Novelty and International Appeal Statement
Through understanding on spatio-temporal variability of groundwater
level in tropical savanna climatic region with heavily stressed aquifers
and its hydrological, geological, and climatic (HGC) controls, has
immense role in long-term sustainable management of the resource. This
study attempts to develop prediction model for GWL taking the dominant
HGC control as explanatory variables using ML algorithm. To achieve the
aforesaid objective, firstly the spatio-temporal variability of GWL in
the study region was comprehended and the dominant HGC controls for
observed spatio-temporal variability were identified using entropy
approach. To the best of our knowledge this study is the first of its
kind in groundwater variability prospective. Further, the grid-wise
relationship among the groundwater level and the features in the
dominant HGC control was established using Random Forest learning
algorithm to make short- and long-term predictions of GWL with
reasonable accuracy in the study region with limited data. The proposed
framework is a novel attempt to characterize spatio-temporal variability
in GWL in tropical regions with highly heterogeneous aquifers and
predict the future trends in GWL at regional level.