Farrokhi et al., 2020 - Google Patents
A new framework for evaluation of rainfall temporal variability through principal component analysis, hybrid adaptive neuro-fuzzy inference system, and innovative …Farrokhi et al., 2020
View PDF- Document ID
- 13120887091471429241
- Author
- Farrokhi A
- Farzin S
- Mousavi S
- Publication year
- Publication venue
- Water Resources Management
External Links
Snippet
In this research, a new framework has been introduced for rainfall temporal variability evaluation by using combination of monthly rainfall data sets in three synoptic stations, Principal Component Analysis (PCA), Adaptive Neuro Fuzzy Inference System (ANFIS) …
- 238000000034 method 0 title abstract description 24
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
- G06N3/06—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
- G06N3/0472—Architectures, e.g. interconnection topology using probabilistic elements, e.g. p-rams, stochastic processors
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- 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
- 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
- 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
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Talebizadeh et al. | Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models | |
Ghorbani et al. | Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to river flows | |
Lohani et al. | Improving real time flood forecasting using fuzzy inference system | |
Kadkhodazadeh et al. | A novel LSSVM model integrated with GBO algorithm to assessment of water quality parameters | |
Chen et al. | Artificial neural network modeling of dissolved oxygen in reservoir | |
Aqil et al. | A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff | |
Barhmi et al. | Forecasting of wind speed using multiple linear regression and artificial neural networks | |
Farrokhi et al. | A new framework for evaluation of rainfall temporal variability through principal component analysis, hybrid adaptive neuro-fuzzy inference system, and innovative trend analysis methodology | |
Nayak et al. | A neuro-fuzzy computing technique for modeling hydrological time series | |
Shiri et al. | Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (South Western Iran) | |
Zaman Zad Ghavidel et al. | Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin | |
Khodakhah et al. | Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH | |
Panahi et al. | Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging | |
Nourani et al. | Estimation of Suspended Sediment Load Using Artificial Intelligence‐Based Ensemble Model | |
Ghorbani et al. | Estimation of soil cation exchange capacity using multiple regression, artificial neural networks, and adaptive neuro-fuzzy inference system models in Golestan Province, Iran | |
Ghorbani et al. | Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level forecasting | |
Farokhnia et al. | Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques | |
Bozorg-Haddad et al. | A self-tuning ANN model for simulation and forecasting of surface flows | |
Zhang et al. | Solar radiation estimation in different climates with meteorological variables using Bayesian model averaging and new soft computing models | |
Kişi | Evolutionary fuzzy models for river suspended sediment concentration estimation | |
Bagirov et al. | A comparative assessment of models to predict monthly rainfall in Australia | |
Sanikhani et al. | Comparison of different data-driven approaches for modeling lake level fluctuations: the case of Manyas and Tuz Lakes (Turkey) | |
Ahmadi et al. | Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models–A case study | |
Sharifi et al. | Evaluating the performance of agricultural water distribution systems using FIS, ANN and ANFIS intelligent models | |
Venkatesan et al. | Forecasting floods using extreme gradient boosting–a new approach |