Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms

• Published in: Environmental science and pollution research international 2021-10, Vol.28 (40), p.57030-57045
• Main Authors: Elzain, Hussam Eldin, Chung, Sang Yong, Senapathi, Venkatramanan, Sekar, Selvam, Park, Namsik, Mahmoud, Ahmed Abdulhamid
• Format: Article
• Language: English
• Subjects: Algorithms; Aquatic Pollution; Aquifers; Artificial intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; Computer applications; Contamination; Deep learning; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Evolutionary algorithms; Evolutionary computation; Groundwater pollution; Information processing; Machine learning; Model accuracy; Neural networks; Optimization algorithms; Particle swarm optimization; Research Article; Swarm intelligence; Waste Water Technology; Water Management; Water Pollution Control; Water resources
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-021-14522-0

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.