FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W588320544> ?p ?o ?g. }

• W588320544 endingPage "1141" @default.
• W588320544 startingPage "1130" @default.
• W588320544 abstract "Floods, natural disasters that occur worldwide, have become more and more frequent in recent decades. Flooding is often unavoidable and unexpected; however, it can be controlled through appropriate measures to minimize losses and damage. Flood hazard risk assessment, a holistic approach that involves numerous evaluation indices in river catchments, is an increasingly effective and sustainable practice, but the complicated, non-linear relationship between evaluation indices and risk levels pose a significant challenge to accurate assessment. An intelligent learning machine called random forest (RF) can run efficiently on large databases and provide estimates regarding the importance of specific variables in the classification. This lends RF a considerable advantage in solving the non-linear problems inherent to risk assessment, as well as estimating the importance degree of each index. As such, in this study, an assessment model based on RF was adopted to evaluate regional flood hazard risk. The proposed flood hazard risk assessment method was implemented in Dongjiang River Basin, China. Eleven risk indices were selected and five thousand samples were created for training and testing. The support vector machine (SVM) was used for risk assessment as a comparison, as well as an analysis of index importance degree. Results show that (1) both the training and testing error rate of RF can be reduced by increasing the sample size and the number of classification trees, (2) the higher and highest-risk zones occupy approximately 19.09% of the total, and are primarily located in Baoan, Longgang, Huiyang, Huidong, the western area of Boluo, and the southern part of Dongguang, and (3) the indices maximum three-day precipitation (M3PD), runoff depth (RD), typhoon frequency (TF), digital elevation model (DEM), and topographic wetness index (TWI) are the top five most important out of the eleven risk indices, occupying 71.11% of the total risk; but normalized difference vegetation index (NDVI), stream power index (SPI), soil texture (ST), distance to the river (DR), slope (SL), and land use pattern (LUP) indices are less consequential, at only 28.89%. This study shows the potential to provide a novel and highly successful approach to flood hazard risk assessment. Evaluation results provide a reference for flood risk management, prevention, and reduction of natural disasters in the study basin." @default.
• W588320544 created "2016-06-24" @default.
• W588320544 creator A5020635042 @default.
• W588320544 creator A5025265159 @default.
• W588320544 creator A5066350606 @default.
• W588320544 creator A5069657194 @default.
• W588320544 creator A5082939536 @default.
• W588320544 creator A5091393409 @default.
• W588320544 date "2015-08-01" @default.
• W588320544 modified "2023-10-17" @default.
• W588320544 title "Flood hazard risk assessment model based on random forest" @default.
• W588320544 cites W1974614011 @default.
• W588320544 cites W1994607702 @default.
• W588320544 cites W1995244252 @default.
• W588320544 cites W2002339893 @default.
• W588320544 cites W2004553299 @default.
• W588320544 cites W2007802759 @default.
• W588320544 cites W2012456251 @default.
• W588320544 cites W2018280852 @default.
• W588320544 cites W2019923759 @default.
• W588320544 cites W2022417541 @default.
• W588320544 cites W2022604714 @default.
• W588320544 cites W2027386095 @default.
• W588320544 cites W2028230084 @default.
• W588320544 cites W2029801487 @default.
• W588320544 cites W2030844533 @default.
• W588320544 cites W2033155403 @default.
• W588320544 cites W2047409389 @default.
• W588320544 cites W2047760271 @default.
• W588320544 cites W2057669676 @default.
• W588320544 cites W2065511632 @default.
• W588320544 cites W2065755547 @default.
• W588320544 cites W2066722804 @default.
• W588320544 cites W2066813588 @default.
• W588320544 cites W2068680128 @default.
• W588320544 cites W2073009068 @default.
• W588320544 cites W2084773003 @default.
• W588320544 cites W2087303343 @default.
• W588320544 cites W2091374137 @default.
• W588320544 cites W2107645058 @default.
• W588320544 cites W2108484081 @default.
• W588320544 cites W2108564850 @default.
• W588320544 cites W2111410742 @default.
• W588320544 cites W2117480889 @default.
• W588320544 cites W2131051513 @default.
• W588320544 cites W2135617432 @default.
• W588320544 cites W2142356775 @default.
• W588320544 cites W2144443458 @default.
• W588320544 cites W2146230369 @default.
• W588320544 cites W2165102158 @default.
• W588320544 cites W2170589321 @default.
• W588320544 cites W2331071973 @default.
• W588320544 cites W2911964244 @default.
• W588320544 cites W4239510810 @default.
• W588320544 cites W4243422210 @default.
• W588320544 cites W4379014164 @default.
• W588320544 cites W3147082496 @default.
• W588320544 doi "https://doi.org/10.1016/j.jhydrol.2015.06.008" @default.
• W588320544 hasPublicationYear "2015" @default.
• W588320544 type Work @default.
• W588320544 sameAs 588320544 @default.
• W588320544 citedByCount "416" @default.
• W588320544 countsByYear W5883205442016 @default.
• W588320544 countsByYear W5883205442017 @default.
• W588320544 countsByYear W5883205442018 @default.
• W588320544 countsByYear W5883205442019 @default.
• W588320544 countsByYear W5883205442020 @default.
• W588320544 countsByYear W5883205442021 @default.
• W588320544 countsByYear W5883205442022 @default.
• W588320544 countsByYear W5883205442023 @default.
• W588320544 crossrefType "journal-article" @default.
• W588320544 hasAuthorship W588320544A5020635042 @default.
• W588320544 hasAuthorship W588320544A5025265159 @default.
• W588320544 hasAuthorship W588320544A5066350606 @default.
• W588320544 hasAuthorship W588320544A5069657194 @default.
• W588320544 hasAuthorship W588320544A5082939536 @default.
• W588320544 hasAuthorship W588320544A5091393409 @default.
• W588320544 hasConcept C105795698 @default.
• W588320544 hasConcept C119857082 @default.
• W588320544 hasConcept C12174686 @default.
• W588320544 hasConcept C136764020 @default.
• W588320544 hasConcept C15744967 @default.
• W588320544 hasConcept C166957645 @default.
• W588320544 hasConcept C169258074 @default.
• W588320544 hasConcept C178790620 @default.
• W588320544 hasConcept C181843262 @default.
• W588320544 hasConcept C185592680 @default.
• W588320544 hasConcept C186594467 @default.
• W588320544 hasConcept C205649164 @default.
• W588320544 hasConcept C2776898743 @default.
• W588320544 hasConcept C2777382242 @default.
• W588320544 hasConcept C2778061687 @default.
• W588320544 hasConcept C33923547 @default.
• W588320544 hasConcept C38652104 @default.
• W588320544 hasConcept C39432304 @default.
• W588320544 hasConcept C41008148 @default.
• W588320544 hasConcept C49261128 @default.
• W588320544 hasConcept C542102704 @default.

• next