Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution. (1st July 2022)

• Record Type: Journal Article
• Authors: Nadiri, Ata Allah; Moazamnia, Marjan; Sadeghfam, Sina; Gnanachandrasamy, Gopalakrishnan; Venkatramanan, Senapathi
• Is Part Of: Environmental pollution. Volume 304(2022)
• Publication Date: 2022-07-01
• Subjects: Intrinsic vulnerability -- Specific vulnerability -- Non-point source pollution -- Urmia aquifer; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.119208
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based on Convolutional Neural Network (CNN) to decrease subjectivity. This formulation considers three definitions of intrinsic, specific, and total vulnerabilities. Accordingly, three CNN models are trained and tested to calculate IVI, SVI, and TVI, respectively referring to the intrinsic, specific, and total vulnerability indices. The formulation is applied in an unconfined aquifer northwest of Iran and delineates hotspots within the aquifer. The area under curve (AUC) values derived by the receiver operating curves evaluate the vulnerability indices versus nitrate concentrations. The AUC values for BDF, IVI, SVI, and TVI are 0.81, 0.91, 0.95, and 0.95, respectively. Therefore, CNNs significantly improve the results compared to BDF, but IVI, SVI, and TVI have approximately identical performances. However, the visual comparison between their results provides evidence that significant differences exist between the spatial patterns despite identical AUC values. Highlights: Convolutional Neural Network (CNN) was formulated for mapping aquifer vulnerability. Intrinsic/specific/total vulnerabilities were delineated in an unconfined aquifer. The inherent subjectivities with the DRASTIC framework was reduced by CNNs. CNNs identify hotspots within the study area and increase the modeling performances. Three types of vulnerabilities have different patterns despite identical performance.