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Table 7

Properties of applied models

Strategy of modelingCharacteristicsAdvantages and disadvantagesPre-processing methodMultistep-ahead-predictions
Multi-station Multi-station form High accuracy EEMD-WT-MI No 
 Spatio-temporal properties Fill the missing values   
  Predict entire river discharge by training only one RVM   
  Using geomorphological features as input   
I-RVMs Integrated form of AI High accuracy EEMD-WT-MI Yes 
 Meta-learning system Take advantage of semi-distributed specification to predict precisely   
 Using geomorphological unit hydrograph based on cascade of linear reservoirs Fill the missing values of target station   
 Combined form GUHCR and meta-learning system Unable to predict or fill missing values of other stations in integrated form   
 Semi distributed AI model    
MRVM Multi input multi output Fill the missing values of target stations EEMD-WT-MI Yes 
  Unable to predict or fill missing values of other stations   
Strategy of modelingCharacteristicsAdvantages and disadvantagesPre-processing methodMultistep-ahead-predictions
Multi-station Multi-station form High accuracy EEMD-WT-MI No 
 Spatio-temporal properties Fill the missing values   
  Predict entire river discharge by training only one RVM   
  Using geomorphological features as input   
I-RVMs Integrated form of AI High accuracy EEMD-WT-MI Yes 
 Meta-learning system Take advantage of semi-distributed specification to predict precisely   
 Using geomorphological unit hydrograph based on cascade of linear reservoirs Fill the missing values of target station   
 Combined form GUHCR and meta-learning system Unable to predict or fill missing values of other stations in integrated form   
 Semi distributed AI model    
MRVM Multi input multi output Fill the missing values of target stations EEMD-WT-MI Yes 
  Unable to predict or fill missing values of other stations   
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