|
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."  In a new paper that is published in Natural Hazards, we applied multiple algorithms to model flood susceptibility in New Orleans. Please find the abstract below: "Machine learning (ML) models, particularly decision tree (DT)-based algorithms, are being increasingly utilized for flood susceptibility mapping. To evaluate the advantages of DT-based ML models over traditional statistical models on flood susceptibility assessment, a comparative study is needed to systematically compare the performances of DT- based ML models with that of traditional statistical models. New Orleans, which has a long history of flooding and is highly susceptible to flooding, is selected as the test bed. The primary purpose of this study is to compare the performance of multiple DT-based ML models namely DT, Adaptive Boosting (AdaBoost), Gradient Boosting (GdBoost), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) models with a traditional statistical model known as Frequency Ratio (FR) model in New Orleans. This study also aims to identify the main drivers contributing to flooding in New Orleans using the best performing model. Based on the most recent Hurricane Ida-induced flood inventory map and nine crucial flood conditioning factors, the models’ accuracies are tested and compared using multiple evaluation metrics. The findings of this study indicate that all DT-based ML models perform better compared to FR. The RF model emerges as the best model (AUC = 0.85) among all DT-based ML models in every evaluation metrics. This study then adopts the RF model to simulate flood susceptibility map (FSM) of New Orleans and compares it with the prediction of FR model. The RF model also demonstrates that low elevation and higher precipitation are the main factors responsible for flooding in New Orleans. Therefore, this comparative approach offers a significant understanding about the advantages of advanced ML models over traditional statistical models in local flood susceptibility assessment."  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." 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]) Our new paper on perceptions of sea level rise has been published in Climatic Change (Impact factor: 4.743). Below please find the abstract:
"Sea level rise (SLR) in the 21st century poses fundamental risks to coastal residents. The U.S. Gulf of Mexico Coast (Gulf Coast) is among the regions experiencing the most rapid relative SLR. Beyond its increasing exposure to SLR and related coastal flooding, the Gulf Coast is home to a large population and displays high social vulnerability. How the coastal population in this vulnerable region perceives the impending risks posed by SLR warrants further examination. Do coastal residents’ perceptions of SLR conform to the scientific projections? We adopt an integrative approach based on a 2019 survey merged with contextual data including percentage of population living within the Special Flood Hazard Area (SFHA) and social vulnerability at the county level, both of which are extracted from the Centers for Disease Control and Prevention. We find that public risk perceptions of sea level change are influenced by political predisposition, with Republicans being less likely than Democrats to expect SLR in the future. Moreover, SLR remains temporally distant issue among coastal residents. We then directly compare public expectations and scientific estimations of SLR in five states of the U.S. Gulf Coast region and find that coastal residents in states that have experienced faster SLR in the past are more optimistic about future SLR by underestimating its magnitude compared to those experiencing slower SLR. Moreover, we find that people likely conflate the severity with likelihood of SLR risk. The contextual force represented by percentage of population living within the SFHA designated by the Federal Emergency Management Agency (FEMA) can significantly influence individuals’ estimations of future SLR, with higher percentages leading to higher estimates. We suspect that the SFHA has become a powerful risk communication tool that influences coastal residents’ judgments about future risk. our new paper has been published in Climate Policy (impact factor: 5.085). Below please find the abstract:
"Floods increasingly threaten disadvantaged communities around the globe. When limited financial resources are available, nature-based and community-based incremental adaptation that codifies existing actions and behaviors can help protect people and assets through risk reduction management. These adaptation measures mainly rely on non-financial capital that can be appropriate alternatives when financial resources are limited, especially within the context of disadvantaged communities. There are, however, challenges in implementing such adaptation measures, including differential power relationships that might lead to misallocation of benefits. We propose a polycentric governance framework that can enhance stakeholder engagement and mobilize various forms of non-financial capital to trigger a web of incremental adaptation measures through four support mechanisms: technological investment, institutional enhancement, knowledge production, and environmental protection. We further discuss how various facilitating factors, including i) communication and transportation infrastructure, ii) flexible laws/regulations, iii) risk communication, and iv) environmental restoration, can increase the likelihood of success in application of the framework. A successful application of the proposed framework also necessitates development of a research agenda around suitable non-financial metrics for monitoring and evaluating the performance of the proposed strategies. In addition, learning from new developments in general societal protection and resilience in communities with relatively large financial capital and experiences of practicing polycentric governance in disadvantaged communities may facilitate the implementation of polycentric governance-based disaster risk reduction globally." 10/1/2020 3 Comments A Ph.D opportunity My research group Environmental Decision Making at the Department of Geography at the University of Alabama is accepting applications for a Ph.D student with research assistantship, in social dimension of hazards in general and flood hazards in particular. The assistantship provides a stipend plus tuition remission.
The successful applicant will work with me and two research groups at the Department of Civil, Construction, and Environmental Engineering and will be involved in projects focused on human dimension of flood hazards. Qualified candidates should have a Master’s degree in Geography, Environmental Studies/Sciences, Planning or a related discipline. Candidates should have a strong interest in the intersection of social and physical dimensions of hazards and be eager to work in an interdisciplinary environment. Experience in quantitative data analysis, survey design, geographic information systems (GIS) are desired. Strong oral and written communication skills are required. For more information about this assistantship, please contact me at [email protected] well in advance of February 15, 2021 (the application deadline). Please include a copy of your CV, unofficial academic transcripts, and a brief personal statement that highlights skills relevant to the position. For more information about the department, please see https://geography.ua.edu/. |
|
RSS Feed