Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/53542
Title: Ensemble based groundwater level prediction using neural network pattern fitting
Authors: Kumar, Ajith S
Vidhya, R
Keywords: Combined model;Data mining;Ensemble;Forecasting;Groundwater level prediction;Time series
Issue Date: Jan-2020
Publisher: NISCAIR-CSIR, India
Abstract: Prediction of groundwater level is implemented using Time-series prediction model and combined prediction model for learning the pattern and trend in groundwater level fluctuation, result show that the combined prediction model using, groundwater level time series and precipitation time series as input predictors is a better predictor. Study also shows that prediction is dependent on the pattern and trends at a particular location as every dataset depends on the dynamics of the location namely the geomorphology of the aquifer, the drainage inside the aquifer and pumping from the aquifer. Ensemble based forecasting is studied to fix the upper and lower limit of the prediction. Ensembles helped in fixing a range for the forecast instead of relying on a single unique value.
Page(s): 44-50
URI: http://nopr.niscair.res.in/handle/123456789/53542
ISSN: 0975-1033 (Online); 0379-5136 (Print)
Appears in Collections:IJMS Vol.49(01) [January 2020]

Files in This Item:
File Description SizeFormat 
IJMS 49(1) 44-50.pdf505.16 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.