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

Sivakumar, 2007 - Google Patents

Nonlinear determinism in river flow: prediction as a possible indicator

Sivakumar, 2007

Document ID
16861221703895261536
Author
Sivakumar B
Publication year
Publication venue
Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group

External Links

Snippet

Whether or not river flow exhibits nonlinear determinism remains an unresolved question. While studies on the use of nonlinear deterministic methods for modeling and prediction of river flow series are on the rise and the outcomes are encouraging, suspicions and criticisms …
Continue reading at onlinelibrary.wiley.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
    • G06F17/10—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
    • 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
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • 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
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image

Similar Documents

Publication Publication Date Title
Wang et al. Re-evaluation of the power of the Mann-Kendall test for detecting monotonic trends in hydrometeorological time series
Karunasinghe et al. Chaotic time series prediction with a global model: Artificial neural network
Khaliq et al. Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: The effect of short-and long-term persistence
Westra et al. A strategy for diagnosing and interpreting hydrological model nonstationarity
Dhanya et al. Nonlinear ensemble prediction of chaotic daily rainfall
Ng et al. Chaos based analytical techniques for daily extreme hydrological observations
Kyoung et al. Dynamic characteristics of monthly rainfall in the Korean Peninsula under climate change
Brunner et al. Modeling the spatial dependence of floods using the Fisher copula
Tarwidi et al. An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach
Beuzen et al. Ensemble models from machine learning: an example of wave runup and coastal dune erosion
Sivakumar Nonlinear determinism in river flow: prediction as a possible indicator
Dhanya et al. Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs
Mehr et al. A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events
Tongal et al. Seasonality of low flows and dominant processes in the Rhine River
De Domenico et al. Chaos and reproduction in sea level
Huang et al. Joint distribution of significant wave height and zero-up-crossing wave period using mixture copula method
Zajac et al. Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation
Bogner et al. Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment
Sheng et al. FARIMA with stable innovations model of Great Salt Lake elevation time series
Snieder et al. Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy
Sampurno et al. Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Linh et al. Flood prediction based on climatic signals using wavelet neural network
Alexander et al. Development of hybrid wavelet-ANN model for hourly flood stage forecasting
Jeong et al. Copula-based modeling and stochastic simulation of seasonal intermittent streamflows for arid regions
Rajagopalan et al. A nonlinear dynamical systems‐based modeling approach for stochastic simulation of streamflow and understanding predictability