Evaluating Flood Hazard Potential in Danang City, Vietnam Using FOSS4G
Raghavan, Venkatesh
This study aims to build flood hazard map for a lowland area in Danang city, Vietnam based on topographical data, land-cover and flood inundation map. ALOS PALSAR imageries with respect to the time before and during flood event in 2007 were used to characterize flood inundation. In addition, topographical data was developed via 5m resolution DEM generated by a bi-cubic spline algorithm that implemented in BS-Horizon program. This high resolution DEM was used to enhance the accuracy of flood map as well as determine the geomorphological features of the study area and the relations on flood hazards. Land-cover map in 2007 extracted from Landsat TM data was also applied for the landform classification process. The flood hazard map was generated based on the probability of submergence of each landform unit. The changes in land-cover as well as topography have significant effect on flood hazard. In this study, optical Rapid Eye remote sensed data in 2014 was used to extract land-cover by the time and the land-cover change from 2007 to 2014. The estimated landform and potential flood hazard map in 2014 was built based on the updated land-cover. The result was compared with field survey flood pillar data, flood map in the past, land-cover change and flood scenario given by Danang City government to assess the accuracy. This research has also proposed some flood prevention plans for this alluvial lowland area. Landforms units derived rule-based classification of land cover map, 5m resolution DEM data and flood inundation map not only facilitates the understanding of the nature of flood but also in flood risk zoning. The methodology developed in this study would be useful in low relief areas in Vietnam and other parts of the world.
 Question number one, my name is Vinay. I am a PhD candidate from Osaka City University. This time I have been joined by Tati and Tran. She is also a PhD candidate from my university. Because of our personal commitments, she could not manage to come here, so I will be presenting. I hope that I could manage your questions. Evaluating flood hazard potential in the noncity with the initial first 4G that is the title today. So, in the presentation I will briefly talk about the study area and the data sets where I am. And in methodology there are three methodologies that have been proposed to generate. In data preparation it is a generation of digitalization model from our application polls. And in flood inundation map we create from 2007 Palsar data. And after that we will do characterization topographic features and find the relation between flood inundation. Then we will evaluate the result with field data as well as the simulation. So, this is the UGA2 by the UGA based in Vietnam during flooding. This place is affected by flood in the floods. So, this is the coastal lowland area. You can see the figure here. This is the name. This is just described in the evaluation data we have collected. We have not collected actually the information that you see from Vietnam is collected. So, and this is the Palsar data which have been collected just before the flooding. And this is the Palsar data which is collected during the flood. And you can see the change. This is the same data which is used to class the land cover. This is also before the flood. And this is the rapidization data which is also used to show the land cover, recent land cover changes. So, these are the data sets we have. This is again the same elevation points which we are using to generate high accuracy digitalization model. So, this is BS Horizon data which is proposed by NaNoWgAkli. He is one of the others in this paper. So, this is it is creating a bitmap which the points you have it is an interpolation method. And the only simple you have you just over on the bitmap and you will be having equality and inequality constraint. Which means that wherever you have you have your points I mean elevation points as vector that whatever. So, these points wherever you have the points the same pixel will be considered as 0 which means you have 0. And the other places it will be have any inequality constraint which means the range of the data which we have collected here is 0 to 10 meter. So, we need to control the points when you do interpolation between 0 and 10. You don't want to underestimate or underestimate. So, this inequality constraint as if it is it means that minus 1 and minus minus 1 and plus 1 we give it means that minus 1 is less than 10 and 1 is greater than 0. Which needs to control the points between range of 0 and 10 meter. This is a graph which actually is this is a graph which to show the test of optimal parameter which which have to get good accuracy digital detection model. And the red red line is Jx which means that so controls smoothness of surface and Rx is controls violation of constraints data. Which means if you if you have we have made 3 zones and B zones is giving good good accuracy results. So, we have Jx axis you have alpha and Y axis you have Jx and Rx. So, we have selected we want to estimate the alpha and Mx and My is nothing but it is the size of the tail. Even if you change the tail size the estimation results can be changed. So, here we estimate an optimal parameter. So, the alpha is this much and Mx is this much, My is this much and you can see the when we compare with the results. Here minimum should be close to 0 and maximum should be close to 10 because our data range is that much. And Rx is also showing better in this