Liu, S.; Tan, N.; Liu, R. A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China. ISPRS Int. J. Geo-Inf.2023, 12, 463.
Liu, S.; Tan, N.; Liu, R. A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China. ISPRS Int. J. Geo-Inf. 2023, 12, 463.
Liu, S.; Tan, N.; Liu, R. A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China. ISPRS Int. J. Geo-Inf.2023, 12, 463.
Liu, S.; Tan, N.; Liu, R. A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China. ISPRS Int. J. Geo-Inf. 2023, 12, 463.
Abstract
Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Mapping, evaluating, and predicting the flood inundation risk (FIR) is significant for coastal tourism. The study develops a spatial tourism–aimed framework integrating a weighted k-Nearest Neighbors (WkNN), Geographic Information Systems, and flood-related spatially environmental criteria such as precipitation, elevation, soil, and drainage systems. These model inputs were standardized and weighted using distance, and integrated into WkNN to infer regional probability and distribution of FIR. Zhejiang province, China, was selected as a case study. The resulting map was pictured to denote the likelihood of the criteria at various risk categories, which was validated by historical Maximum Inundation Extent (MIE) extracted from World Environment Situation Room. The result indicates 80.59% of WkNN results reasonably confirm the MIE. Precipitation and elevation make a negative contribution to high-medium risk, and drainage systems positively alleviate the regional stress of FIR. The results can help stakeholders make suitable strategies to protect coastal tourism, and also weigh WkNN is superior to kNN in FIR assessment. The framework provides a productive way to yield a reliable assessment of FIR and can also be extended to other risk-related environmental studies under climate change.
Environmental and Earth Sciences, Environmental Science
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