The application of data mining techniques for the regionalisation of hydrological variables

Hydrology and Earth System Sciences

Volumn 6, Issue 4, 2002, Pages 685-694

  Hall, M J ^a   Minns, A W ^b   Ashrafuzzaman, A K M ^c  

^a UNESCO IHE INSTITUTE FOR WATER EDUCATION   (Netherlands)

^b UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^c River Research Institute   (Bangladesh)

Author keywords

Artificial neural networks; Catchment characteristics; Data mining; Floods; Regionalisation

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CATCHMENT; DATA PROCESSING; FLOOD FREQUENCY;

ENGLAND; INDONESIA; JAVA; SCOTLAND; SUMATRA; UNITED KINGDOM; WALES;

EID: 0036698602     PISSN: 10275606     EISSN: None     Source Type: Journal    
DOI: 10.5194/hess-6-685-2002     Document Type: Article

References (17)

1. Factors influencing the occurrence of floods in a humid region of diverse terrain
2. Flood frequency analysis
3. On the delimitation of regional patterns of recent climatic fluctuations
4. Regional flood frequency analysis using artificial neural networks
5. The classification of hydrologically homogeneous regions
6. Regional flood frequency analysis using artificial neural networks: A case study
7. Multilayer feedforward networks are universal approximators
8. Precipitation variability and drought
9. USGS study reveals a decline in long-record streamgauges
10. Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications
11. Prediction of two-year peak stream discharges using neural networks
12. Identification of homogeneous regions for the purpose of regionalisation