characteristic (ROC) curve method was used to validate the model. The area under the curve (AUC) values of the model were 81% for the prediction rate and 82% for the success rate indicating its effectiveness in identifying areas susceptible to flooding. The results showed that 18.48% of the...
accurate flood hazard mapping is crucial. Machine learning models have emerged as valuable approaches for flood hazard assessment. In this study, six machine learning (ML) models, including Maximum Entropy,
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-R...
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...
losses and damages; flood risk management; local government; Malaysia1. Introduction The Department of Irrigation and Drainage (DID) in Malaysia in 2023 defined flood as a body of water, rising, swelling and overflowing land not usually thus covered [1,2,3]. Overflowing of the bank of a ...
SPI is a statistic that measures the erosive strength of discharge compared to a specific area within a watershed. It is also a measure of the erosive force of the flowing water [91]. SPI can be accounted for using the following equation [92]: SPI = As× tan(β) The SPI map was...
Slope was calculated using an inbuilt tool called “slope” in the geoprocessing toolbox. Figure 2. Workflow from model builder in ArcGIS Pro v3.0.1. TPI was calculated from the focal statistic and raster calculator [64]. TPI is the difference in elevation at the central point (𝑋0X0)...
The value of the test statistic for the two samples is defined by 𝐷=𝑠𝑢𝑝|𝐹1(𝑥)−𝐹2(𝑥)|D=supF1x−F2x (20) where F1 and F2 are the empirical distribution functions based on the two samples, and sup is the supremum function. The hypothesis of the test is ...
Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and d