Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models

Stochastic Environmental Research and Risk Assessment

Volumn 30, Issue 6, 2016, Pages 1769-1784

  Deo, Ravinesh C ^a   Samui, Pijush ^b   Kim, Dookie ^c  

^a UNIVERSITY OF SOUTHERN QUEENSLAND   (Australia)

^b VIT UNIVERSITY   (India)

^c Kunsan National University   (South Korea)

Author keywords

Extreme learning machine; Machine learning; Multivariate adaptive regression spline; Prediction of evaporation; Relevance vector machine

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; ERROR STATISTICS; FEEDFORWARD NEURAL NETWORKS; FORECASTING; FUNCTIONS; INCIDENT SOLAR RADIATION; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; MEAN SQUARE ERROR; NETWORK LAYERS; NEURAL NETWORKS; PERSONNEL TRAINING; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SPLINES; WATER MANAGEMENT; WATER RESOURCES;

COMPUTATIONALLY EFFICIENT; DECOMPOSITION OF VARIANCES; EXTREME LEARNING MACHINE; GENERALIZED CROSS VALIDATION; MODEL-PERFORMANCE STATISTICS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; RELEVANCE VECTOR MACHINE; SINGLE LAYER FEED-FORWARD NEURAL NETWORKS;

LEARNING SYSTEMS;

ECOSYSTEM MANAGEMENT; EVAPORATION; FARMING SYSTEM; HYDROLOGICAL CYCLE; MACHINE LEARNING; MULTIVARIATE ANALYSIS; NUMERICAL MODEL; PREDICTION; REGRESSION ANALYSIS; WATER MANAGEMENT; WATER RESOURCE;

EID: 84941335923     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-015-1153-y     Document Type: Article

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