Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth

Abstract

Accurate and reliable interpolation of groundwater depth over a region is a pre-requisite for efficient planning and management of water resources. The performance of two deterministic, such as inverse distance weighting (IDW) and radial basis function (RBF) and two stochastic, i.e., ordinary kriging (OK) and universal kriging (UK) interpolation methods was compared to predict spatio-temporal variation of groundwater depth. Pre- and post-monsoon groundwater level data for the year 2006 from 110 different locations over Delhi were used. Analyses revealed that OK and UK methods outperformed the IDW method, and UK performed better than OK. RBF also performed better than IDW and OK. IDW and RBF methods slightly underestimated and both the kriging methods slightly overestimated the prediction of water table depth. OK, RBF and UK yielded 27.52, 27.66 and 51.11 % lower RMSE, 27.49, 35.34 and 51.28 % lower MRE, and 14.21, 16.12 and 21.36 % higher R2 over IDW. The isodepth-area curves indicated the possibility of exploitation of groundwater up to a depth of 20 m.