Pranavesh Panakkal is a Postdoctoral Researcher in the Department of Civil and Environmental Engineering at Rice University. His work with Dr. Jamie Ellen Padgett focuses on situational awareness and smart resilience.
On situational awareness, he develops algorithms and tools to predict and sense the performance of urban infrastructure systems to stressors such as natural disasters to inform emergency response and recovery. An example application would be real-time sensing of flood impacts on roadways to inform emergency response navigation.
His work on smart resilience aims to leverage diverse data sources, physics-based data-informed algorithms, and equity-aware models to predict, sense, and respond to acute stressors to enhance community resilience. An example application would include digital twins that can aid in informing preparedness and risk-informed decision-making during flood disasters.
Reliable sensing of road conditions during flooding can facilitate safe and efficient emergency response, reduce vehicle-related fatalities, and enhance community resilience. Existing situational awareness tools typically depend on limited data sources or simplified models, rendering them inadequate for sensing dynamically evolving roadway conditions. Consequently, roadway-related incidents are a leading cause of flood fatalities (40%–60%) in many developed countries. While an extensive network of physical sensors could improve situational awareness, they are expensive to operate at scale. This study proposes an alternative—a framework that leverages existing data sources, including physical, social, and visual sensors and physics-based models, to sense road conditions. It uses source-specific data collection and processing, data fusion and augmentation, and network and spatial analyses workflows to infer flood impacts at link and network levels. A limited case study application of the framework in Houston, Texas, indicates that repurposing existing data sources can improve roadway situational awareness. This framework offers a paradigm shift for improving mobility-centric situational awareness using open-source tools, existing data sources, and modern algorithms, thus offering a practical solution for communities. The paper’s contributions are timely: it provides an equitable framework to improve situational awareness in an epoch of climate change and exacerbating urban flood risk.
@article{panakkal2024more,title={More eyes on the road: Sensing flooded roads by fusing real-time observations from public data sources},author={Panakkal, Pranavesh and Padgett, Jamie Ellen},journal={Reliability Engineering \& System Safety},pages={110368},year={2024},publisher={Elsevier},}
2023
ISLAND: Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator
Yuhao Liu, Pranavesh Panakkal, Sylvia Dee, and 3 more authors
Cloud occlusion is a common problem in the field of remote sensing, particularly for thermal infrared imaging. Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from Earth’s surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable thermal images for downstream applications, making it impractical to perform intricate time-series analysis of land surface temperature (LST). In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator. Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler’s first law of Geography, ISLAND predicts occluded brightness temperature and LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND thermal infrared and LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.
@article{liu2023island,title={ISLAND: Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator},author={Liu, Yuhao and Panakkal, Pranavesh and Dee, Sylvia and Balakrishnan, Guha and Padgett, Jamie and Veeraraghavan, Ashok},journal={arXiv preprint arXiv:2309.12416},year={2023},}
Safer this way: Identifying flooded roads for facilitating mobility during floods
Pranavesh Panakkal, Allison M. Wyderka, Jamie E. Padgett, and 1 more author
Severe storms and associated flooding pose a significant risk to urban mobility. Consequently, 40 to 63 percent of flood-related deaths are linked to roadway-related incidents in developed countries. The dynamic nature of flooding and the lack of real-time information make it challenging to sense flooding and its impact on roadways. Hence, existing state-of-the-art methods fall short of providing a robust, reliable, and affordable tool to facilitate situational awareness during storms. Such a tool is indispensable to aid emergency response, especially considering the potential increase in risk to flood exposure due to climate change and other factors. This study addresses this need by providing an open-source framework that couples real-time rainfall data, a physics-based flood model, and network and spatial analyses to sense real-time flood impact on the road transportation system. Case studies using three recent storms in Houston, Texas demonstrate the framework’s ability to provide vehicle-class specific roadway conditions for even minor roads and residential streets—a problem existing approaches struggle with. Aside from road-link conditions, the framework can also estimate network-level flood impacts, such as identifying regions without access to critical facilities like hospitals, giving decision-makers a more holistic view of network performance. Further, the framework is interoperable with existing situational awareness tools and could augment their ability to sense road conditions during flooding. Finally, the proposed framework can equip flood-prone communities and emergency responders with reliable and accessible situational awareness content using open-source tools and data to promote safer mobility during flooding—a key goal of intelligent transportation systems.
@article{PANAKKAL2023130100,title={Safer this way: Identifying flooded roads for facilitating mobility during floods},journal={Journal of Hydrology},pages={130100},year={2023},issn={0022-1694},doi={https://doi.org/10.1016/j.jhydrol.2023.130100},author={Panakkal, Pranavesh and Wyderka, Allison M. and Padgett, Jamie E. and Bedient, Philip B.},}
Sensing Flooded Roads to Support Roadway Mobility during Flooding: A Web-Based Tool and Insights from Needs Assessment Interviews
Reliable sensing of roadway conditions during flooding is a long-standing, challenging problem with societal importance for roadway safety. Tools that provide real-time data on road conditions during floods can facilitate safer mobility, reduce vehicle-related drownings, enhance flood response efficiency, and support emergency response decision-making. Following the tenets of user-centered design, such tools ideally should address the needs of diverse stakeholders involved in flood response. Currently, the existing literature lacks a thorough understanding of stakeholder needs to guide situational awareness tool development in the area of roadway mobility during flood events. This paper addresses this gap by studying the needs of stakeholders responsible for managing flood response in Houston. Semi structured one-on-one interviews were conducted with stakeholders from different Houston-based organizations responsible for managing and responding to flood hazard events in the downtown metropolitan area. Interview responses were systematically analyzed to identify (1) data needs for facilitating efficient and safe emergency response, (2) the most and least valuable information available during flooding, (3) communication and visualization strategies, (4) factors influencing stakeholder trust, and (5) factors influencing occupational stress during flood response. Finally, interview insights were used to develop a conceptual situational awareness framework and a prototype map-based tool that provides real-time road condition data during flood events. This study elucidates vital information for improving existing tools and providing preliminary guidance for future mobility-centric situational awareness tools that promote safer mobility and facilitate emergency response decision-making during flooding. Although the study focused on Houston, insights gained may be useful for comparable flood-prone regions. In developed countries, 40–60 percent of flood fatalities are attributed to vehicle-related incidents. Flooded roads and lack of real-time road condition data pose safety risks to first responders and reduce emergency response efficiency. Understanding stakeholder needs and developing tools that address them are essential for improving the safety and efficiency of emergency response, especially considering a potential increase in flood risk to urban mobility due to climate change and other factors. Following the tenets of the user-centered design process, this study identified stakeholder needs, conceptualized a framework for sensing road conditions, and developed an open-source prototype tool in the context of flood response in Houston. Insights gained in this study can improve the efficacy of existing mobility-centric situational awareness tools and provide preliminary guidance for quick prototyping of new situational awareness tools. Furthermore, organizations can use the insights presented here to help reduce work-related stress among emergency response personnel, thereby improving emergency response efficiency and organizational resilience.
@article{panakkal_sensing_2023,author={Panakkal, Pranavesh and Fattoracci, Elisa S. M. and Padgett, Jamie E. and King, Danielle D. and Yoo, Teddi},title={Sensing Flooded Roads to Support Roadway Mobility during Flooding: A Web-Based Tool and Insights from Needs Assessment Interviews},journal={Natural Hazards Review},volume={24},number={4},pages={04023039},year={2023},doi={10.1061/NHREFO.NHENG-1753},url={https://ascelibrary.org/doi/abs/10.1061/NHREFO.NHENG-1753},}
