Wastewater recycling and groundwater sustainability through self-organizing map and style based generative adversarial networks

Abstract

Wastewater recycling is a pivotal strategy in sustainable water management, designed to mitigate water scarcity and curb environmental pollution. This research introduces an innovative approach merging Self-Organizing Maps (SOM) and Style-Based Generator Generative Adversarial Networks (StyleGAN) to revolutionize wastewater recycling. SOM optimizes the distribution of treated water by identifying ideal locations for recycling outlets and treatment facilities. Meanwhile, StyleGAN enhances water quality by learning from diverse samples, producing purified water suitable for non-drinking purposes. The combination of SOM and StyleGAN maximizes resource utilization and addressing water scarcity challenges while minimizing environmental impact. Also, this method is not only optimizes treated water distribution but also enhances water quality, showcasing potential benefits for sustainable water practices, including groundwater replenishment. Self-Organizing Maps construct a spatial model of the wastewater treatment system, finding optimal locations for recycling outlets and treatment facilities. This spatial intelligence optimizes the distribution of treated water, channeling it efficiently to high-demand areas, thereby maximizing resource utilization. Simultaneously, Style-Based Generator Generative Adversarial Networks elevate the quality of treated wastewater. By assimilating knowledge from diverse wastewater samples, StyleGAN produces water of superior quality with diminished contaminants and enhanced aesthetics. This transformation renders treated wastewater more versatile for non-potable purposes like irrigation and industrial processes. The SOM-StyleGAN technologies offers a holistic solution for wastewater recycling, addressing both distribution efficiency and water quality enhancement. The SOM method outperforms MLP, DBN, and GAN-ANN in fidelity, diversity, contaminant reduction, and aesthetic quality. With a fidelity of 0.97, substantial improvements over GAN-ANN showcase its effectiveness. These results underscore the method's potential contribution to sustainable water management practices, emphasizing its versatility and quality in treated water generation. These findings contribute significantly to enhancing water quality and underscore its potential practical implications for sustainable water practices, marking a substantial step forward in the field.