A Novel Comparative Flood Risk Assessment Framework for Urban Planning: Investigating the Impact of Development History on Flood Risk

Published: 25 March 2024| Version 3 | DOI: 10.17632/bmxcrkkc3s.3
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Description

This repository code serves as supplementary data for the article titled 'A Novel Comparative Flood Risk Assessment Framework for Urban Planning: Investigating the Impact of Development History on Flood Risk'

Files

Steps to reproduce

The provided repository link includes all the codes used in the research, covering: 1.JavaScript codes for Land Use and Land Cover (LULC) identification and classification, specifically designed for Landsat and Sentinel series hyperspectral data, and compatible with the Google Earth Engine (GEE) platform. 2.Hydrological calibration and validation codes, which calculate Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and the Pearson correlation index. 3.Thirteen detailed statistical case codes, covering descriptive statistics, hypothesis testing, and logistic regression. 4.Calculation of Shannon's Land Use Diversity Index (SLD) used to describe land use diversity.

Institutions

Harbin Institute of Technology

Categories

Statistics, Hydrology, Remote Sensing, Land Cover Analysis, Landsat Satellite, Multinomial Logistic Regression, Chi-Square Testing, Shanon Entropy, Sentinel Surveillance, Pearson Correlation Coefficient, Kruskal-Wallis H Test, Mann-Whitney U Test, Wilcoxon Signed-Rank Test, Random Decision Forest

Funding

International Cooperation Training Program for Innovative Talents, China Scholarship Council (CSC)

202106120332

Ministry of Science and Technology of the People's Republic of China

2022YFC3203402

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