No abstract

Abstract:Extreme landslides triggered by rainfall in hilly regions frequently lead to serious damage, including casualties and property loss. The frequency of landslide occurrences may increase under climate change, due to the increasing variability of precipitation. Developing urban areas outside landslide risk zones is the most effective method of reducing or preventing damage; however, planning in real life is a complex and nonlinear problem. For such multi-objective problems, genetic algorithms may be the most appropriate optimization tools. Therefore, in this study, we suggest a comprehensive land-use allocation plan using the Non-dominated Sorting Genetic Algorithm II to overcome multi-objective problems, including the minimization of landslide risk, minimization of change, and maximization of compactness. Our study area is Pyeongchang-gun, the host city of the 2018 Winter Olympics in Korea, where high development pressure has resulted in urban sprawl into the hazard zone where a large-scale landslide occurred in 2006. We obtain 100 Pareto plans that are better than the actual land use data for at least one objective, with five plans that explain the trade-offs between meeting the first and second objectives. The results can be used by decision makers for better urban planning and for climate change-related spatial adaptation.

As climate change is ongoing, many studies have recently focused on adaptation to climate change from a spatial perspective. However little is known about how changing the spatial composition of landuse could improve climate change resilience. Consideration of climate change impacts when spatially allocating landuse could be a useful and fundamental long term adaptation strategy, particularly for regional planning. Here, we identify climate adaptation scenarios based on existing extents of three landuse classes using multi-objective genetic algorithms for a 9982 km 2 region with 3.5 million inhabitants in South Korea. We selected five objectives for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damage and existing landuse conversion, maximization of rice yield, protection of high species richness areas, and economic value. We generated 17 Pareto landuse scenarios by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial landuse composition for all adaptation objectives, suggesting that some alteration of current landuse patterns could increase overall climate resilience. Given the flexible structure of the optimization model, we expect that regional stakeholders could efficiently generate other scenarios by adjusting model parameters (weighting combinations) or replacing input data (impact maps), and selecting a scenario depending on preference or a number of problem-related factors.

No abstract

No abstract