Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall-runoff modelling under different input domains

Hydrological Processes

Volumn 25, Issue 2, 2011, Pages 175-193

  Lohani, Anil Kumar ^a   Goel, N K ^b   Bhatia, K K S ^c  

^a NATIONAL INSTITUTE OF HYDROLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^c Director   (India)

Author keywords

Antecedent moisture content; Clustering; Fuzzy logic; Gaussian membership function; Linear transfer function; Neural network

Indexed keywords

ANTECEDENT MOISTURE; ANTECEDENT SOIL MOISTURE; APPROPRIATE MODELS; ARTIFICIAL NEURAL NETWORK; CENTRAL INDIA; CLUSTERING; COMPARATIVE STUDIES; DAILY RAINFALL; FUZZY MODELLING; GAUSSIAN MEMBERSHIP FUNCTION; INPUT DATA VECTOR; INPUT VECTOR; LINEAR TRANSFER FUNCTION; MODEL PERFORMANCE; MODELLING TECHNIQUES; RAINFALL RUNOFF; RAINFALL-RUNOFF MODELLING; TAKAGI-SUGENO FUZZY MODELS;

FUZZY LOGIC; FUZZY SYSTEMS; GEOLOGIC MODELS; MEMBERSHIP FUNCTIONS; MOISTURE DETERMINATION; NEURAL NETWORKS; RUNOFF; SOIL MOISTURE; TRANSFER FUNCTIONS;

RAIN;

ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; COMPARATIVE STUDY; FUZZY MATHEMATICS; GAUSSIAN METHOD; MOISTURE CONTENT; RAINFALL-RUNOFF MODELING; SOIL MOISTURE; TRANSFER FUNCTION;

EID: 78650952042     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.7831     Document Type: Article

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