Flooding is the most common natural hazard worldwide. Severe floods can cause significant
damage and sometimes loss of life. During a flood event, hydraulic models play an important
role in forecasting and identifying potential inundated areas, where emergency responses
should be deployed. Nevertheless, hydraulic models are not able to capture all of the
processes in flood propagation because flood behaviour is highly dynamic and complex.
Thus, there are always uncertainties associated with model simulations. As a result, near-real
time observations are required to incorporate with hydraulic models to improve model
forecasting skills. Crowdsourced (CS) social media data presents an opportunity for
supporting urban flood management as it can provide insightful information collected by
individuals in near real-time.
In this thesis, approachesto maximise the impact of CS social media data (Twitter) to reduce
uncertainty in flood inundation modelling (LISFLOOD-FP) through data assimilation were
investigated. The developed methodologies were tested and evaluated using a real flooding
case study of Phetchaburi city, Thailand. Firstly, two approaches (binary logistic regression
and fuzzy logic) were developed based on Twitter metadata and spatiotemporal analysis to
assess the quality of CS social media data. Both methods produced good results, but the
binary logistic model was preferred as it involved less subjectivity. Next, the generalized
likelihood uncertainty estimation methodology was applied to estimate model uncertainty
and identify behavioural parameter ranges. Particle swarm optimisation was also carried out
to calibrate for an optimum model parameter set. Following this, an ensemble Kalman filter
was applied to assimilate the flood depth information extracted from the CS data into the
LISFLOOD-FP simulations using various updating strategies. The findings show that the
global state update suffers from inconsistency of predicted water levels due to overestimating
the impact of the CS data, whereas a topography based local state update provides
encouraging results as the uncertainty in model forecasts narrows, albeit for a short time
period. To extend the improvement time span, a combination of state and boundary updating
was further investigated to correct both water levels and model inputs, and was found to
produce longer lasting improvements in terms of uncertainty reduction. Overall, the results
indicate the feasibility of applying CS social media data to reduce model uncertainty in flood
forecasting
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