optimal parameter. So, we fix this as the optimal parameter which can be used for further studies when you use BS horizon algorithm to interpolate. And this is the 5 meter resolution which is produced. And then we compare with other algorithm which has been used by so many people IW and RST. The BS horizon algorithm gives better R score and RMSE. So, this is just a diverse scatter plot to show the correlation of BS horizon model with the ground truth data. So, since we have collected we have generated good accuracy digitalization model. We are going to estimate the inundation area by using pulsar data. So, using normalised back scattering coefficient, this was the equation used to generate the normalised back scattering coefficient from a low spacer data. So, while this image itself we can identify some of the part which is already covered by water inundated. So, normalised back scattering coefficient estimated by 2007 September data and October data which is during the flood. And we just difference the NNDC and you get this result. And this can be considered as the flood inundation but it has some anomalies and we have to remove it. So, we have used digital animation used to integrate that which means that the maximum water line during the flood was around 3 meter. So, we have removed the other other masses of more than 3 meter. So, the final output will be looking like this. So, this is almost matching with the pulsar. So, then we are estimated the land per classification map to estimate the topographic features. So, green cover is agriculture, yellow is built up and science water and brown is burnt land here. And the area is covered by the naturalization is more mass from agriculture area and built up. Water is of course, it is a permanent water and the land. So, the percentage of inundation you can see over there. This is the two extracts and geomorphological features using a cross section which means that HD means higher terrace which if you have above, then higher than 4 meter which is considered as higher terrain and this is lower terrain. If you have flood basin which is lower than 2 meter and this is again lower terrain and flood basin and then natural way you can see the figure. And here, now we have three kinds of input which is digitalization and land cover map, then pulsar and from the pulsar aqua inundation map. So, these all three input data we have rule based classification structure to find the outside potential, flood outside potential. Here, permanent water if permanent water this is the rule like permanent if it is land cover water then it is permanent water. And in case of flood basin, land cover if land cover is of agriculture or not and or flood map is flooded or in the dam it is less than 2 meter. Then you can consider that area as flood basin. And in case of natural levee if you can cover water, land cover is natural water and the distance to the river channel should be less than 5 meter and the dam should be less than 3 meter. Then you can consider it as a natural levee and lower terrace area also the same if it is non water and dam less than 4 meter and higher terrace if it is greater than 4 meter. So, that way you are classified. So, using Hari Amal et al. So, we made a flood potential if it is permanent water and water level raised but no damage such case there is no flood damage. And if submerged for long time with deep water which is high flood potential and submerged in other flood which is moderate which has moderate potential flood. Submissive flood in major flood which is low potential and never submerged in other flood potential. This is the land cover base classification potential base on land form classification which means permanent water and high potential natural levee is moderate potential, lower terrace is low potential and higher terrace is low potential. So, we need to analyze the evaluation which we have been generated. So, this is the field data collected during the flood 2007 and 1999. So, we have carried out a field work to collect this data from the pillars of flood pillars. So, flood potential base done raised land cover change. So, this is the land cover change of 2007 which we have been already done and 2007 land cover change is extracted from rapid eye image. So, recent land cover changes also been investigated by field work. This photo taken by which this build up areas before it was agricultural areas or paddy fields now it became build up. And this is also some of the constructing bridges or something like that. This same method we have used to get the flood potential base on land cover changes which is used rapid eye before 2007 land set image. And the same method and the done same digital elevation model used to estimate the flood potential base on land cover changes. So, in this we can see previously these areas were agriculture. Now it is staying to artificial field then it became low potential. Before it was high potential area. In the resultant discussion we evaluate the results in the right side we can see flood map in 2007 at maximum level which is a bit away from the density. So, the results show here is the project by history. And the black dots you can see the pillars collected from the field and red which is almost matching with the pillars. And one more thing this flood is due to flood and not at the extreme level of flood which was that but the density one was during the extreme flood. So, there is a slight change in the flood. This is the flood potential in 2007. High potential is a green and other areas as a low. So, in the flood potential and