Social and Behavioral Sciences Commons^™

Hydrological Drought Forecasting Using A Deep Transformer Model, Amobichukwu C. Amanambu, Joann Mossa, Yin-Hsuen Chen

University Administration Publications

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were …

Participatory Mapping To Address Neighborhood Level Data Deficiencies For Food Security Assessment In Southeastern Virginia, Usa, Nicole S. Hutton, George Mcleod, Thomas R. Allen, Christopher Davis, Alexander Garnand, Heather Richter, Prachi P. Chaven, Leslie Hoglund, Jill Comess, Matthew Herman, Brian Martin, Cynthia Romero

Political Science & Geography Faculty Publications

Background: Food is not equitably available. Deficiencies and generalizations limit national datasets, food security assessments, and interventions. Additional neighborhood level studies are needed to develop a scalable and transferable process to complement national and internationally comparative data sets with timely, granular, nuanced data. Participatory geographic information systems (PGIS) offer a means to address these issues by digitizing local knowledge.

Methods: The objectives of this study were two-fold: (i) identify granular locations missing from food source and risk datasets and (ii) examine the relation between the spatial, socio-economic, and agency contributors to food security. Twenty-nine subject matter experts from three cities …

Odu Researchers Attempt To Forecast Flood Impacts In Real Dollars, News @ Odu

News Items

No abstract provided.

A Raft To Coastal Resilience: Odu Researchers Collaborate To Help Rural Communities Combat Flooding Impacts, News @ Odu

News Items

No abstract provided.

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t₁ together with low-resolution images at a …