In the COVID-19 era, people face situations that they have never experienced before, which alerted the importance of the ESG. Investors also consider ESG indexes as an essential factor for their investments, and some research yielded that the return on sustainable funds is more significant than on non-sustainable ones. Nevertheless, a deficiency in research exists about analyzing ESG through artificial intelligence algorithms due to adversity in collecting ESG-related datasets. Therefore, this paper suggests integrated AI approaches to the ESG datasets with the five different experiments. We also focus on analyzing the governance and social datasets through NLP algorithms and propose a straightforward method for predicting a specific firm’s ESG rankings. Results were evaluated through accuracy score, RMSE, and MAE, and every experiment conducted relevant scores that achieved our aim. From the results, it could be concluded that this paper successfully analyzes ESG data with various algorithms. Unlike previous related research, this paper also emphasizes the importance of the adversarial attacks on the ESG datasets and suggests methods to detect them effectively. Furthermore, this paper proposes a simple way to predict ESG rankings, which would be helpful for small businesses. Even though it is our limitation that we only use restricted datasets, our research proposes the possibility of applying the AI algorithms to the ESG datasets in an integrated approach.

Urban flooding in economically and environmentally vulnerable areas—such as alleyways, lowlands, and semi-basement residences—poses serious threats. Previous studies on flood detection have largely relied on aerial or satellite-based imagery. While some studies used ground-level images, datasets capturing localized flooding in economically vulnerable urban areas remain limited. To address this, we constructed AlleyFloodNet, a dataset designed for rapid flood detection in flood-vulnerable urban areas, with ground-level images collected from diverse regions worldwide. In particular, this dataset includes data from flood-vulnerable urban areas under diverse realistic conditions, such as varying water levels, colors, and lighting. By fine-tuning several deep learning models on AlleyFloodNet, the ConvNeXt-Large model achieved excellent performance, with an accuracy of 96.56%, precision of 95.45%, recall of 97.67%, and an F1 score of 96.55%. Comparative experiments with existing ground-level image datasets confirmed that datasets specifically designed for economically and flood-vulnerable urban areas, like AlleyFloodNet, are more effective for detecting floods in these regions. By successfully fine-tuning deep learning models, AlleyFloodNet not only addresses the limitations of existing flood monitoring datasets but also provides foundational resources for developing practical, real-time flood detection and alert systems for urban populations vulnerable to flooding.

Urban flood detection requires visual examples of flooded streets and alleys, but such event-state images are difficult to collect at scale. This study examines whether sparse real-image training sets can be strengthened using text-prompt-only AI-generated virtual imagery for ground-level flood detection in flood-prone urban areas. Building on AlleyFloodNet, the generated images were used only as condition-specific training augmentation, while validation and testing were conducted exclusively on real images. Across eight ImageNet-pretrained architectures and three random seeds, the results show that virtual imagery is not a substitute for real flood observations. When virtual images dominated the training set, performance declined. However, when a sufficient real-image anchor was available, virtual augmentation improved the highest observed fixed-test performance. The strongest mixed-condition result was obtained by EfficientNet-B2 under Real30_Aug70, reaching 91.00% accuracy, 89.48% flooded-class recall, and 90.79% macro-F1. These findings suggest that prompt-only virtual imagery can help mitigate real-image scarcity in selected training conditions, but its benefit depends on the real-to-virtual composition and model architecture.