density and the high potential is almost matching. So, this results shows the good estimation of potential areas. And we have used our lake series in the grass areas which is used to see the water level changes during the flood. So, when you have 0.5 meter water level this much area will be covered. And if you have 1 meter then 1.5 meter 2 meter 2.5 and 3. So, during 3 meter you can see that all the pillar points are covering the maximum height which has been investigated in the field was 2.8 by the pillar survey. So, you can see that most of the pillar survey points are covering the 3 meter flooded area. And this comparison also showing the same thing which I have said just before. This is flood potential based on land cover, rice and land cover changes. And in the right side you can see the flood scenario for the probability 5 percent. It is actually a prediction done by Danang city. And you can see this area is changed to build up area and which has become low potential in our estimation. And that is not inundating during the prediction of flood. So, conclusions of our studies. We also think that this method is better than IW and RST to generate high resolution dump from field elevation points. Alipaz-Palsam-Hathers supports for creating flood denomination map easily and accurately. Flood map, dump and land cover data are much more effective in life and classification in our estimation estimating flood potential. The methodology developed in this study would be used in life relief areas. Future where we have to get more about these conclusions introduced by man-made objects like roads and bridges, developing more coastal algorithm for micro-landform classification in this low land area, flood simulation and flood depth estimation using digital elevation model, rainfall, river discharger and land cover data. Thank you. Yeah, here we use grass. Yeah, I look at show, it's okay, here we have time to talk. I don't know if you have anything else to expect. Now we have to talk about the version, but we have used one program called B-Solvation here to estimate which is a fault-run based one. We are talking about processor. What are the functions in the grass, if you are using them in less complex environment, why are you using them from the command line or running a Python script? Both we use, but interestingly we have just used the modules which are available, sometimes we do not develop but integrate with Python and make some more calculation or something like that. You are the last speaker, so your time, so I don't have to stop my session here. Maybe I can stop. I am teaching my class with grass as well as TGIS, sometimes I am using ArcGIS and LSE model, but I am still, I also look at this, it will come out, the name of this, open source and stage revolution. It will come out, but still I am out doubting about open source of TGIS. There are so many other kinds of TGIS, probably I am not very good at operating TGIS and the class as well. So how about your opinion, audience opinion? It is reliable, open TGIS, I wrote some text book yesterday, I am curious. No, when you get bugs I think you learn more. I was also using ArcGIS before, I didn't know what is happening inside, I was just clicking and clicking. Now I know something, not everything, but I know something what is happening inside. When I get bugs, forever. Can you use this still, commercial software? No, I don't use now. I said one over study. In fact the grass is very stable and it is very fast. I have never used that this in my life, so I cannot tell you, very large is expected. Grass works quite well and of course QGIS also uses many of the grass, so it should work as well. My experience is I started using ArcGIS and I learned GIS from ArcGIS, but I stopped using it quite a year after I started and then I used only this grass. No problem. So grass is great and it is much more, I think it is no stepping on the level of speed, but grass is much more stable than QGIS. Imagine. It is much more stable, but it is a difference, there is two different, it is depending on what you are doing. So I am using QGIS for some tasks, but mostly grass because it is safer or it is more reliable. If you get a problem, you should send an email and then respond in one of the ways. If you get a bug and you should run it to a Linux computer. One more problem is in ArcGIS, production is okay. But QGIS, you have a production problem, it is okay. In fact, I think the two different grass, no problem, no production, same projection. Is there any Korean? Korean is not, I am not expert in QGIS, but is there any, all that there are. Actually, Korean production is not okay, that is my own experience, but I am not sure. Is there any other Korean, who shares the same problem? No Korean. There is no expert in. I think there is one problem that is not Korean. In general, they are not so good in making shiny user interfaces and less marketing codes for others. So one project, when you do cool algorithms and technologies and processing chains and command lines, and you use a button, they find somewhere, so it is, I think, for country software, there is a lot of technology. How it works, it is an experienced user, and yeah, so that is just the biggest problem. Okay, thank you. So he was, he is actually, okay. Anyway, I was so happy you moved. So this session is over, and any announcements from the, or any local announcement? Maybe you have a keynote, we have a keynote speech, before we end. Later on, not this moment. No, we have a keynote speech. Okay, next session is over. See you again. Thank you.
