Reliable flood forecasting systems are essential for predicting and mitigating the impact of flooding worldwide. However, minimising flood forecast uncertainties remains a challenging task due to many sources of uncertainty in underlying flood simulation modelling. Such uncertainties can be reduced by employing data assimilation techniques to dynamically incorporate the most recent available observations into the system while accounting for existing uncertainties in both models and observations. However, traditional observations often lack the necessary temporal or spatial resolution, limiting the adoption of data assimilation methods for real-time applications. In contrast, data collection through crowdsourcing has grown in popularity with the potential to provide high spatiotemporal resolution data, especially in urban areas. Nevertheless, the use of crowdsourcing is still impacted by validation uncertainties and data quality, which makes it a complementing data to traditional observations rather than an alternative data source. This paper presents a novel methodology for assimilating crowdsourced social media data to improve a 2D flood forecasting model through various update strategies. The methodology was tested against a real case flood event of the 2017 Phetchaburi flood (Thailand), and the performance of different update strategies was evaluated with reference to the calibrated model output obtained from a particle swarm optimisation algorithm. Empirical results demonstrate that global state updates suffer from inconsistencies in predicted water levels, whereas topographically based local state updates provide encouraging results. Specifically, the improvement due to the local state update alone is short-lived, and findings indicate that a longer lasting improvement in flood forecasting performance can be achieved through a combination of both state and boundary updates. Overall, the results indicate the feasibility of utilising crowdsourced social media data to improve the performance of flood forecasting systems for urban environments.

可靠的洪水预报系统对于预测和减轻全球洪水的影响至关重要。然而，由于潜在洪水模拟模型中的许多不确定性来源，最大限度地减少洪水预报的不确定性仍然是一项具有挑战性的任务。通过采用数据同化技术将最新的可用观测动态纳入系统，同时考虑模型和观测中现有的不确定性，可以减少此类不确定性。然而，传统的观测往往缺乏必要的时间或空间分辨率，限制了数据同化方法在实时应用中的采用。相比之下，通过众包收集数据越来越受欢迎，有可能提供高时空分辨率数据，尤其是在城市地区。尽管如此，众包的使用仍然受到验证不确定性和数据质量的影响，这使其成为传统观察的补充数据，而不是替代数据源。本文提出了一种吸收众包社交媒体数据以通过各种更新策略改进二维洪水预报模型的新方法。该方法针对 2017 年 Phetchaburi 洪水（泰国）的真实案例洪水事件进行了测试，并参考从粒子群优化算法获得的校准模型输出评估了不同更新策略的性能。实证结果表明，全球状态更新受到预测水位不一致的影响，而基于地形的局部状态更新提供了令人鼓舞的结果。具体来说，仅局部状态更新带来的改善是短暂的，研究结果表明，通过结合状态和边界更新，可以实现更持久的洪水预报性能改善。总体而言，结果表明利用众包社交媒体数据提高城市环境洪水预报系统性能的可行性。