نوع المادة: كتاب

الموضوعات

valve; pump; real time control; pressure; water distribution modelling; leakage; non-revenue water; multiple regression analysis; artificial neural network; water distribution network; water distribution network; graph partitioning; modularity; district metered areas; water distribution network management; complex network theory; topological analysis; mathematical model; valve; pressure; leakage; optimization; water distribution network; snapshot simulation; pressure-driven; energy recovery systems; runaway conditions; unsteady flow; water hammer; water distribution system; water quality; disinfection by-products; vulnerability; water quality (WQ); blending; release of heavy metals (HMR); dual response surface optimization (DRSO); multiple source waters blending optimization (MSWBO); intermittent water supply; water service quality; 24

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038975564

ملف خارجي

نوع المادة: كتاب

الموضوعات

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

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038975489

ملف خارجي

نوع المادة: كتاب

الموضوعات

evapotranspiration; Northeast China; MS–PT algorithm; spatial-temporal variations; controlling factors; potential evapotranspiration; vegetation remote sensing; reflectance model; spectra; leaf; copper; PROSPECT; leaf area density; terrestrial LiDAR; tree canopy; vertical structure; voxel; spatial representativeness; heterogeneity; validation; land-surface temperature products (LSTs); observations; HiWATER; remote sensing; spatiotemporal representative; cost-efficient, sampling design; heterogeneity; validation; FY-3C/MERSI; GLASS; Land surface temperature; Land surface emissivity; GPP; SIF; MuSyQ-GPP algorithm; BEPS; vegetation phenology; Tibetan Plateau; MODIS; NDVI; start of growing season (SOS); end of growing season (EOS); GLASS LAI time series; forest disturbance; disturbance index; latent heat; machine learning algorithms; plant functional type; high-resolution freeze/thaw; AMSR2; MODIS; LAI; ZY-3 MUX; GF-1 WFV; HJ-1 CCD; maize; PROSPECT-5B+SAILH (PROSAIL) model; spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER; spectral; albedometer; interference filter; photoelectric detector; validation; land surface albedo; multi-scale validation; rugged terrain; MRT-based model; MCD43A3 C6; precipitation; statistics methods; China; Tibetan Plateau; South China’s; drought; SPI; TMI data; crop-growing regions; downward shortwave radiation; machine learning; gradient boosting regression tree; AVHRR; CMA; BRDF; aerosol; MODIS; sunphotometer; arid/semiarid; solar-induced chlorophyll fluorescence; fluorescence quantum efficiency in dark-adapted conditions (FQE); SCOPE; Fraunhofer Line Discrimination (FLD); gross primary productivity (GPP); longwave upwelling radiation (LWUP); Visible Infrared Imaging Radiometer Suite (VIIRS); surface radiation budget; hybrid method; remote sensing; leaf age; leaf spectral properties; leaf area index; Cunninghamia; Chinese fir; canopy reflectance; NIR; EVI2; geometric optical radiative transfer (GORT) model; land surface albedo; snow-free albedo; rugged terrain; topographic effects; black-sky albedo (BSA); GPP; NPP; MODIS; validation; phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree; forest canopy height; aboveground biomass; ICESat GLAS; Landsat; random forest model; anisotropic reflectance; BRDF; rugged terrain; solo slope; composite slope; surface solar irradiance; geostationary satellite; polar orbiting satellite; LUT method; SURFRAD; downward shortwave radiation; daily average value; Antarctica; sinusoidal method; cloud fraction; interpolation; boreal forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters; multisource data fusion; aerosol retrieval; urban scale; vegetation dust-retention; multiple ecological factors; geographical detector model; snow cover; passive microwave; FY-3C/MWRI; algorithmic assessment; China; land surface temperature; satellite observations; flux measurements; latitudinal pattern; land cover change; fractional vegetation cover (FVC); multi-data set; northern China; spatio-temporal; inter-annual variation; uncertainty; standard error of the mean; downscaling; GPP; spatial heterogeneity; remote sensing; subpixel information; LiDAR; point cloud; leaf; gap fraction; 3D reconstruction; biodiversity; remote sensing; species richness; metric comparison; metric integration; leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing; vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles; n/a; geometric-optical model; thermal radiation directionality; quantitative remote sensing inversion; scale effects; comprehensive field experiment

