensemble machine learning --- flood forecast --- sensitivity --- hydrologic models --- phase space reconstruction --- water level forecast --- data forward prediction --- early flood warning systems --- bees algorithm --- random forest --- uncertainty --- soft computing --- data science --- hydrometeorology --- LSTM --- rating curve method --- forecasting --- superpixel --- particle swarm optimization --- high-resolution remote-sensing images --- machine learning --- support vector machine --- Lower Yellow River --- extreme event management --- runoff series --- empirical wavelet transform --- Muskingum model --- hydrograph predictions --- bat algorithm --- data scarce basins --- Wilson flood --- self-organizing map --- big data --- extreme learning machine (ELM) --- hydroinformatics --- nonlinear Muskingum model --- invasive weed optimization --- rainfall–runoff --- flood forecasting --- artificial neural networks --- flash-flood --- streamflow predictions --- precipitation-runoff --- the upper Yangtze River --- survey --- parameters --- Haraz watershed --- ANN --- time series prediction --- postprocessing --- flood susceptibility modeling --- rainfall-runoff --- deep learning --- database --- LSTM network --- ensemble technique --- hybrid neural network --- self-organizing map (SOM) --- data assimilation --- particle filter algorithm --- monthly streamflow forecasting --- Dongting Lake --- machine learning methods --- micro-model --- stopping criteria --- Google Maps --- cultural algorithm --- wolf pack algorithm --- flood events --- urban water bodies --- Karahan flood --- St. Venant equations --- hybrid & --- hydrologic model
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