River management projects involve multiple stakeholders with different interests, priorities, and constraints. As these stakeholders could have conflicting perspectives, river management entails complex decision-making processes. While technical expertise is essential, students in higher education should also develop an understanding of the societal impacts of engineering interventions. Integrating such skills into engineering curricula is crucial. One effective method is the use of serious games, which simulate and simplify the realworld complexities (Kriz, 2003). These games provide a safe space for students to explore practical challenges without facing real-world consequences (Freese et al., 2020). Serious games are also proven to enhance the learning outcomes and increase motivation, even in professional contexts (Bekebrede and Champlin, 2022).

We developed Facing Floods, a game designed for students in higher education to simulate the challenges of river management projects. Players take on the roles of stakeholders, each with specific goals, needs, and budgets. Through discussion and negotiation, they must balance individual objectives with the shared responsibility of effective river management.

Braided rivers are the most dynamic type of rivers, with a rapid and intricate morphological evolution (Stecca et al., 2019). Being able to predict where and how rivers evolve is crucial for supporting spatial-related decisionmaking processes in the vicinity of these rivers. However, a limited understanding and inadequate algorithm implementation of specific morphological processes limits the prediction capabilities of physics-based models (Jagers, 2003; Siviglia and Crosato, 2016). The design of structures, infrastructure, and other interventions is consequently hampered at the expenses of the popoulation safety. In recent years artificial intelligence techniques rapidly gained popularity across different contexts (Blake et al., 2021) and the availability of satellite images increased. This research sets a novel attempt to predict the planform evolution of braided rivers by means of a deeplearning algorithm and using satellite images. The Brahmaputra-Jamuna River, in India and Bangladesh, was selected as case study (Best et al., 2022).

Braided rivers are the most dynamic type of rivers, with a rapid and intricate morphological evolution. A limited understanding and inadequate algorithm implementation of specific morphological processes limits the prediction capabilities of physics-based models. The design of structures, infrastructure, and other interventions is consequently hampered. In recent years artificial intelligence (AI) techniques rapidly gained popularity across different contexts. Additionally, the availability of satellite images increased. This research sets a novel attempt to predict the planform evolution of braided rivers by means of deep learning and satellite images. The Brahmaputra-Jamuna River, in India and Bangladesh, was selected as case study. A convolutional neural network (CNN) with U-Net architecture was developed. The model was trained with the Global Surface Water Dataset (GSWD). The goal of the model was to classify each pixel as either "Non-water" or "Water". Four images, representative of the same month over four consecutive years, were used as input. The fifth-year image represented the target. The model demonstrated good skills in predicting the planform development. Processes like the migration of meanders, the abandonment of channels, and the evolution of confluences and bifurcations were often well captured. However, a lack of temporal patterns was noticed. More complex phenomena, like the formation and shifting of channels, were never predicted. The total areas of erosion and deposition were constantly underpredicted. Metrics such as precision, recall, F1-score, and critical success index (CSI) were tracked. Overall, our model achieved a 5-6% total improvement of these metrics compared to the benchmark method for which no morphological change is assumed to occur. Our model could be useful as a preliminary tool for water management authorities in India and Bangladesh. It can support the prioritisation of bank protection measures in areas subject to erosion or land reclamation projects in areas subject to deposition and assist inland navigation. Given the inherent tendency of the model to underpredict erosion, caution is always advised. More research is required to improve the current model. Despite this, deep-learning modelling could become a potentially valuable field of research. Testing alternative model architectures, increasing the datasets size, and incorporating additional data, such as water levels or river discharge, are some of the proposed strategies to improve the model performance.

"Plastic pollution is a threat for all ecosystems due to its effects on people, animals, and environment. Rivers are estimated to transport around 0.5 millions tons of plastic per year. When plastic enters a river system, it is transported downstream towards the sea but it is also likely to accumulate at specific cross sections and locations, including hydraulic structures, eventually increasing the risk of floods. Gates, locks, weirs, and bridges are commonly present in rivers and canals and have several functions, including water level regulation, flood safety, and inland water shipping. These can also be found in water treatment plants, hydropower stations as well as debris/plastic collection systems. Riverine plastic accumulation is also known to cause geomorphic changes. In-depth knowledge on how plastic particles accumulate upstream of hydraulic structures is therefore crucial to understand the processes that affect plastic transport, its influence on the safety and functionality of hydraulic structures and their effects on the hydro- and morphodynamic conditions of the flow. In this research experiments were performed using simplified plastic particles to analyse the processes that lead to the instability of accumulated particles upstream of a simple gate."