Advancing Aquifer Vulnerability Mapping through Integrated Deep Learning Approaches

Abstract

Groundwater vulnerability maps are crucial for safeguarding groundwater quality. A research gap exists in using advanced data fusion techniques to identify areas subject to seawater intrusion. To address this gap, this research enhances the GALDIT method and applies diverse deep learning models, combined with machine learning techniques, to improve the precision of aquifer vulnerability mapping. The new GALDITMW model incorporates the seawater mixing index and the parameters related to the production well density and aquifer porous medium. For the first time, supervised and unsupervised deep learning models, such as deep neural networks, deep belief networks, deep stacked autoencoders, and convolutional neural networks, are used for vulnerability mapping. In the second stage, the results of various machine learning models are fused to improve performance. The models' effectiveness is evaluated using a vulnerability index based on total dissolved solids (TDS) in an aquifer hydraulically connected with Salt Lake in central Iran, which faces groundwater depletion and salinization. The evaluation of the models based on performance metrics and the confusion matrix demonstrates that initial deep-learning models perform well. Significant improvements were observed in the second stage involving machine learning models, confirming their strong correlation (R² > 0.985) with observed chloride values. The GPR model achieved an F1 score of 86.92%, an NSE of 0.911, and an RMSE reduction of 0.026 mg/L compared to the first-stage models. The proposed method offers a novel and accurate method for identifying vulnerable areas and provides helpful information for groundwater resource management.