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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

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ISBN: 9783039212156 / 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

Remote Sensing of Precipitation: Volume 1

Author:
ISBN: 9783039212859 / 9783039212866 Year: Pages: 480 DOI: 10.3390/books978-3-03921-286-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.

Keywords

GPM --- IMERG --- satellite precipitation adjustment --- numerical weather prediction --- heavy precipitation --- flood-inducing storm --- complex terrain --- precipitation --- geostationary microwave sensors --- polar systems --- synoptic weather types --- drop size distribution (DSD) --- microstructure of rain --- disdrometer --- radar reflectivity–rain rate relationship --- CHIRPS --- CMORPH --- TMPA --- MSWEP --- statistical evaluation --- VIC model --- hydrological simulation --- precipitation --- satellite --- GPM --- TRMM --- CFSR --- PERSIANN --- MSWEP --- streamflow simulation --- lumped models --- Peninsular Spain --- GPM IMERG v5 --- TRMM 3B42 v7 --- precipitation --- evaluation --- Huaihe River basin --- precipitation --- radar --- radiometer --- T-Matrix --- microwave scattering --- quantitative precipitation estimates --- validation --- PERSIANN-CCS --- meteorological radar --- satellite rainfall estimates --- satellite precipitation retrieval --- neural networks --- GPM --- GMI --- remote sensing --- hurricane Harvey --- GPM satellite --- IMERG --- tropical storm rainfall --- gridded radar precipitation --- precipitation --- satellites --- climate models --- regional climate models --- X-band radar --- dual-polarization --- precipitation --- complex terrain --- runoff simulations --- snowfall detection --- snow water path retrieval --- supercooled droplets detection --- GPM Microwave Imager --- Satellite Precipitation Estimates --- GPM --- TRMM --- IMERG --- GSMaP --- TMPA --- CMORPH --- assessment --- Pakistan --- heavy rainfall prediction --- satellite radiance --- data assimilation --- RMAPS --- harmonie model --- radar data assimilation --- pre-processing --- mesoscale precipitation patterns --- GNSS meteorology --- GPS --- Zenith Tropospheric Delay --- precipitable water vapor --- SEID --- single frequency GNSS --- Precise Point Positioning --- low-cost receivers --- goGPS --- GPM --- IMERG --- TRMM --- precipitation --- Cyprus --- satellite precipitation product --- Tianshan Mountains --- GPM --- TRMM --- CMORPH --- heavy precipitation --- rainfall retrieval techniques --- forecast model --- Red–Thai Binh River Basin --- TMPA 3B42V7 --- TMPA 3B42RT --- rainfall --- bias correction --- linear-scaling approach --- climatology --- topography --- precipitation --- remote sensing --- CloudSat --- CMIP --- high latitude --- mineral dust --- wet deposition --- cloud scavenging --- dust washout process --- Saharan dust transportation --- precipitation rate --- precipitating hydrometeor --- hydrometeor classification --- cloud radar --- Ka-band --- thunderstorm --- thundercloud --- vertical air velocity --- terminal velocity --- Milešovka observatory --- rain gauges --- radar --- quality indexes --- satellite rainfall retrievals --- validation --- surface rain intensity --- kriging with external drift --- PEMW --- MSG --- SEVIRI --- downscaling --- tropical cyclone --- rain rate --- precipitation --- remote sensing --- radiometer --- retrieval algorithm --- GPM --- DPR --- validation network --- volume matching --- reflectivity --- rainfall rate --- TRMM-era TMPA --- GPM-era IMERG --- satellite rainfall estimate --- Mainland China --- satellite precipitation --- Global Precipitation Measurement (GPM) --- IMERG --- TRMM-TMPA --- Ensemble Precipitation (EP) algorithm --- topographical and seasonal evaluation --- daily rainfall estimations --- TRMM 3B42 v7 --- rain gauges --- Amazon Basin --- regional rainfall regimes --- regional rainfall sub-regimes --- TRMM 3B42 V7 --- CMORPH_CRT --- PERSIANN_CDR --- GR models --- hydrological simulation --- Red River Basin --- satellite precipitation --- Tibetan Plateau --- GPM --- IMERG --- GSMaP --- precipitation --- weather --- radar --- GPM --- RADOLAN --- QPE --- TRMM --- TMPA --- 3B42 --- validation --- rainfall --- telemetric rain gauge --- Lai Nullah --- Pakistan --- XPOL radar --- GPM/IMERG --- WRF-Hydro --- CHAOS --- hydrometeorology --- flash flood --- Mandra --- typhoon --- IMERG --- GSMaP --- Southern China --- precipitation --- satellite remote sensing --- error analysis --- triple collocation --- precipitation --- TRMM --- GPM --- IMERG --- weather radar --- precipitable water vapor --- precipitation retrieval --- rain rate --- QPE

Remote Sensing of Precipitation: Volume 2

Author:
ISBN: 9783039212873 / 9783039212880 Year: Pages: 318 DOI: 10.3390/books978-3-03921-288-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-08-28 11:21:27
License:

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Abstract

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.

