Work

Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation

Year: 2024

Type: conference-paper

Abstract: Wetlands are important to communities, offering benefits ranging from water purification, and flood protection to recreation and tourism. Therefore, identifying and prioritizing potential wetland areas is a critical decision problem. While data-driven solutions are feasible, this is complicated by significant data sparsity due to the low proportion of wetlands (3-6%) in many areas of interest in the southwestern US. This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas. To solve this limitation, we propose two strategies: (1) knowledge transfer from regions with rich wetlands (such as the Eastern US) to regions with sparser wetlands (such as the Southwestern area). , and (2) spatial data enrichment strategy that relies on an adaptive propagation mechanism. This mechanism differentiates between node pairs that have positive and negative impacts on each other for Graph Neural Networks (GNNs). We conduct rigorous experiments to substantiate our proposed method's effectiveness, robustness, and scalability compared to state-of-the-art baselines. Additionally, an ablation study demonstrates that each module is essential in prioritizing potential wetlands.

Source: IEEE International Conference on Big Data (BigData)

DOI: 10.1109/bigdata62323.2024.10825821

Authors: Yoonhyuk Choi, Reepal Shah, John Sabo, Huan Liu, K. Selçuk Candan

Institutions: Arizona State University, Tulane University

Language: English

Topic: Wetland Management and Conservation

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Life on land, Clean water and sanitation

Open Access status: green

Funder: Arm