Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, gaussian process, and minimax probability machine regression: Case study of Brisbane city

Journal of Hydrologic Engineering

Volumn 22, Issue 6, 2017, Pages

  Deo, Ravinesh C ^a   Samui, Pijush ^b  

^a UNIVERSITY OF SOUTHERN QUEENSLAND   (Australia)

^b NATIONAL INSTITUTE OF TECHNOLOGY PATNA   (India)

Author keywords

Evaporation model; Gaussian process regression; Genetic programming; Least square support vector regression; Minimax probability machine regression

Indexed keywords

ERRORS; FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GENETIC ALGORITHMS; MEAN SQUARE ERROR; PROBABILITY; REGRESSION ANALYSIS; SOLAR RADIATION; WIND;

CORRELATION COEFFICIENT; CUMULATIVE FREQUENCIES; EVAPORATION MODEL; GAUSSIAN PROCESS REGRESSION; IRRIGATION MANAGEMENT; LEAST SQUARE SUPPORT VECTOR REGRESSIONS; MINIMAX PROBABILITY MACHINE; ROOT MEAN SQUARE ERRORS;

GENETIC PROGRAMMING;

EVAPORATION; FORECASTING METHOD; GAUSSIAN METHOD; HYDROLOGICAL MODELING; LEAST SQUARES METHOD; PROBABILITY; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE;

AUSTRALIA; BRISBANE; QUEENSLAND;

EID: 85017512971     PISSN: 10840699     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)HE.1943-5584.0001506     Document Type: Article

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