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Over the past half century, flooding has been the leading form of environmental disaster around the world. The biggest issue for flood risk management in urban areas is the prediction that under climate change there will be considerably more flooding in these areas (Tkachenko, et al., 2016).
This research considers the key issue of urban flooding. Figure 1 represents the flood risk data of each LGA showing their risk level. Around 28 percent of Australia’s gross domestic product and nearly a quarter of Australian population live in LGAs with high to extreme risk of flooding (IAG, 2016). Flooding can cause disruption to urban transport systems and can lead to disruption of economic activity in the short term while not causing much significant damage to the infrastructure. It is predicted that one day of disruption to transport networks in Sydney CBD can decrease the GDP by up to $30 million (IAG, 2016).
In case of Queensland, extreme flooding and Tropical cyclone Yasi had disastrous consequences for Queensland’s economy. The government spent $7 billion (Queensland Government, 2011) to support businesses and rebuild public infrastructure.
Also, a case study conducted by Molino Stewart for NSW Infrastructure (Molino Stewart, 2012) found that in case of a one in a 1,000-year flood occurrence in the Hawkesbury Nepean Valley could cost the government $8 billion in damages and could destroy 6,500 homes and flooding 14,000 more while displacing more than 40,000 people. This could also affect economic export activity by disrupting the Main Western rail line.
Figure 1: Risk Score by LGA. Source: SGS(2016) based on IAG data.
Research shows that visualization through computer simulations are more efficient when compared to 2D drawings (Serginson, et al., 2013), but on-site observations lead to lesser misconceptions as they are enhanced by acoustic and haptic experiences (Wergles & Muhar, 2009). Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), was created so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process (Harmon, et al., 2016). The characteristics of immersivity and interactivity helps in assessing urban environments by urban planners (Piga & Morello, 2015).
This study investigated the usefulness of Augmented Reality (AR) Sandbox, a tool on which Tangible Landscape was developed, in simulating floods on an urban landscape. A team of researchers at the UC Davis W.M. Keck Center for Active Visualization in the Earth Sciences (KeckCAVES, 2015) created the AR Sandbox in part to address the difficulty of topographic map interpretation. The tool’s projector overlays elevation information in the form of contour lines onto a physical sandbox. As the user moves the sand to create 3D terrain models, the system senses the elevation adjustments and in real time, redraws the elevation information thus allowing the user to understand and replicate real world topography in urban planning and design hydrology (Petrasova, et al., 2015) which some might find challenging to comprehend. It helped the users to visualize and analyze urban issues like flooding, surface water modelling and watershed analysis. The AR Sandbox enables the user in visualizing real time simulations based on the changes made helping in live interaction with the sandbox. Users have reported increased 3D spatial cognition and gained a better understanding of the water flow compared to a 2D model of the topography (Harmon, et al., 2016; Petrasova, et al., 2015). This study fills the gap in the existing literature on the accuracy of the water flow model developed to facilitate practical applications in surface water modelling and urban flood simulations on the AR Sandbox.
1.2 Coogee Bay Area: Case Study
The topography of the Coogee Bay area is mostly steep with the northern 80% of it forming a single catchment and the southern 20% forming several smaller, less well-defined catchments. All these catchments drain east of the sea (BMT WBM Pty Ltd, 2013). The natural creek(stream) bed has been heavily modified, and the area is entirely drained by a stormwater pipe network. But it has been reported that if the capacity of the network exceeds the designed capacity then overland flow occurs along the original creek bed, with many of them located through developed properties facing a significant flood risk. Land use within the study area primarily consists of urban development (90%), open recreational space (9%) and tree-covered land (1%) (BMT WBM Pty Ltd, 2013).
The May 2009 flooding event in the Coogee Bay area recorded a peak flood of 10.7m AHD (Australian Height Datum) from Dolphin Street, down Mount Street and through to the Bowling Club submerging all the cars parked on the road. At Oswald Street in the same area, a 23.1m AHD flood level was recorded. The properties on that road were damaged due to scouring caused by the floor water flowing at high velocity (BMT WBM Pty Ltd, 2013). Figure 2 shows the flooding at Coogee Bay Area.
Figure 2: Flooding at Coogee Bay Area. Source: (BMT WBM Pty Ltd, 2013)
1.3 Aim and Objectives
To measure and assess the use of AR Sandbox in simulating and visualizing flood impacts
Quantifying the use of AR Sandbox by identifying the flood prone areas in Coogee.
1.3.3 Research Question
The objectives of the thesis are
- Identification of flood prone areas in Coogee Bay.
- Predict flood prone areas/sectors in Coogee Bay.
- Simulate and visualize the impacts of flooding in the predicted flood prone area using AR Sandbox.
2. Literature Review
In this section the concept of Augmented Reality will be reviewed for a better understanding of the Augmented Reality Sandbox. Also, literature regarding floods, its causes and its affects will be reviewed.
