Flood Inundation Mapping Using Remote Sensing DEMs and HEC-RAS (for data-limited areas)- Part4

okay we explained and describe all about uh Ras mapper and heas for extraction of river geometry using remotely sens them uh in this part I want to share with you my knowledge about and my findings about the US the usability and the potential of remote sensing for flood ntion mapping in data Spar regions or data limited areas okay let me to share you know uh in a case study we investigated the uh usability of remotely sens STS for f inundation mapping and found that uh all stem is the best one for for inundation mapping okay let me check the final results I think the output uh the output of this uh results this paper uh helps you to find better uh them sources for your case studies okay in this uh paper or in this study we use different D sources for example gdm which is created in based on topographic maps ground topographic maps H the the next Ones based on alos is AR ctim Aster ctim and SRM 90 M and the details of these data sets is presented in this table and I don't want to explain that okay uh we use uh these DMS uh for extraction of river geometry I mean cross-sectional shape uh the black color is based on ground topographic maps the red color is based on Aster cm and the blue and green color and the orange color is based onm 9M Sr ctim and allos as you can see in the uh section one the aster is tends to underestimate the elevation of uh crosssection moreover srtm both srtm 90 and 30 m tend to overestimate while allos almost is perform good as well as surveyed cross-section you know we investigated We compare in different cross-section and you can see in all cross-sections Asser tends to underestimate the elevation and srtm tends to overestimate while allows performs as well as ground topographic maps you know that's completely fantastic and uh shows the potential of allo stem for using in flood ntion and Hydra simulation at the next step we use uh these processes for different Riv and the comparison completely show the superiority of all stem for uh extraction of river geometry okay these are the statistical variation of all thems okay uh in this figure you can see the effect of using D sources on the average percentage error of simulated flood components uh you know uh in simulation of water surface elevation alls with the mean difference lower than 2% is the best one uh for simulation of water surface elevation it means that when you don't have enough uh or high resolution data sets or Grant graphic maps you can use all of them for simulation of water surface elevation with the minimum errors or with the minimum difference while using a DM lead to higher error in simulation of water surface elevation and uh flood extend you can see uh ather averagely lead to lower than 90% error in simulation of flood uh wids and flood extents while a using AET lead to higher errors in flood extend and this is for soas and this is for Saros Rivers okay in this figure the special distribution of flood extends uh is shown and you can see the all is completely uh agreement with ground topographic maps outputs while using AER srtm and srtm CM and srtm 9M uh does not lead accurately or uh satisfy outputs this is for sarb and this is for soas R you can see soas is a smaller River and sarbas is a the wide with River but you know you can see alos performs relatively better than other data source and other data source does not simulate continuous and uh logical flood extent and this is some statistical parameters and in this figure you can see the comparison between flood based on ground topographic DM or based on all you know as I mentioned allos performs well and you can see the flood extends in different cross-sections completely fall into 10% Bond width while for ather you can see uh the model tends to underestimate the flood extent moreover RTM 30m and 9M perform lower than alos also for so River srtm and Aster CM perform vs and uh the result an unsatisfied output while allows performs better uh for smaller small re however there are a little bit uh more errors in Vex and but it's completely obvious because when you are using remotely sense St for a smaller reverse you you shouldn't expect uh high accuracy results because uh this result I think completely okay and you shouldn't expect higher performance in a small uh rever okay I think uh this uh explanation and this descriptions will help you to select better data source for your case study and and uh I hope you learn all about River extraction and download uh and download processing of uh download them uh for your case study and extracting River geometry and Justice uh I I hope you have a good time uh Happy hydraulic modeling bye-bye to the next sections and parts

Channel: HydroAI

Views: 1,618

Keywords: HydroAi, Azizian, Artificial Intelligence, Hydrology, HEC-RAS, Flood, Flood management, Remote sensing, Digital Elevation Models, DEMs, River engineering

Id: XhJ2WpjzwjI

Length: 8min 52sec (532 seconds)

Published: Thu Dec 07 2023