Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data

Water Science and Technology

Volumn 69, Issue 6, 2014, Pages 1326-1333

  Mounce, S R ^a   Shepherd, W ^a   Sailor, G ^a   Shucksmith, J ^a   Saul, A J ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Artificial neural networks; Combined sewer overflows; Cross correlation; Depth monitoring; Prediction; Rainfall radar

Indexed keywords

CATCHMENTS; FORECASTING; HYDRAULIC MODELS; NEURAL NETWORKS; RADAR; RAIN;

COMBINED SEWER OVERFLOWS; COMBINED SEWER SYSTEMS; CROSS CORRELATIONS; HYDRAULIC PERFORMANCE; RAINFALL INTENSITY; RAINFALL RADAR DATA; RAINFALL RADARS; URBAN DRAINAGE SYSTEMS;

COMBINED SEWERS;

RAIN;

ARTIFICIAL NEURAL NETWORK; CATCHMENT; DRAINAGE NETWORK; HYDROLOGICAL MODELING; MONITORING; PRECIPITATION INTENSITY; RADAR; RAINFALL; RAINGAUGE; SEWER NETWORK; TIME SERIES; URBAN DRAINAGE; WASTE MANAGEMENT; WATER INDUSTRY;

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CATCHMENT; COMBINED SEWER OVERFLOW; MEASUREMENT ACCURACY; PERFORMANCE; PREDICTION; SEWER; TELECOMMUNICATION; TIME SERIES ANALYSIS;

ENGLAND; UNITED KINGDOM;

DRAINAGE, SANITARY; NEURAL NETWORKS (COMPUTER); RADAR; RAIN;

EID: 84897511520     PISSN: 02731223     EISSN: None     Source Type: Journal    
DOI: 10.2166/wst.2014.024     Document Type: Article

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