|
Our new paper entitled, "Simulating flood risk in Tampa Bay using a machine learning driven approach" is published in npj: Natural Hazards. Below is the abstract: "Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study’s goal is to simulate flood risk and identify dominant FRFs using historical flood damage data as target variable, with 16 FRFs as predictor variables. Five different ML models such as decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and random forest (RF) were adopted. RF classifies 2.42% of Tampa Bay as very high risk and 2.54% as high risk, while XGBoost classifies 3.85% as very high risk and 1.11% as high risk. Moreover, the communities reside at low altitudes and near the waterbodies, with dense man-made infrastructure, are at high flood risk. This study introduces a comprehensive framework for flood risk assessment and helps policymakers mitigate flood risk."
2 Comments
9/18/2024 0 Comments My new analysis article In my analysis article published in the Conversation, I argue that the increasing damages caused by hurricanes are partly due to climate change effects and partly due to growing coastal population.
"Warm water in the Atlantic Ocean and Gulf of Mexico can fuel powerful hurricanes, but how destructive a storm becomes isn’t just about the climate and weather – it also depends on the people and property in harm’s way. In many coastal cities, fast population growth has left more people living in areas at high risk of flooding." Our new paper on comprehensive flood risk analysis integrating flood susceptibility and social vulnerability is published in the Journal of Geovisualization and Spatial Analysis. Please find the abstract below: "Due to climate change, the frequency and intensity of floods have dramatically increased worldwide. The innate social inequality has been exposed and even exacerbated by increasing flooding. It is imperative to assess flood risk in a comprehensive manner, accounting for both physical exposure and social vulnerability. Harris County in Texas, U.S., is selected as the study area as it has experienced a few devastating floods in recent history, with Hurricane Harvey (2017) being the most impactful. First, this study generates a flood susceptibility map (FSM) by applying a Random Forest (RF) model with 500 flood inventory points and 12 flood conditioning factors. Then, it generates a social vulnerability map (SoVM) by applying Principal Component Analysis (PCA) with ten social variables at the census tract level. Finally, it combines FSM with SoVM to produce a flood risk map (FRM) of Harris County. The findings of this study demonstrate that 9.06% of the area of Harris County has high flood susceptibility and 1.45% of the area has a very high social vulnerability. Combining both flood susceptibility and social vulnerability, this study reveals that 5.59% of the total area has a very high risk for flooding. This study further compares the FRM with the Federal Emergency Management Agency’s (FEMA) 100-year floodplain map and notes major differences. The comparison reveals that 76.7% of very high and 81.8% of high-risk areas in FRM are underestimated by the FEMA 100-year floodplain. This study produces a comprehensive FRM, highlighting areas where flooding can exacerbate social inequality and cause higher economic costs. FEMA’s 100-year floodplain map underestimates a significant portion of high-risk areas suggesting that current zoning and development policy may fail to consider flood risks adequately."  11/17/2023 2 Comments Our new paper on changing community vulnerability in the U.S. Mobile Bay from 2000 - 2020Our new paper is published in the journal Applied Spatial Analysis and Policy. Below is the abstract:
"The coastal community is confronted with heightened risks posed by climate change. Mobile Bay in the United States is a large estuarine system along the Gulf of Mexico (GOM) coast, providing critical ecosystem services for the nation. This region is however subject to increased urbanization and uncertain impacts of climate change. To ensure sustainability of this important ecosystem, it is imperative to examine the changing spatial patterns of community vulnerability to environmental changes in this region. Using data from the U.S. Census of multiple years, we investigate the changing spatial patterns of social vulnerability at the census block group level in Mobile Bay consisting of Mobile County and Baldwin County over the past 20 years (2000 – 2020). Additionally, we utilize hotspot and cluster analyses to formalize the observations of the spatiotemporal changes. Further, we examine how land use and land cover (LULC) changes co-occur with social vulnerability changes across Mobile Bay. We identify several hotspots where land cover has been converted to urban land and social vulnerability has increased. The investigation of the spatial patterns over a relatively long period helps to deepen the insight into the dynamic spatiotemporal changes of social and environmental vulnerability. This insight can better inform future plans to cope with climate change and ensure sustainability. Specifically, hotspots that have undergone urbanization and increased social vulnerability demand special attention from policy makers for future risk mitigation and disaster planning." 5/30/2023 0 Comments Our new paper was published in the International Journal of Disaster Risk ReductionOur new paper identifying effective hurricane risk communication tools was published in the International Journal of Disaster Risk Reduction. Below please find the abstract:
"Coastal regions such as the U.S. Atlantic and Gulf Coasts are highly vulnerable to extreme coastal hazards such as tropical cyclones and major hurricanes. The effects of these hazards pose a threat now and are expected to increase in the future, which highlights the need for coastal communities to receive and understand information regarding risks involved with these hazards. Through this study, we identify points of improvement in the tools used to communicate the short and long-term risk associated with hurricane hazards through three surveys in Mobile, AL, Savannah, GA, and Houston TX. These surveys identify public response to hurricane descriptions, Cone of Uncertainty graphics, and long-term trend graphics. Analysis of trends in responses to these communication tools identifies relationships between risk perceptions and existing factors in each study location. Further, public response to these tools is identified and analyzed using structural equation models for each location with a “response” latent variable containing information from endogenous variables in the survey. Response was measured as action intent, concern for the scenario, reported evacuation likelihood, and interpretation of long-term trends. We identify points of improvement for all three communication tools to aid in public comprehension of the information provided as well as to increase response to hurricane hazards by more effectively communicating risk information. These would help to improve comprehension and increase different responses to tropical storm and hurricane damage from high winds and storm surge with the intent to improve resident response to hazards along the U.S. Atlantic and Gulf Coasts." 1/18/2023 0 Comments Our new paper using social media data to detect public response to hurricanes and climate change was published in Climate Risk ManagementOur new paper titled "Toward reduction of detrimental effects of hurricanes using a social media data analytic approach: How climate change is perceived?" has been published in Climate Risk Management. Below please find the abstract:
