[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Fiedler, 2020 - Google Patents

Sensitivity analysis of a deep learning model for discharge prediction in the Regen catchment

Fiedler, 2020

View PDF
Document ID
3029147512613667430
Author
Fiedler L
Publication year

External Links

Snippet

Estimating rainfall-runoff relationship and streamflow modeling is a significant element in operational flood management. In order to be conducted successfully the rainfall-runoff relationship and discharge behavior in a watershed have to be predicted accurately. Thus …
Continue reading at mediatum.ub.tum.de (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
US12050845B2 (en) Estimating physical parameters of a physical system based on a spatial-temporal emulator
US11966670B2 (en) Method and system for predicting wildfire hazard and spread at multiple time scales
Xu et al. Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation
Ghosh et al. Application of Cellular automata and Markov-chain model in geospatial environmental modeling-A review
Feizizadeh et al. A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis
Hamzah et al. Imputation methods for recovering streamflow observation: A methodological review
Cherkassky et al. Computational intelligence in earth sciences and environmental applications: Issues and challenges
US20210064802A1 (en) Method and System for Increasing the Resolution of Physical Gridded Data
Li et al. Characterizing distributed hydrological model residual errors using a probabilistic long short-term memory network
Zanoni et al. A catchment-scale model of river water quality by Machine Learning
Bai et al. A forecasting method of forest pests based on the rough set and PSO-BP neural network
Oriani et al. Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and pattern-based estimation on different terrain types
Secci et al. Artificial intelligence models to evaluate the impact of climate change on groundwater resources
Troncoso-García et al. Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting
Misra et al. Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks
Al Moteri et al. An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index
Khatun et al. A novel insight on input variable and time lag selection in daily streamflow forecasting using deep learning models
Fiedler Sensitivity analysis of a deep learning model for discharge prediction in the Regen catchment
Peeters et al. Conceptual evaluation of continental land-surface model behaviour
Vidyarthi et al. Does ANN really acquire the physics of the system? A study using conceptual components from an established water balance model
Pensoneault et al. Ensemble Kalman Inversion for upstream parameter estimation and indirect streamflow correction: A simulation study
Kadri et al. Comparison between NARX-NN and HEC-HMS models to simulate Wadi Seghir catchment runoff events in Algerian northern
Afzaal et al. Artificial neural modeling for precision agricultural water management practices
Loosvelt Assessment of uncertainty in land surface characterization and implications for spatially distributed hydrological modelling
Tapoglou et al. Uncertainty estimations in different components of a hybrid ANN–Fuzzy–Kriging model for water table level simulation