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Dull Disasters? How planning ahead will make a difference

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ISBN: 9780198785576 Year: Pages: 160 DOI: 10.1093/acprof:oso/9780198785576.001.0001 Language: English
Publisher: Oxford University Press Grant: World Bank Group
Subject: Economics --- Social and Public Welfare --- Social Sciences --- History --- Migration
Added to DOAB on : 2016-07-14 11:01:15
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Abstract

Economic losses from disasters are now reaching an average of US$250–$300 billion a year. In the last 20 years, more than 530,000 people died as a direct result of extreme weather events; millions more were seriously injured. Most of the deaths and serious injuries were in developing countries. Meanwhile, highly infectious diseases will continue to emerge or re-emerge, and natural hazards will not disappear. But these extreme events do not need to turn into large-scale disasters. Better and faster responses are possible. The authors contend that even though there is much generosity in the world to support the responses to and recovery from natural disasters, the current funding model, based on mobilizing financial resources after disasters take place, is flawed and makes responses late, fragmented, unreliable, and poorly targeted, while providing poor incentives for preparedness or risk reduction. The way forward centres around reforming the funding model for disasters, moving towards plans with simple rules for early action and that are locked in before disasters through credible funding strategies—all while resisting the allure of post-disaster discretionary funding and the threat it poses for those seeking to ensure that disasters have a less severe impact.

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-03-08 11:42:05
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Abstract

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water

Keywords

data scarce basins --- runoff series --- data forward prediction --- ensemble empirical mode decomposition (EEMD) --- stopping criteria --- method of tracking energy differences (MTED) --- deep learning --- convolutional neural networks --- superpixel --- urban water bodies --- high-resolution remote-sensing images --- monthly streamflow forecasting --- artificial neural network --- ensemble technique --- phase space reconstruction --- empirical wavelet transform --- hybrid neural network --- flood forecasting --- self-organizing map --- bat algorithm --- particle swarm optimization --- flood routing --- Muskingum model --- machine learning methods --- St. Venant equations --- rating curve method --- nonlinear Muskingum model --- hydrograph predictions --- flood routing --- Muskingum model --- hydrologic models --- improved bat algorithm --- Wilson flood --- Karahan flood --- flood susceptibility modeling --- ANFIS --- cultural algorithm --- bees algorithm --- invasive weed optimization --- Haraz watershed --- ANN-based models --- flood inundation map --- self-organizing map (SOM) --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- ensemble technique --- artificial neural networks --- uncertainty --- streamflow predictions --- sensitivity --- flood forecasting --- extreme learning machine (ELM) --- backtracking search optimization algorithm (BSA) --- the upper Yangtze River --- deep learning --- LSTM network --- water level forecast --- the Three Gorges Dam --- Dongting Lake --- Muskingum model --- wolf pack algorithm --- parameters --- optimization --- flood routing --- flash-flood --- precipitation-runoff --- forecasting --- lag analysis --- random forest --- machine learning --- flood prediction --- flood forecasting --- hydrologic model --- rainfall–runoff, hybrid & --- ensemble machine learning --- artificial neural network --- support vector machine --- natural hazards & --- disasters --- adaptive neuro-fuzzy inference system (ANFIS) --- decision tree --- survey --- classification and regression trees (CART), data science --- big data --- artificial intelligence --- soft computing --- extreme event management --- time series prediction --- LSTM --- rainfall-runoff --- flood events --- flood forecasting --- data assimilation --- particle filter algorithm --- micro-model --- Lower Yellow River --- ANN --- hydrometeorology --- flood forecasting --- real-time --- postprocessing --- machine learning --- early flood warning systems --- hydroinformatics --- database --- flood forecast --- Google Maps

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