Keywords

GPM --- IMERG --- satellite precipitation adjustment --- numerical weather prediction --- heavy precipitation --- flood-inducing storm --- complex terrain --- precipitation --- geostationary microwave sensors --- polar systems --- synoptic weather types --- drop size distribution (DSD) --- microstructure of rain --- disdrometer --- radar reflectivity–rain rate relationship --- CHIRPS --- CMORPH --- TMPA --- MSWEP --- statistical evaluation --- VIC model --- hydrological simulation --- precipitation --- satellite --- GPM --- TRMM --- CFSR --- PERSIANN --- MSWEP --- streamflow simulation --- lumped models --- Peninsular Spain --- GPM IMERG v5 --- TRMM 3B42 v7 --- precipitation --- evaluation --- Huaihe River basin --- precipitation --- radar --- radiometer --- T-Matrix --- microwave scattering --- quantitative precipitation estimates --- validation --- PERSIANN-CCS --- meteorological radar --- satellite rainfall estimates --- satellite precipitation retrieval --- neural networks --- GPM --- GMI --- remote sensing --- hurricane Harvey --- GPM satellite --- IMERG --- tropical storm rainfall --- gridded radar precipitation --- precipitation --- satellites --- climate models --- regional climate models --- X-band radar --- dual-polarization --- precipitation --- complex terrain --- runoff simulations --- snowfall detection --- snow water path retrieval --- supercooled droplets detection --- GPM Microwave Imager --- Satellite Precipitation Estimates --- GPM --- TRMM --- IMERG --- GSMaP --- TMPA --- CMORPH --- assessment --- Pakistan --- heavy rainfall prediction --- satellite radiance --- data assimilation --- RMAPS --- harmonie model --- radar data assimilation --- pre-processing --- mesoscale precipitation patterns --- GNSS meteorology --- GPS --- Zenith Tropospheric Delay --- precipitable water vapor --- SEID --- single frequency GNSS --- Precise Point Positioning --- low-cost receivers --- goGPS --- GPM --- IMERG --- TRMM --- precipitation --- Cyprus --- satellite precipitation product --- Tianshan Mountains --- GPM --- TRMM --- CMORPH --- heavy precipitation --- rainfall retrieval techniques --- forecast model --- Red–Thai Binh River Basin --- TMPA 3B42V7 --- TMPA 3B42RT --- rainfall --- bias correction --- linear-scaling approach --- climatology --- topography --- precipitation --- remote sensing --- CloudSat --- CMIP --- high latitude --- mineral dust --- wet deposition --- cloud scavenging --- dust washout process --- Saharan dust transportation --- precipitation rate --- precipitating hydrometeor --- hydrometeor classification --- cloud radar --- Ka-band --- thunderstorm --- thundercloud --- vertical air velocity --- terminal velocity --- Milešovka observatory --- rain gauges --- radar --- quality indexes --- satellite rainfall retrievals --- validation --- surface rain intensity --- kriging with external drift --- PEMW --- MSG --- SEVIRI --- downscaling --- tropical cyclone --- rain rate --- precipitation --- remote sensing --- radiometer --- retrieval algorithm --- GPM --- DPR --- validation network --- volume matching --- reflectivity --- rainfall rate --- TRMM-era TMPA --- GPM-era IMERG --- satellite rainfall estimate --- Mainland China --- satellite precipitation --- Global Precipitation Measurement (GPM) --- IMERG --- TRMM-TMPA --- Ensemble Precipitation (EP) algorithm --- topographical and seasonal evaluation --- daily rainfall estimations --- TRMM 3B42 v7 --- rain gauges --- Amazon Basin --- regional rainfall regimes --- regional rainfall sub-regimes --- TRMM 3B42 V7 --- CMORPH_CRT --- PERSIANN_CDR --- GR models --- hydrological simulation --- Red River Basin --- satellite precipitation --- Tibetan Plateau --- GPM --- IMERG --- GSMaP --- precipitation --- weather --- radar --- GPM --- RADOLAN --- QPE --- TRMM --- TMPA --- 3B42 --- validation --- rainfall --- telemetric rain gauge --- Lai Nullah --- Pakistan --- XPOL radar --- GPM/IMERG --- WRF-Hydro --- CHAOS --- hydrometeorology --- flash flood --- Mandra --- typhoon --- IMERG --- GSMaP --- Southern China --- precipitation --- satellite remote sensing --- error analysis --- triple collocation --- precipitation --- TRMM --- GPM --- IMERG --- weather radar --- precipitable water vapor --- precipitation retrieval --- rain rate --- QPE

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CC by-nc-nd (3)


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eng (3)


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2019 (3)