Abstract
Groundwater serves the significant demand created by the human ecosystem across the globe. In this scenario, primary dependence on the groundwater scenario, the prediction of its availability will be valuable information for the stakeholders. Researchers in the current decade have widely used the prediction of the resources based on the available/historical database. Above made researchers generalize the selection models depending on the domain of study (application domain). In the generalization process, the introduction of risk towards dependability on the predicted outputs has been increased. This study tries to identify the performance of two well-known predictive algorithms, which have their footprints both in data-driven, and data mining models, namely, (a) artificial neural network (ANN) and (b) support vector machine (SVM). The performance analysis has been performed to understand the impact of neurons in ANN and Kernel functions in cases of SVM. The developed framework is allowed to explore three well locations in the same basin to explicitly show the performance of the algorithms. The prediction capability is measured in Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE). The results inferred that even though the study groundwater wells are pertaining to the same basin (where climate and lithology are almost similar), the algorithms may not be generalized for the domain application. The above observation may be due to the draft and land use pattern variability.
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Ramsundram, N., Sattari, M.T., Kaviya, R., Kaarthic, M., Niveditha, M. (2023). Temporal Prediction of Groundwater Levels: A Gap in Generalization. In: Thambidurai, P., Dikshit, A.K. (eds) Impacts of Urbanization on Hydrological Systems in India. Springer, Cham. https://doi.org/10.1007/978-3-031-21618-3_11
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