TY - JOUR
T1 - A spatial improved-knn-based flood inundation risk framework for urban tourism under two rainfall scenarios
AU - Liu, Shuang
AU - Liu, Rui
AU - Tan, Nengzhi
N1 - Funding Information:
The study was funded by Research on Practical Teaching Mode of Tourism Major in Higher Vocational Education Based on CBE Mode (FG2019131), Research on the Design and Application of Smart Classroom Mode under the Background of “Internet + Education” (186140055), and Seeing Beautiful China in Huzhou: Evolution and Development of Villages in Tourist Attractions in Huzhou (20hzghy026). The anonymous reviewers are acknowledged for their valuable comments.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take costeffective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.
AB - Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take costeffective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.
KW - GIS
KW - Landsat TM
KW - Likelihood
KW - Sensitivity analysis
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85102705340&partnerID=8YFLogxK
U2 - 10.3390/su13052859
DO - 10.3390/su13052859
M3 - Article
AN - SCOPUS:85102705340
SN - 2071-1050
VL - 13
SP - 1
EP - 19
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 5
M1 - 2859
ER -
