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Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia

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ISBN: 9783039212354 / 9783039212361 Year: Pages: 384 DOI: 10.3390/books978-3-03921-236-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

To promote scientific understanding of surface processes in East Asia, we have published details of the CMADS dataset in the journal, Water, and expect that users around the world will learn about CMADS datasets while promoting the development of hydrometeorological disciplines in East Asia. We hope and firmly believe that scientific development in East Asia and our understanding of this typical region will be further advanced.

Keywords

East Asia --- CMADS --- meteorological input uncertainty --- hydrological modelling --- SWAT --- non-point source pollution models --- CMADS --- Qinghai-Tibet Plateau (TP) --- SWAT --- CFSR --- TRMM --- PERSIANN --- PERSIANN-CDR --- CMADS --- satellite-derived rainfall --- streamflow simulation --- SWAT --- Han River --- GLUE --- hydrological model --- ParaSol --- SUFI2 --- uncertainty analysis --- SWAT model --- CMADS --- Lijiang River --- runoff --- uncertainty analysis --- hydrological elements --- statistical analysis --- SWAT --- CMADS --- climate variability --- land use change --- streamflow --- potential evapotranspiration --- Penman-Monteith --- CMADS --- China --- CMADS dataset --- parameter sensitivity --- SUFI-2 --- Yellow River --- reanalysis products --- satellite-based products --- hydrological model --- bayesian model averaging --- Xiang River basin --- total nitrogen --- accumulation --- SWAT model --- CMADS --- Biliuhe reservoir --- CMADS --- SWAT --- East Asia --- meteorological --- hydrological --- precipitation --- TMPA-3B42V7 --- CMADS --- hydrologic model --- uncertainty --- reservoirs --- operation rule --- Noah LSM-HMS --- capacity distribution --- aggregated reservoir --- CMADS --- CMADS --- IMERG --- statistical analysis --- SWAT hydrological simulation --- Jinsha River Basin --- blue and green water flows --- climate variability --- sensitivity analysis --- Erhai Lake Basin --- CMADS --- SWAT --- JBR --- soil moisture --- hydrological processes --- spatio-temporal --- sloping black soil farmland --- soil moisture content --- freeze–thaw period --- soil temperature --- CMADS-ST --- reservoir parameters --- runoff --- CMADS --- SWAT --- Yalong River --- CMADS --- impact --- hydrological modeling --- SWAT --- runoff --- sediment yield --- land-use change --- SWAT --- CMADS

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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MDPI - Multidisciplinary Digital Publishing Institute (2)


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CC by-nc-nd (2)


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eng (2)


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2019 (2)