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1. WO2019224739 - SYSTEM AND METHOD FOR REAL TIME PREDICTION OF WATER LEVEL AND HAZARD LEVEL OF A DAM

Publication Number WO/2019/224739
Publication Date 28.11.2019
International Application No. PCT/IB2019/054228
International Filing Date 22.05.2019
Chapter 2 Demand Filed 20.03.2020
IPC
G06N 3/02 2006.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
G06Q 10/04 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA 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
10Administration; Management
04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
CPC
G06F 17/18
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
10Complex mathematical operations
18for evaluating statistical data ; , e.g. average values, frequency distributions, probability functions, regression analysis
G06K 9/6263
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6262Validation, performance evaluation or active pattern learning techniques
6263based on the feedback of a supervisor
G06K 9/6265
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6262Validation, performance evaluation or active pattern learning techniques
6265based on a specific statistical test
G06N 20/20
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
20Ensemble learning
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
G06N 3/0445
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0445Feedback networks, e.g. hopfield nets, associative networks
Applicants
  • UNIVERSITY OF JOHANNESBURG [ZA]/[ZA]
Inventors
  • PAUL, Dipanjan
  • TSHILIDZI, Marwala
  • PAUL, Satyakama
Agents
  • SPOOR & FISHER
  • MOUBRAY, Hugh Robert
  • GILSON, David Grant
  • KEMP, Mark
  • WHITTAKER, Jonathan Denis
  • COCHRANE, David Hylton
  • ABRAMSON, Lance
  • MCKNIGHT, John Crawford
  • MAHOMED, Shanaaz
  • KAHN, Craig
  • BIAGIO, Dina
  • CILLIERS, Lodewyk Petrus
  • GRANT, Tyron James
  • BEHARIE, Tertia
  • VAN SCHALKWYK, Herman
  • PIENAAR, Danie
  • HANEKOM, Dirk Christiaan
Priority Data
2018/0346325.05.2018ZA
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) SYSTEM AND METHOD FOR REAL TIME PREDICTION OF WATER LEVEL AND HAZARD LEVEL OF A DAM
(FR) SYSTÈME ET PROCÉDÉ DE PRÉDICTION EN TEMPS RÉEL D'UN NIVEAU D'EAU ET D'UN NIVEAU DE RISQUE D'UN BARRAGE
Abstract
(EN) The invention relates to a water level prediction system for a dam. The system includes a water level prediction module which is configured to (a) receive time series data, which relates to a water level of the dam, in real-time; and (b) predict, in real-time, a future water level of the dam by processing the received time series data in one or more predictive models/formula(s)/algorithm(s). The one or more predictive models/formula(s)/algorithm(s) may include a recurrent neural network (RNN) or RNN model/algorithm which is configured/trained to predict, in real-time, a future water level of the dam by using the received time series data in the RNN or RNN model/algorithm. The water level prediction module may also include at least one statistical model/algorithm which is configured/trained to predict, in real-time, a future water level of the dam by using the received time series data in the statistical model/algorithm.
(FR) L'invention concerne un système de prédiction de niveau d'eau pour un barrage. Le système comprend un module de prédiction de niveau d'eau qui est configuré pour : (a) recevoir des données de série chronologique, qui concernent un niveau d'eau du barrage, en temps réel ; et (b) prédire, en temps réel, un futur niveau d'eau du barrage en traitant les données de séries chronologiques reçues dans un ou plusieurs modèles prédictifs/formules/algorithmes. Le(s) modèle(s) prédictif(s)/formule(s)/algorithme(s) peuvent comprendre un réseau neuronal récurrent (RNN) ou un modèle/algorithme RNN qui est configuré/appris pour prédire, en temps réel, un futur niveau d'eau du barrage à l'aide des données de série chronologique reçues dans le RNN ou le modèle/algorithme RNN. Le module de prédiction de niveau d'eau peut également comprendre au moins un modèle/algorithme statistique qui est configuré/appris pour prédire, en temps réel, un futur niveau d'eau du barrage à l'aide des données de série chronologique reçues dans le modèle/algorithme statistique.
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