Search results: Found 3

Listing 1 - 3 of 3
Sort by
Volcanic Unrest

Authors: --- ---
Book Series: Advances in Volcanology ISBN: 9783319584126 Year: Pages: 313 DOI: 10.1007/978-3-319-58412-6 Language: English
Publisher: Springer Nature
Subject: Political Science --- Law --- Geology --- Earth Sciences --- General and Civil Engineering
Added to DOAB on : 2020-02-04 11:21:18
License:

Loading...
Export citation

Choose an application

Abstract

This open access book summarizes the findings of the VUELCO project, a multi-disciplinary and cross-boundary research funded by the European Commission's 7th framework program. It comprises four broad topics: 1. The global significance of volcanic unrest 2. Geophysical and geochemical fingerprints of unrest and precursory activity 3. Magma dynamics leading to unrest phenomena 4. Bridging the gap between science and decision-making Volcanic unrest is a complex multi-hazard phenomenon. The fact that unrest may, or may not lead to an imminent eruption contributes significant uncertainty to short-term volcanic hazard and risk assessment. Although it is reasonable to assume that all eruptions are associated with precursory activity of some sort, the understanding of the causative links between subsurface processes, resulting unrest signals and imminent eruption is incomplete. When a volcano evolves from dormancy into a phase of unrest, important scientific, political and social questions need to be addressed. This book is aimed at graduate students, researchers of volcanic phenomena, professionals in volcanic hazard and risk assessment, observatory personnel, as well as emergency managers who wish to learn about the complex nature of volcanic unrest and how to utilize new findings to deal with unrest phenomena at scientific and emergency managing levels. This book is open access under a CC BY license. ;

Nature-Based Flood Risk Management on Private Land

Authors: --- ---
ISBN: 9783030238421 Year: Pages: 228 DOI: 10.1007/978-3-030-23842-1 Language: English
Publisher: Springer Nature
Subject: Environmental Sciences --- General and Civil Engineering --- Geology --- Earth Sciences
Added to DOAB on : 2020-02-05 11:21:07
License:

Loading...
Export citation

Choose an application

Abstract

This open access book addresses the various disciplinary aspects of nature-based solutions in flood risk management on private land. In recent decades, water management has been moving towards nature-based solutions. These are assumed to be much more multi-purpose than traditional “grey infrastructures” and seem to be regarded as a panacea for many environmental issues. At the same time, such measures require more – and mostly privately owned – land and more diverse stakeholder involvement than traditional (grey) engineering approaches. They also present challenges related to different disciplines. Nature-based solutions for flood risk management not only require technical expertise, but also call for interdisciplinary insights from land-use planning, economics, property rights, sociology, landscape planning, ecology, hydrology, agriculture and other disciplines to address the challenges of implementing them. Ultimately, nature-based flood risk management is a multi-disciplinary endeavor. Featuring numerous case studies of nature-based flood risk management accompanied by commentaries, this book presents brief academic reflections from two different disciplinary perspectives that critically highlight which specific aspects are of significance, and as such, underscore the multi-disciplinary nature of the challenges faced. ;

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: English
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
License:

Loading...
Export citation

Choose an application

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

Listing 1 - 3 of 3
Sort by
Narrow your search

Publisher

Springer Nature (2)

MDPI - Multidisciplinary Digital Publishing Institute (1)


License

CC by (2)

CC by-nc-nd (1)


Language

english (3)


Year
From To Submit

2019 (3)