"During natural disasters, there is a noticeably increased use of social media sites such as Twitter. Substantial research on social media data use during disasters has been conducted in the past decade since various social media platforms have emerged and gained popularity. This research highlights a thorough examination of the textual content of users’ posts shared on Twitter across the 48 contiguous U.S. states (CONUS) during hurricanes Harvey (2017) and Dorian (2019). We processed and analyzed 35 million tweets by classifying them into the main topics of concern discussed on Twitter over the CONUS. Sentiment analysis, topic modeling, and topic classification are a few of the Artificial Intelligence techniques from Natural Language Processing (NLP) that we employed in this work to analyze the Twitter data. Applying the NLP techniques on this large volume of data, made it possible to classify the tweet content into distinct categories in order to reveal valuable information on social response to hurricanes and assist crisis management agencies and disaster responders during and post disasters. Furthermore, this study offers helpful insights on the way climate change is discussed on Twitter before, during and after hurricane Harvey and Dorian. The outcome of this study uncovers detailed information on social response to hurricanes which benefits disaster managers and responders in reducing the detrimental effects of such extreme events and enhancing community readiness when these events occur." 8/31/2022 2 Comments Our new paper on the socio-geographic patterns of rescue requests during Hurricane Harvey has been published in Findings Our paper on the socio-geographic patterns of rescue requests during Hurricane Harvey has been published in Findings. Below is the abstract:
"We analyze a public dataset of rescue requests for the Houston Metropolitan Area during Hurricane Harvey (2017) from the Red Cross. This dataset contains information including the location, gender, and emergency description in each requester’s report. We reveal the spatial distribution of the rescue requests and its relationship with indicators of the social, physical, and built environment. We show that the rescue request rates are significantly higher in regions with higher percentages of children, male population, population in poverty, or people with limited English, in addition to regions with higher inundation rate or worse traffic condition during Hurricane Harvey. The rescue request rate is found to be statistically uncorrelated with the percentage of flood hazard zone designated by the Federal Emergency Management Agency (FEMA)." 8/2/2022 0 Comments Open PhD Position – coastal community resilience, risk perceptions, community engagement, Nature Based Solution The Environmental Decision Making Lab at the Department of Geography of the University of Alabama seeks a geography PhD student to focus on coastal community resilience, risk perceptions, community engagement under the theme of Nature Based Solution (NBS). The broader research team is focused on developing actionable design guidance for NBS (i.e., wetland restoration) along the US Gulf Coast. Our highly interdisciplinary group includes social scientists, wetland ecologists, water resource engineers, and government agency partners. Our goal is to develop guidance for wetland restoration activities optimized to reduce flooding and increase coastal community resilience. To accomplish this goal, we will employ a combination of community engagement, wetland plant community characterization, and state-of-the-art hydrologic and hydraulic modeling.
The successful candidate will be expected to start in spring, 2023. The candidate will work closely with social scientists, wetland ecologists, and water resource engineers, and our government partners to develop, assess, and communicate NBS design alternatives by engaging stakeholders in a knowledge co-production fashion. The candidate will be expected to work with the team to develop a plan for stakeholder engagement meetings, organize and facilitate stakeholder engagement activities, collect the data from the meetings, analyze the data, and report findings in peer-reviewed manuscripts. Through this work, the candidate will also be expected to develop hypothesis driven research based on their interests. The ideal candidate will have MS degrees in a relevant field (i.e., geography, urban and regional planning, environmental sociology, ecology, environmental science, or closely related field). The candidate should be excited about working on an interdisciplinary team; interacting with community partners, and conducting both basic and applied research. Further, experience with statistical analysis and programs (e.g., R, Stata, SPSS) and geographic information systems (e.g., ArcGIS, QGIS) are required. Experience with textual analysis programs (e.g., NVivo) is preferred but not required. Additionally, experience with scripting languages (e.g., R, Python, or Matlab) are preferred but not required. For more information, please contact Dr. Wanyun Shao ([email protected]) 3/31/2022 0 Comments Our new paper has been published in the International Journal of Disaster Risk Reduction Our paper has been published in the International Journal of Disaster Risk Reduction (Impact factor: 4.32). Below please find the abstract:
"Climate change has posed serious risks to coastal cities around the world. Effective urban disaster management calls for the coordination between the local government and residents. We propose a comprehensive framework to study urban disaster resilience under climate change with New Orleans of Louisiana in the U.S. as the study area. Municipal hazard mitigation must be sufficient to mitigate these hazards. Residents’ risk perceptions are a vital component of social vulnerability and can shape public decisions to increase disaster resiliency. Because climate change is expected to intensify, it becomes important to ensure that residents’ risk perceptions are considered when developing municipal plans to maximize regional resiliency. This research aims to identify a gap in the hazard mitigation process that can be closed to better prepare the community to manage coastal hazards. To achieve this, an online survey is distributed in the New Orleans metropolitan area to determine residents’ risk perceptions and expectations of the local government’s action. Policy analysis is conducted to identify the priorities held by municipal planners in these issues. Although there is no gap in the perception of risk and municipal mitigation of current coastal hazards, there is a gap between the municipal approach to climate change mitigation and the concern and expectation of actions the residents hold regarding the future effects of climate change. The approach to climate change should be reconsidered on a municipal level and new small-scale personal incentives should be promoted to maximize resiliency toward coastal hazards in the future." Our paper on community vulnerability to floods and hurricanes in the Gulf Coast has been recognized as the most cited paper in the journal Disasters.  |
|
RSS Feed