Nothing Special   »   [go: up one dir, main page]

Wang et al., 2023 - Google Patents

R-statistic based predictor variables selection and vine structure determination approach for stochastic streamflow generation considering temporal and spatial …

Wang et al., 2023

Document ID
9659906822793662982
Author
Wang X
Shen Y
Publication year
Publication venue
Journal of Hydrology

External Links

Snippet

Stochastic streamflow generation is crucial for water resources planning and management as well as water conservancy project design and operation. This study proposes an accurate, reliable and parsimonious approach for stochastic streamflow generation …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"

Similar Documents

Publication Publication Date Title
Dibike et al. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models
Herbert et al. Long-term reservoir inflow forecasts: Enhanced water supply and inflow volume accuracy using deep learning
Shen et al. Random forests-based error-correction of streamflow from a large-scale hydrological model: Using model state variables to estimate error terms
Pektaş et al. ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient
Mehta et al. Improving flood forecasting in Narmada river basin using hierarchical clustering and hydrological modelling
Nathan et al. Estimating the exceedance probability of extreme rainfalls up to the probable maximum precipitation
Yan et al. Flood risk analysis of reservoirs based on full-series ARIMA model under climate change
Bahati et al. Quantifying the potential impacts of land-use and climate change on hydropower reliability of Muzizi hydropower plant, Uganda
Dong et al. A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions
Gobena et al. Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system
Dawkins et al. The advanced meteorology explorer: a novel stochastic, gridded daily rainfall generator
Gao et al. A framework for automatic calibration of SWMM considering input uncertainty
Tounsi et al. On the use of machine learning to account for reservoir management rules and predict streamflow
Mahoney et al. Blasts from the past: Reimagining historical storms with model simulations to modernize dam safety and flood risk assessment
Kjeldsen et al. Uncertainty in flood frequency analysis
Wang et al. R-statistic based predictor variables selection and vine structure determination approach for stochastic streamflow generation considering temporal and spatial dependence
Chen et al. TreeLSTM: A spatiotemporal machine learning model for rainfall-runoff estimation
Fan et al. Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification
Chen et al. Urban inundation rapid prediction method based on multi-machine learning algorithm and rain pattern analysis
Regonda et al. Prototype decision support system for operations on the Gunnison basin with improved forecasts
Curceac et al. Adjusting for conditional bias in process model simulations of hydrological extremes: An experiment using the North Wyke Farm Platform
Zhong et al. Probabilistic forecasting based on ensemble forecasts and EMOS method for TGR inflow
Velasco et al. Days-ahead water level forecasting using artificial neural networks for watersheds
Rajabi et al. The analysis of uncertainty of climate change by means of SDSM model case study: Kermanshah
Tian et al. Stochastic RCM-driven cooling and heating energy demand analysis for residential building