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• W2474785551 abstract "ABSTRACTConsiderable uncertainty occurs in the parameter estimates of traditional rainfall–water level transfer function noise (TFN) models, especially with the models built using monthly time step datasets. This is due to the equal weights assigned for rainfall occurring during both water level rise and water level drop events while estimating the TFN model parameters using the least square technique. As an alternative to this approach, a threshold rainfall-based binary-weighted least square method was adopted to estimate the TFN model parameters. The efficacy of this binary-weighted approach in estimating the TFN model parameters was tested on 26 observation wells distributed across the Adyar River basin in Southern India. Model performance indices such as mean absolute error and coefficient of determination values showed that the proposed binary-weighted approach of fitting independent threshold-based TFN models for water level rise and water level drop scenarios considerably improves the model accurac..." @default.
• W2474785551 created "2016-07-22" @default.
• W2474785551 creator A5020173833 @default.
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• W2474785551 date "2016-08-08" @default.
• W2474785551 modified "2023-09-23" @default.
• W2474785551 title "Transfer function noise modelling of groundwater level fluctuation using threshold rainfall-based binary-weighted parameter estimation approach" @default.
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• W2474785551 doi "https://doi.org/10.1080/02626667.2016.1171325" @default.
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