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

Mazandaranizadeh et al., 2017 - Google Patents

Development of a PSO-ANN model for rainfall-runoff response in basins, Case Study: Karaj Basin

Mazandaranizadeh et al., 2017

View PDF
Document ID
9245067688410856399
Author
Mazandaranizadeh H
Motahari M
Publication year
Publication venue
Civil Engineering Journal

External Links

Snippet

Successful daily river flow forecasting is necessary in water resources planning and management. A reliable rainfallrunoff model can provide useful information for water resources planning and management. In this study, particle swarm optimization algorithm …
Continue reading at www.academia.edu (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
    • G06N3/08—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
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • 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
    • 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
    • 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
CN109858647B (en) A regional flood disaster risk assessment and prediction method coupled with GIS and GBDT algorithms
Abbasi et al. A hybrid of Random Forest and Deep Auto-Encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction
Nourani et al. Estimation of prediction interval in ANN-based multi-GCMs downscaling of hydro-climatologic parameters
He et al. A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall
Mazandaranizadeh et al. Development of a PSO-ANN model for rainfall-runoff response in basins, Case Study: Karaj Basin
Nayak et al. Short‐term flood forecasting with a neurofuzzy model
Jalalkamali et al. Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran
Shamim et al. A comparison of artificial neural networks (ANN) and local linear regression (LLR) techniques for predicting monthly reservoir levels
Jalalkamali Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters
CN106971237B (en) A kind of Medium-and Long-Term Runoff Forecasting method for optimization algorithm of being looked for food based on bacterium
Gholami Rostam et al. Precipitation forecasting by large-scale climate indices and machine learning techniques
CN116933621A (en) Urban waterlogging simulation method based on terrain feature deep learning
Calp A hybrid ANFIS-GA approach for estimation of regional rainfall amount
Chiu et al. Infilling missing rainfall and runoff data for Sarawak, Malaysia using Gaussian mixture model based K-nearest neighbor imputation
Venkatesan et al. Forecasting floods using extreme gradient boosting–a new approach
Li et al. Decomposition-ANN methods for long-term discharge prediction based on Fisher’s ordered clustering with MESA
Li et al. A stepwise clustered hydrological model for addressing the temporal autocorrelation of daily streamflows in irrigated watersheds
Asghari et al. Spatial rainfall prediction using optimal features selection approaches
Chen et al. Physics-guided meta-learning method in baseflow prediction over large regions
Abozari et al. Comparison performance of artificial neural network based method in estimation of electric conductivity in wet and dry periods: Case study of Gamasiab river, Iran
Özgür et al. Modelling of daily reference evapotranspiration using deep neural network in different climates
Roshni et al. Operational use of machine learning models for sea-level modeling
Rawat et al. Daily Monsoon Rainfall Prediction using Artificial Neural Network (ANN) for Parbhani District of Maharashtra, India
Yılmaz et al. RECURRENT NEURAL NETWORKS FOR PEAK FLOW ESTIMATION.
Katipoğlu et al. Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network