Enhanced machine learning model to estimate groundwater spring potential based on digital elevation model parameters

Abstract

In the current work, an enhanced model was developed to map groundwater spring potential using parameters derived uniquely from digital elevation model (DEM) as inputs. The proposed method is based on combining Quantum Particle Swarm Optimization (QPSO) and the Credal Decision Tree (CDT) groups (QPSO/CDT model). The principle of the suggested algorithm is to establish a CDT tree realized according to Random Subspace model (RSS). Then, we integrated QPSO to improve the three indices (subspace size, number of CDT sub-groups, and the CDT highest-range-of-trees). To reach this goal, a case study area in northeast Tunisia (region of Mornag) was chosen and 10 parameters were derived from the DEM. The result shows high accuracy of the QPSO/CDT model outputs compared to other machine learning models. Across the ten parameters, the convergence index, topographic wetness, drainage density, and altitude are the most relevant parameters.

Keywords:

• Groundwater spring potential
• machine learning
• QPSO/CDT model
• decision tree
• geospatial delineation

Acknowledgements

Data are available from the U.S. Geological Survey and Water Resources General Directorate (DGRE). The authors appreciate the support of Tunis El Manar University and the Research Unit UR13ES26 as well as the collaboration of ‘Institut de Recherche et d'Analyse Physico-Chimique’ – INRAP.

Disclosure statement

No potential conflict of interest was reported by the author(s).