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972709

ملف خارجي

نوع المادة: كتاب

الموضوعات

evapotranspiration; Northeast China; MS–PT algorithm; spatial-temporal variations; controlling factors; potential evapotranspiration; vegetation remote sensing; reflectance model; spectra; leaf; copper; PROSPECT; leaf area density; terrestrial LiDAR; tree canopy; vertical structure; voxel; spatial representativeness; heterogeneity; validation; land-surface temperature products (LSTs); observations; HiWATER; remote sensing; spatiotemporal representative; cost-efficient, sampling design; heterogeneity; validation; FY-3C/MERSI; GLASS; Land surface temperature; Land surface emissivity; GPP; SIF; MuSyQ-GPP algorithm; BEPS; vegetation phenology; Tibetan Plateau; MODIS; NDVI; start of growing season (SOS); end of growing season (EOS); GLASS LAI time series; forest disturbance; disturbance index; latent heat; machine learning algorithms; plant functional type; high-resolution freeze/thaw; AMSR2; MODIS; LAI; ZY-3 MUX; GF-1 WFV; HJ-1 CCD; maize; PROSPECT-5B+SAILH (PROSAIL) model; spatial heterogeneity; variability; evapotranspiration; land surface variables; probability density function; HiWATER; spectral; albedometer; interference filter; photoelectric detector; validation; land surface albedo; multi-scale validation; rugged terrain; MRT-based model; MCD43A3 C6; precipitation; statistics methods; China; Tibetan Plateau; South China’s; drought; SPI; TMI data; crop-growing regions; downward shortwave radiation; machine learning; gradient boosting regression tree; AVHRR; CMA; BRDF; aerosol; MODIS; sunphotometer; arid/semiarid; solar-induced chlorophyll fluorescence; fluorescence quantum efficiency in dark-adapted conditions (FQE); SCOPE; Fraunhofer Line Discrimination (FLD); gross primary productivity (GPP); longwave upwelling radiation (LWUP); Visible Infrared Imaging Radiometer Suite (VIIRS); surface radiation budget; hybrid method; remote sensing; leaf age; leaf spectral properties; leaf area index; Cunninghamia; Chinese fir; canopy reflectance; NIR; EVI2; geometric optical radiative transfer (GORT) model; land surface albedo; snow-free albedo; rugged terrain; topographic effects; black-sky albedo (BSA); GPP; NPP; MODIS; validation; phenology; RADARSAT-2; rice; Synthetic Aperture Radar (SAR); decision tree; forest canopy height; aboveground biomass; ICESat GLAS; Landsat; random forest model; anisotropic reflectance; BRDF; rugged terrain; solo slope; composite slope; surface solar irradiance; geostationary satellite; polar orbiting satellite; LUT method; SURFRAD; downward shortwave radiation; daily average value; Antarctica; sinusoidal method; cloud fraction; interpolation; boreal forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters; multisource data fusion; aerosol retrieval; urban scale; vegetation dust-retention; multiple ecological factors; geographical detector model; snow cover; passive microwave; FY-3C/MWRI; algorithmic assessment; China; land surface temperature; satellite observations; flux measurements; latitudinal pattern; land cover change; fractional vegetation cover (FVC); multi-data set; northern China; spatio-temporal; inter-annual variation; uncertainty; standard error of the mean; downscaling; GPP; spatial heterogeneity; remote sensing; subpixel information; LiDAR; point cloud; leaf; gap fraction; 3D reconstruction; biodiversity; remote sensing; species richness; metric comparison; metric integration; leaf area index; MODIS products; Landsat; high resolution; homogeneous and pure pixel filter; pixel unmixing; vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles; n/a; geometric-optical model; thermal radiation directionality; quantitative remote sensing inversion; scale effects; comprehensive field experiment

المصادر: قاعدة بيانات الكتب المجانية

اللغة: الإنجليزية

التاريخ: 2019

ردمك: 9783038972761

ملف خارجي