A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping

Water Resources Management

Volumn 29, Issue 14, 2015, Pages 5217-5236

  Naghibi, Seyed Amir ^a   Pourghasemi, Hamid Reza ^b  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b SHIRAZ UNIVERSITY   (Iran)

Author keywords

Boosted regression tree; Classification and regression tree; Evidential belief function; General linear model; GIS; Groundwater potential mapping; Random forest

Indexed keywords

COST EFFECTIVENESS; DECISION TREES; FORESTRY; GEOGRAPHIC INFORMATION SYSTEMS; GROUNDWATER; LAND USE; LITHOLOGY; MACHINE LEARNING; MULTIVARIANT ANALYSIS; RANDOM FORESTS; UNCERTAINTY ANALYSIS;

BELIEF FUNCTION; BOOSTED REGRESSION TREES; CLASSIFICATION AND REGRESSION TREE; GENERAL LINEAR MODELING; GROUNDWATER POTENTIALS;

MAPPING;

ALGORITHM; CLASSIFICATION; COMPARATIVE STUDY; GIS; GROUNDWATER; MACHINE LEARNING; MAPPING; MULTIVARIATE ANALYSIS; NUMERICAL MODEL; REGRESSION ANALYSIS; SPRING WATER; WATERSHED;

CHAHAR MAHALL VA BAKHTIARI; IRAN;

EID: 85027950488     PISSN: 09204741     EISSN: 15731650     Source Type: Journal    
DOI: 10.1007/s11269-015-1114-8     Document Type: Article

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