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Shu EG, Porter JR, Hauer ME, Sandoval Olascoaga S, Gourevitch J, Wilson B, Pope M, Melecio-Vazquez D, Kearns E. Integrating climate change induced flood risk into future population projections. Nat Commun 2023; 14:7870. [PMID: 38110409 PMCID: PMC10728110 DOI: 10.1038/s41467-023-43493-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 11/10/2023] [Indexed: 12/20/2023] Open
Abstract
Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years.
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Affiliation(s)
| | - Jeremy R Porter
- First Street Foundation, Brooklyn, NY, USA
- Department of Sociology and Demography, City University of New York, New York, NY, USA
- Environmental Health Sciences Department, Columbia University's Mailman School of Public Health, New York, NY, USA
| | - Mathew E Hauer
- Department of Sociology and Center for Demography and Population Health, Florida State University, Tallahassee, FL, USA
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Tounsi A, Temimi M. A systematic review of natural language processing applications for hydrometeorological hazards assessment. NATURAL HAZARDS (DORDRECHT, NETHERLANDS) 2023; 116:2819-2870. [PMID: 36776702 PMCID: PMC9905760 DOI: 10.1007/s11069-023-05842-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.
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Affiliation(s)
- Achraf Tounsi
- Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030 USA
| | - Marouane Temimi
- Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030 USA
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Rocca R, Tamagnone N, Fekih S, Contla X, Rekabsaz N. Natural language processing for humanitarian action: Opportunities, challenges, and the path toward humanitarian NLP. Front Big Data 2023; 6:1082787. [PMID: 37034436 PMCID: PMC10080095 DOI: 10.3389/fdata.2023.1082787] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Natural language processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence, which is concerned with developing methods to process and generate language at scale. Modern NLP tools have the potential to support humanitarian action at multiple stages of the humanitarian response cycle. Both internal reports, secondary text data (e.g., social media data, news media articles, or interviews with affected individuals), and external-facing documents like Humanitarian Needs Overviews (HNOs) encode information relevant to monitoring, anticipating, or responding to humanitarian crises. Yet, lack of awareness of the concrete opportunities offered by state-of-the-art techniques, as well as constraints posed by resource scarcity, limit adoption of NLP tools in the humanitarian sector. This paper provides a pragmatically-minded primer to the emerging field of humanitarian NLP, reviewing existing initiatives in the space of humanitarian NLP, highlighting potentially impactful applications of NLP in the humanitarian sector, and describing criteria, challenges, and potential solutions for large-scale adoption. In addition, as one of the main bottlenecks is the lack of data and standards for this domain, we present recent initiatives (the DEEP and HumSet) which are directly aimed at addressing these gaps. With this work, we hope to motivate humanitarians and NLP experts to create long-term impact-driven synergies and to co-develop an ambitious roadmap for the field.
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Affiliation(s)
- Roberta Rocca
- Department of Culture, Cognition and Computation, Aarhus University, Aarhus, Denmark
- *Correspondence: Roberta Rocca
| | | | - Selim Fekih
- Data Friendly Space, Richmond, VA, United States
| | | | - Navid Rekabsaz
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
- Linz Institute of Technology, AI Lab, Linz, Austria
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Mehedi MAA, Smith V, Hosseiny H, Jiao X. Unraveling the complexities of urban fluvial flood hydraulics through AI. Sci Rep 2022; 12:18738. [PMID: 36333429 PMCID: PMC9636396 DOI: 10.1038/s41598-022-23214-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022] Open
Abstract
As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extension and finer discretization and solving the physics-based numerical equations are computationally expensive. Computational efforts increase drastically with an increase in model dimension and resolution, preventing current solutions from fully realizing the data revolution. In this research, we demonstrate the effectiveness of artificial intelligence (AI), in particular, machine learning (ML) methods including the emerging deep learning (DL) to quantify urban flooding considering the lower part of Darby Creek, PA, USA. Training datasets comprise multiple geographic and urban hydraulic features (e.g., coordinates, elevation, water depth, flooded locations, discharge, average slope, and the impervious area within the contributing region, downstream distance from stormwater outfalls and dams). ML Classifiers such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN) are used to identify the flooded locations. A Deep neural network (DNN)-based regression model is used to quantify the water depth. The values of the evaluation matrices indicate satisfactory performance both for the classifiers and DNN model (F-1 scores- 0.975, 0.991, 0.892, and 0.855 for binary classifiers; root mean squared error- 0.027 for DNN regression). In addition, the blocked K-folds Cross Validation (CV) of ML classifiers in detecting flooded locations showed satisfactory performance with the average accuracy of 0.899, which validates the models to generalize to the unseen area. This approach is a significant step towards resolving the complexities of urban fluvial flooding with a large multi-dimensional dataset in a highly computationally efficient manner.
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Affiliation(s)
- Md Abdullah Al Mehedi
- grid.267871.d0000 0001 0381 6134Villanova Centre of Resilient Water System, Villanova University, Villanova, PA USA
| | - Virginia Smith
- grid.267871.d0000 0001 0381 6134Villanova Centre of Resilient Water System, Villanova University, Villanova, PA USA
| | - Hossein Hosseiny
- grid.4367.60000 0001 2355 7002Department of Earth and Planetary Sciences, Washington University in St. Louis, St. Louis, MO USA
| | - Xun Jiao
- grid.267871.d0000 0001 0381 6134Department of Electrical and Computer Engineering, Villanova University, Villanova, PA USA
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Pezanowski S, Mitra P, MacEachren AM. Exploring Descriptions of Movement Through Geovisual Analytics. KN - JOURNAL OF CARTOGRAPHY AND GEOGRAPHIC INFORMATION 2022; 72:5-27. [PMID: 35229072 PMCID: PMC8866112 DOI: 10.1007/s42489-022-00098-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 01/31/2022] [Indexed: 11/26/2022]
Abstract
Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable.
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Affiliation(s)
- Scott Pezanowski
- Information Sciences and Technology, The Pennsylvania State University, Westgate Building, University Park, PA 16802 USA
| | - Prasenjit Mitra
- Information Sciences and Technology, The Pennsylvania State University, Westgate Building, University Park, PA 16802 USA
| | - Alan M. MacEachren
- Information Sciences and Technology, The Pennsylvania State University, Westgate Building, University Park, PA 16802 USA
- Department of Geography, The Pennsylvania State University, Walker Building, University Park, PA 16802 USA
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