Human Sensor Networks for Improved Modeling of Natural Disasters | Zendy

Oleg Aulov | Zendy; Milton Halem | Zendy

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Human Sensor Networks for Improved Modeling of Natural Disasters

Author(s) -

Oleg Aulov,

Milton Halem

Publication year - 2012

Publication title -

proceedings of the ieee

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.383

H-Index - 287

eISSN - 1558-2256

pISSN - 0018-9219

DOI - 10.1109/jproc.2012.2195629

Subject(s) - general topics for engineers , engineering profession , aerospace , bioengineering , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , fields, waves and electromagnetics , geoscience , nuclear engineering , robotics and control systems , signal processing and analysis , transportation , power, energy and industry applications , communication, networking and broadcast technologies , photonics and electrooptics

In this paper, we present a novel approach that views social media (SM) data as a human sensor network. These data can serve as a low-cost augmentation to an observing system, which can be incorporated into geophysical models together with other scientific data such as satellite observations and sensor measurements. As a use case scenario, we analyze the Deepwater Horizon oil spill disaster. We gather SM data that mention sightings of oil from Flickr, geolocate them, and use them as boundary forcings in the General NOAA Oil Modeling Environment (GNOME) software for oil spill predictions. We show how SM data can be incorporated into the GNOME model to obtain improved estimates of the model parameters such as rates of oil spill, couplings between surface winds and ocean currents, diffusion coefficient, and other model parameters.

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