A Predictive Environmental Modelling Framework for Decision Support:Monthly Municipal-Level Forecasting of Nuisance Insect Outbreak Risk in Seoul, South Korea

Climate change and rapid urbanization are reshaping insect phenology and spatial occurrence patterns, leading to increasingly frequent nuisance insect outbreaks in urban environments. In dense metropolitan areas, sudden mass emergence of nuisance insects-such as mayflies, red-backed lovebugs, and non-biting midges-can cause sanitation concerns, disruption of urban infrastructure, and surges in public complaints, placing growing pressure on local environmental management. Despite these challenges, most current management practices remain reactive, relying on complaint-driven responses after outbreaks occur, which limits timely and efficient allocation of monitoring and management resources.

This study presents a predictive environmental modelling framework designed to support municipal decision-making by forecasting monthly nuisance insect outbreak risk at the district (gu) level in Seoul, South Korea. Rather than pursuing nationwide prediction, the study focuses on a single metropolitan system where environmental heterogeneity, administrative demand, and operational feasibility are closely aligned. By fixing the spatial analysis unit at the municipal district level, the framework delivers risk information directly compatible with urban monitoring plans, prioritization of management efforts, and allocation of limited resources.

A GIS-based spatial database was constructed by integrating nuisance insect occurrence history derived from citizen-science platforms and open biodiversity databases, monthly climate variables (mean temperature and cumulative precipitation), and district-level land cover composition. Occurrence records were subjected to quality control procedures, including coordinate validation and spatial de-duplication, and aggregated into monthly district-level counts as a proxy for outbreak intensity. Climate predictors were selected for interpretability and relevance to insect life cycles, while land cover metrics emphasized water and wetland areas, green spaces, and urbanized land.

To characterize baseline spatial tendencies, species distribution modelling was applied to derive habitat suitability indices for each target taxon. These indices were incorporated as auxiliary predictors to support interpretation of spatial risk patterns rather than serving as standalone forecasts. A predictive model integrating climate variables, land cover composition, occurrence history, and habitat suitability indices was then developed to estimate one-month-ahead outbreak risk scores for each district. Continuous risk scores were translated into ordinal risk classes using objective threshold rules to facilitate interpretation and identification of priority districts.

The predicted results indicate clear seasonal and spatial heterogeneity in outbreak risk across Seoul. Elevated risk tends to concentrate within specific seasonal windows, while district-level patterns vary according to local environmental conditions. Species-specific differences suggest that the relative importance of spatial drivers differs among taxa, with some showing stronger associations with water-related land cover and others responding more strongly to urban–green space configurations.

By delivering interpretable, one-month-ahead risk information at an administrative scale, the proposed framework provides a practical basis for shifting nuisance insect management from reactive responses toward anticipatory, risk-informed planning. The workflow is implemented as a reproducible, GIS-based pipeline that can be updated as new climate or occurrence data become available, demonstrating how predictive environmental modelling can function as an operational decision-support tool for urban environmental management under increasing climate and ecological uncertainty.

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2021-NR060142).