@mastersthesis {bnh-8092, title = {Optimising SAR-based flood extent assimilation for improved hydraulic flood inundation forecasts}, volume = {Doctor of Philosophy}, year = {2020}, month = {05/2020}, pages = {363}, school = {Monash University}, address = {Melbourne}, abstract = {

Accurate forecasts of flood inundation are vital to effective flood rescue, response, and resource allocation. However, uncertainty in inputs, boundaries, and parameters necessitate the use of independent observations to constrain flood predictions. Radar remote sensing allows the synoptic and systematic coverage of flooded areas and is thus a valuable resource for more accurate flood forecasts when combined with models. Accordingly, this thesis first improved the satellite-based probabilistic flood extent observation, and then designed a novel likelihood function to integrate such observations with flood model estimates yielding improved flood inundation forecasts.

}, keywords = {crowdsourcing, data assimilation, flood forecasting, flood inundation mapping, flood inundation modelling, hydraulic modelling, mutual information, sensitivity analysis, synthetic aperture radar, uncertainty reduction}, url = {https://bridges.monash.edu/articles/thesis/Optimizing_SAR-based_Flood_Extent_Assimilation_for_Improved_Hydraulic_Flood_Inundation_Forecasts/12375188}, author = {Antara Dasgupta} } @article {bnh-5394, title = {Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions}, journal = {Reviews of Geophysics}, volume = {56}, year = {2018}, month = {12/2018}, chapter = {698}, abstract = {

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

}, keywords = {big data, categorisation, crowdsourcing, data collection, geophysics}, doi = {https://doi.org/10.1029/2018RG000616}, url = {https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018RG000616}, author = {Feifei Zheng and Ruoling Tao and Holger Maier and Linda See and Dragan Savic and Tuqiao Zhang and Qiuwen Chang and Thaine H. Assump{\c c}{\~a}o and Pan Yang and Bardia Heidari and J{\"o}rg Rieckermann and Barbara Minsker and Weiwei Bi and Ximing Cai and Dimitri Solomatine and Ioana Popescu} }