Abstract. General public participating in research design, data collection or analysis process is often referred to as citizen science, and when digital means are involved, it’s defined as crowdsourcing. This thesis project is aimed at examining the feasibility and potential of using citizen science/crowdsourcing for hydrological modelling. The research project revolves around developing a user friendly crowdsourcing mobile application for gathering data from the citizens, which will be specific to urban flooding data, river ice data, lake water quality data and vegetation condition data.
The registered users are able to register on the application and upload data in the form of reports, which will be in text form and also attach images of the situation. In the end, we utilize the text reports uploaded by users regarding urban flooding to extract useful hydrological insights, that could be used for updating already existing hydrological models as well as create new hydrological models using NLP. The results indicate that it is possible to extract useful insights from the data reports submitted by the citizen scientists, which could be further used for updating hydrological models or maybe set alerts for the hydrologists in case of important hydrological updates
