Rainfall-runoff modeling: Comparison of two approaches with different data requirements

Water Resources Management

Volumn 24, Issue 1, 2010, Pages 37-62

  Bhadra, A ^a   Bandyopadhyay, A ^b   Singh, R ^c   Raghuwanshi, N S ^c  

^a NORTH EASTERN REGIONAL INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

^b NATIONAL INSTITUTE OF HYDROLOGY   (India)

^c INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Artificial neural network; Levenberg marquardt; Muskingum routing; Rainfall runoff modeling; Scs curve number method

Indexed keywords

ARTIFICIAL NEURAL NETWORK; LEVENBERG-MARQUARDT; MUSKINGUM ROUTING; RAINFALL-RUNOFF MODELING; SCS CURVE NUMBER;

AGRICULTURAL RUNOFF; AGRICULTURE; ATMOSPHERIC THERMODYNAMICS; BENCHMARKING; C (PROGRAMMING LANGUAGE); CATCHMENTS; FORECASTING; GEOLOGIC MODELS; GRAPHICAL USER INTERFACES; NEURAL NETWORKS; SOIL CONSERVATION; SOIL MOISTURE;

RAIN;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; COMPUTER SIMULATION; CONCEPTUAL FRAMEWORK; DRAINAGE NETWORK; HYDROLOGICAL MODELING; NUMERICAL MODEL; PREDICTION; RAINFALL-RUNOFF MODELING; RIVER FLOW;

INDIA; KANGSABATI RIVER; WEST BENGAL;

EID: 77955276945     PISSN: 09204741     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11269-009-9436-z     Document Type: Article

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