This study explores the efficacy of a Transformer model for 120-hour streamflow prediction across 125 diverse locations in Iowa, US. Utilizing data from the preceding 72 hours, including precipitation, evapotranspiration, and discharge values, we developed a generalized model to predict future streamflow. Our approach contrasts with traditional methods that typically rely on location-specific models. We benchmarked the Transformer model's performance against three deep learning models (LSTM, GRU, and Seq2Seq) and the Persistence approach, employing Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Pearson's r, and Normalized Root Mean Square Error (NRMSE) as metrics. The study reveals the Transformer model's superior performance, maintaining higher median NSE and KGE scores and exhibiting the lowest NRMSE values. This indicates its capability to accurately simulate and predict streamflow, adapting effectively to varying hydrological conditions and geographical variances. Our findings underscore the Transformer model's potential as an advanced tool in hydrological modeling, offering significant improvements over traditional and contemporary approaches.

Hackathons and software competitions, increasingly pivotal in the software industry, serve as vital catalysts for innovation and skill development for both organizations and students. These platforms enable companies to prototype ideas swiftly, while students gain enriched learning experiences, enhancing their practical skills. Over the years, hackathons have transitioned from mere competitive events to significant educational tools, fusing theoretical knowledge with real-world problem-solving. The integration of hackathons into computer science and software engineering curricula aims to align educational proficiencies within a collaborative context, promoting peer connectivity and enriched learning via industry-academia collaborations. However, the infusion of advanced technologies, notably artificial intelligence (AI), and machine learning, into hackathons is revolutionizing their structure and outcomes. This evolution brings forth both opportunities, like enhanced learning experiences, and challenges, such as ethical concerns. This study delves into the impact of generative AI, examining its influence on student's technological choices based on a case study on the University of Iowa 2023 event. The exploration provides insights into AI's role in hackathons, and its educational implications, and offers a roadmap for the integration of such technologies in future events, ensuring innovation is balanced with ethical and educational considerations.

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research which incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.