2.1 Augmented Reality
Virtual Reality is a fully computer generated or artificial environment that completely immerses the user through sensory stimuli, making the real world inaccessible. Ivan Sutherland created a head-mounted display in 1968 making it the first functional virtual reality device. Augmented Reality (AR) allows a user to visualize virtual objects in the physical world by superimposing them in it and interact with it, allowing AR to supplement reality (Azuma, 1997). The term “Augmented Reality” was devised by Boeing Computer Services, Research, and Technology scientists while creating a tool that assisted in overlaying virtual information onto real objects (Caudell & Mizell, 1992). AR Devices are characterized by: (1) combination of real and virtual, (2) interactive in real time and (3) registered in 3D (Azuma, 1997).
In urban planning and design projects real environments is an influential part in the experience. This makes Augmented Reality different from Virtual Reality. With the emergence of smartphones, mobile AR has become quite popular due to the higher quality visuals its offering. It also gives the user an understanding of location, orientation and flow of information and the reality itself. AR is built on many other technologies and functions formed around it. It has many possibilities and gives the user the freedom to go to the location and have a virtually place a one to one scale 3D model in the environment. This gives the user the chance to experience the natural environment around him like the wind, light and smell. Figure 3 shows the system architecture of AR.
Figure 3: System Architecture of AR combining inertial and vision sensing (Corke, et al., 2007)
2.2 AR Sandbox
The first prototype of the AR Sandbox was created at UC Davis KeckCAVES. It allowed the users to create topographic models by shaping real sand on which an elevation color map, topographic contour lines, and simulated water are projected in real time. As the users interacted with sand, the Microsoft Kinect would detect any changes made to the model topography. This facilitates the projector to display colors and contour lines transform accordingly. When an object usually the users’ hand is sensed over the sand surface, virtual rain occurs on the surface below increasing the water, which directly affects the flow simulation across the landscape. The AR sandbox also simulates the drainage of water as if it was infiltrating the soil. It also has an option to drain the water rapidly with a push of a button. Since its development, the AR Sandbox has more than 100 versions of it in the U.S. and internationally (Reed, et al., 2014) (Terri.L.Woods, et al., 2016).
It was a project funded by the Lake Visualization 3D (LakeViz3D, 2015b) (Terri.L.Woods, et al., 2016), which is a National Science Foundation, as part of a collaboration among the UC-Davis W.M.Keck Center for Active Visualization in the Earth Sciences, UC-Davis Tahoe Environmental Research Center, Lawrence Hall of Science for Lake Champlain and Audience Viewpoints Consulting, with its objective to increase understanding of freshwater lake ecosystems using 3D visualizations. To solve this issue, the team built an intuitive device which is a hands-on sandbox exhibit, with kinetic sand, overlaid with virtual but interactive topography and virtual water created using a motion-sensing input device (Microsoft Kinect 3D camera), a projector to display the data onto the sandbox, and a visualization software. (Terri.L.Woods, et al., 2016). The figure below shows a typical arrangement of the AR Sandbox.
Figure 4: Typical arrangement of an AR Sandbox. Source: (University of California, Davis, 2014)
The water-flow simulation was created based on a set of Saint-Venant shallow water equations, which are a part of the depth-integrated version of Navier-Stokes equations governing fluid flow (Kurganov & Petrova, 2007). The boundary condition being the sand surface, helps the model to simulate in such a way that the water flows at real speed, assuming a 1:100 scale factor. (Terri.L.Woods, et al., 2016)
In studying the impact of new development on the surface water model, Petrasova, et al., 2015 utilized tangible landscape to calculate the flow accumalation and watersheds for the modified topography. There was an immediate change in the watershed boundaries in the developed area due to a new road acting as an artificial watershed boundary.
An event where water engulfs land which is normally dry is known as a flood. They are a natural process that can be caused by several factors and affected by human activities. (Queensland Government, 2017)
Flash floods are the floods that quickly rise in minutes or hours due to heavy and intense rainfall. They take place in small catchment areas. (Queensland Government, 2017)
A site’s maximum flood level and frequency can be determined using previous flood records and flood hazard maps based on hydraulic modelling which are available at the local council, local water authority, state/territory government department responsible for emergency planning. A site survey is recommended to improve the understanding of the nature of the topography between the potential threat which are the water bodies and the site. Flood hazard levels are too often not known by property owners and occupants. (Hancock & Rea, 2013)
The main sources of flooding that are likely to impact the site can include:
• Rivers, streams
• Sea/ storm surge
• Overland flow
• Damaged water mains
• Drains and sewers that are overloaded or blocked
With the increase in development due to urbanisation, flooding risk from rivers and streams also increased as the land available for the natural capacity of flood plains to drain/dissipate water has been reduced. Urbanisation has led to hard landscaping with impervious ground surfaces that leads to more water runoff by at least 3 to 5 times the original amount that occurs on a natural terrain. (Hancock & Rea, 2013) Flood prone land closer to Central Business District (CBD) is reconsidered for development as the land near the CBD gets scarcer.
Figure 5: Factors that can cause a flood. Source: (Queensland Government, 2017)
To predict flash flood and future flooding hazard more than rain records are needed. A site’s model is needed to understand the topography of the site. This facilitates the user to familiarize with the surface flow of the site, providing a check on the urban development plans to gain useful insights into the future development of the area. (Hancock & Rea, 2013)
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