Basu A, Ghosh A, Mandal A, Martín N, Pardo L (2017) A wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator. Electron J Stat. https://doi.org/10.1214/17-EJS1295 Article Google Scholar Beven KJ, Kirkby MJ...
Performance evaluation metrics such as Kappa statistic, accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated for each model. RF (AUC = 0.92) and XGB (AUC = 0.9) models showed excellent classification capabilities, surpassing the performance of the other ...
Flood can be defined as the flooding of areas that are not normally submerged as a result of higher current and level rise in a stream. Floods are among the main causes of natural disaster damage in many parts of the world, as well as can be determined by the causes affected environment....
The findings provided relevant information that detected the degree of flooding in a particular flood zone. The validity tests conducted show the efficiency of our method; the result also helped in selecting the test that best suits our analysis. The Gap statistic is good in terms of ...
The uncertainty of the combined variables was assessed with the likelihood measures such as F-statistic, mean absolute error, root mean square error, and Nash-Sutcliffe efficiency which compares observed and predicted inundated area as well as flood water depth simulated using the HEC-RAS model....
The AUC is a single scalar statistic that quantifies a binary classifier's total performance (Hanley and McNeil 1982). An AUC value of 0.7 to 0.8 is acceptable, 0.8 to 0.9 is excellent, and more than 0.9 is outstanding (Hosmer and Lemeshow 2000). The results of the study were validated...
Nevertheless, the graph may not have a good power of discrimination when many probability distributions are suitable for the sample data in L-moment ratio diagrams. For this reason, a numerical goodness of fit test, called the Z-statistic, is secondly applied to choose the best frequency ...
The p-values in Equation (4) are assessed using the Anderson–Darling (AD) test for each candidate threshold, with the respective statistic assessed as: 𝐴2𝑛=−𝑛−1𝑛∑𝑛𝑖=1(2𝑖−1)[𝑙𝑜𝑔(𝑧(𝑖))+𝑙𝑜𝑔(1−𝑧(𝑛+1−𝑖))]An2=−n−1n∑...
For this, we used three common continuous-statistic metrics: root mean square error (RMSE), correlation coefficient (R), and Kling–Gupta efficiency (KGE). Furthermore, three categorical statistics were used to assess the precipitation detection ability: probability of detection (POD), false alarm...