FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4211033815> ?p ?o ?g. }

• W4211033815 endingPage "3061" @default.
• W4211033815 startingPage "3041" @default.
• W4211033815 abstract "Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flood are different for every watershed as they depend upon the characteristics of each watershed. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of ensemble model showed 99% accuracy in training and 98% accuracy in testing datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies." @default.
• W4211033815 created "2022-02-13" @default.
• W4211033815 creator A5022305321 @default.
• W4211033815 creator A5028831066 @default.
• W4211033815 creator A5056236904 @default.
• W4211033815 date "2022-02-11" @default.
• W4211033815 modified "2023-10-18" @default.
• W4211033815 title "Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model" @default.
• W4211033815 cites W1066523502 @default.
• W4211033815 cites W1186112364 @default.
• W4211033815 cites W183022862 @default.
• W4211033815 cites W1848280225 @default.
• W4211033815 cites W1942836393 @default.
• W4211033815 cites W1964412169 @default.
• W4211033815 cites W1964997367 @default.
• W4211033815 cites W1968546252 @default.
• W4211033815 cites W1971720236 @default.
• W4211033815 cites W1974281059 @default.
• W4211033815 cites W1974614011 @default.
• W4211033815 cites W1975914988 @default.
• W4211033815 cites W1981646498 @default.
• W4211033815 cites W1982948123 @default.
• W4211033815 cites W1987342969 @default.
• W4211033815 cites W1990240845 @default.
• W4211033815 cites W1993934953 @default.
• W4211033815 cites W2003938839 @default.
• W4211033815 cites W2012118327 @default.
• W4211033815 cites W2020432660 @default.
• W4211033815 cites W2027386095 @default.
• W4211033815 cites W2030675529 @default.
• W4211033815 cites W2036033711 @default.
• W4211033815 cites W2042315239 @default.
• W4211033815 cites W2046567883 @default.
• W4211033815 cites W2065511632 @default.
• W4211033815 cites W2065949495 @default.
• W4211033815 cites W2066329766 @default.
• W4211033815 cites W2069483533 @default.
• W4211033815 cites W2076475918 @default.
• W4211033815 cites W2077483615 @default.
• W4211033815 cites W2094534696 @default.
• W4211033815 cites W2097229458 @default.
• W4211033815 cites W2102148524 @default.
• W4211033815 cites W2117150513 @default.
• W4211033815 cites W2137034166 @default.
• W4211033815 cites W2141720560 @default.
• W4211033815 cites W2165419732 @default.
• W4211033815 cites W2229346331 @default.
• W4211033815 cites W2259343653 @default.
• W4211033815 cites W226699106 @default.
• W4211033815 cites W2309165934 @default.
• W4211033815 cites W2313636521 @default.
• W4211033815 cites W2344846457 @default.
• W4211033815 cites W2423094380 @default.
• W4211033815 cites W2473194932 @default.
• W4211033815 cites W2489814317 @default.
• W4211033815 cites W2519746072 @default.
• W4211033815 cites W2549184242 @default.
• W4211033815 cites W2640557513 @default.
• W4211033815 cites W2728770052 @default.
• W4211033815 cites W2741517055 @default.
• W4211033815 cites W2761698665 @default.
• W4211033815 cites W2766228856 @default.
• W4211033815 cites W2773213923 @default.
• W4211033815 cites W2783350994 @default.
• W4211033815 cites W2800522401 @default.
• W4211033815 cites W2895196240 @default.
• W4211033815 cites W2899026392 @default.
• W4211033815 cites W2907882001 @default.
• W4211033815 cites W2912361013 @default.
• W4211033815 cites W2916335270 @default.
• W4211033815 cites W2920548804 @default.
• W4211033815 cites W2920820407 @default.
• W4211033815 cites W2938393691 @default.
• W4211033815 cites W2965746779 @default.
• W4211033815 cites W2981581709 @default.
• W4211033815 cites W2981818224 @default.
• W4211033815 cites W2990262107 @default.
• W4211033815 cites W2990483797 @default.
• W4211033815 cites W2992321006 @default.
• W4211033815 cites W2993767981 @default.
• W4211033815 cites W2995023359 @default.
• W4211033815 cites W2997237841 @default.
• W4211033815 cites W2998031487 @default.
• W4211033815 cites W2999113262 @default.
• W4211033815 cites W3001758897 @default.
• W4211033815 cites W3003715176 @default.
• W4211033815 cites W3019758389 @default.
• W4211033815 cites W3091948139 @default.
• W4211033815 cites W3093835305 @default.
• W4211033815 cites W3115061751 @default.
• W4211033815 cites W3129718208 @default.
• W4211033815 cites W3140276143 @default.
• W4211033815 cites W3160253347 @default.
• W4211033815 cites W4210949798 @default.
• W4211033815 cites W562231173 @default.
• W4211033815 doi "https://doi.org/10.1007/s00477-022-02179-1" @default.
• W4211033815 hasPublicationYear "2022" @default.
• W4211033815 type Work @default.

• next