 Question number one, my name is Vinay. I am a PhD candidate from Osaka City University.  This time I have been joined by Tati and Tran. She is also a PhD candidate from my university.  Because of our personal commitments, she could not manage to come here, so I will be presenting.  I hope that I could manage your questions.  Evaluating flood hazard potential in the noncity with the initial first 4G that is the title today.  So, in the presentation I will briefly talk about the study area and the data sets where I am.  And in methodology there are three methodologies that have been proposed to generate.  In data preparation it is a generation of digitalization model from our application polls.  And in flood inundation map we create from 2007 Palsar data.  And after that we will do characterization topographic features and find the relation between flood inundation.  Then we will evaluate the result with field data as well as the simulation.  So, this is the UGA2 by the UGA based in Vietnam during flooding.  This place is affected by flood in the floods.  So, this is the coastal lowland area. You can see the figure here.  This is the name.  This is just described in the evaluation data we have collected.  We have not collected actually the information that you see from Vietnam is collected.  So, and this is the Palsar data which have been collected just before the flooding.  And this is the Palsar data which is collected during the flood.  And you can see the change.  This is the same data which is used to class the land cover.  This is also before the flood.  And this is the rapidization data which is also used to show the land cover, recent land cover changes.  So, these are the data sets we have.  This is again the same elevation points which we are using to generate high accuracy digitalization model.  So, this is BS Horizon data which is proposed by NaNoWgAkli.  He is one of the others in this paper.  So, this is it is creating a bitmap which the points you have it is an interpolation method.  And the only simple you have you just over on the bitmap and you will be having equality and inequality constraint.  Which means that wherever you have you have your points I mean elevation points as vector that whatever.  So, these points wherever you have the points the same pixel will be considered as 0 which means you have 0.  And the other places it will be have any inequality constraint which means the range of the data which we have collected here is 0 to 10 meter.  So, we need to control the points when you do interpolation between 0 and 10.  You don't want to underestimate or underestimate.  So, this inequality constraint as if it is it means that minus 1 and minus minus 1 and plus 1 we give it means that minus 1 is less than 10 and 1 is greater than 0.  Which needs to control the points between range of 0 and 10 meter.  This is a graph which actually is this is a graph which to show the test of optimal parameter which which have to get good accuracy digital detection model.  And the red red line is Jx which means that so controls smoothness of surface and Rx is controls violation of constraints data.  Which means if you if you have we have made 3 zones and B zones is giving good good accuracy results.  So, we have Jx axis you have alpha and Y axis you have Jx and Rx.  So, we have selected we want to estimate the alpha and Mx and My is nothing but it is the size of the tail.  Even if you change the tail size the estimation results can be changed.  So, here we estimate an optimal parameter.  So, the alpha is this much and Mx is this much, My is this much and you can see the when we compare with the results.  Here minimum should be close to 0 and maximum should be close to 10 because our data range is that much.  And Rx is also showing better in this optimal parameter.  So, we fix this as the optimal parameter which can be used for further studies when you use BS horizon algorithm to interpolate.  And this is the 5 meter resolution which is produced.  And then we compare with other algorithm which has been used by so many people IW and RST.  The BS horizon algorithm gives better R score and RMSE.  So, this is just a diverse scatter plot to show the correlation of BS horizon model with the ground truth data.  So, since we have collected we have generated good accuracy digitalization model.  We are going to estimate the inundation area by using pulsar data.  So, using normalised back scattering coefficient, this was the equation used to generate the normalised back scattering coefficient from a low spacer data.  So, while this image itself we can identify some of the part which is already covered by water inundated.  So, normalised back scattering coefficient estimated by 2007 September data and October data which is during the flood.  And we just difference the NNDC and you get this result.  And this can be considered as the flood inundation but it has some anomalies and we have to remove it.  So, we have used digital animation used to integrate that which means that the maximum water line during the flood was around 3 meter.  So, we have removed the other other masses of more than 3 meter.  So, the final output will be looking like this.  So, this is almost matching with the pulsar.  So, then we are estimated the land per classification map to estimate the topographic features.  So, green cover is agriculture, yellow is built up and science water and brown is burnt land here.  And the area is covered by the naturalization is more mass from agriculture area and built up.  Water is of course, it is a permanent water and the land.  So, the percentage of inundation you can see over there.  This is the two extracts and geomorphological features using a cross section which means that HD means higher terrace which if you have above,  then higher than 4 meter which is considered as higher terrain and this is lower terrain.  If you have flood basin which is lower than 2 meter and this is again lower terrain and flood basin and then natural way you can see the figure.  And here, now we have three kinds of input which is digitalization and land cover map,  then pulsar and from the pulsar aqua inundation map.  So, these all three input data we have rule based classification structure to find the outside potential, flood outside potential.  Here, permanent water if permanent water this is the rule like permanent if it is land cover water then it is permanent water.  And in case of flood basin, land cover if land cover is of agriculture or not and or flood map is flooded or in the dam it is less than 2 meter.  Then you can consider that area as flood basin.  And in case of natural levee if you can cover water, land cover is natural water and the distance to the river channel should be less than 5 meter and the dam should be less than 3 meter.  Then you can consider it as a natural levee and lower terrace area also the same if it is non water and dam less than 4 meter and higher terrace if it is greater than 4 meter.  So, that way you are classified.  So, using Hari Amal et al.  So, we made a flood potential if it is permanent water and water level raised but no damage such case there is no flood damage.  And if submerged for long time with deep water which is high flood potential and submerged in other flood which is moderate which has moderate potential flood.  Submissive flood in major flood which is low potential and never submerged in other flood potential.  This is the land cover base classification potential base on land form classification which means permanent water and high potential natural levee is moderate potential, lower terrace is low potential and higher terrace is low potential.  So, we need to analyze the evaluation which we have been generated.  So, this is the field data collected during the flood 2007 and 1999.  So, we have carried out a field work to collect this data from the pillars of flood pillars.  So, flood potential base done raised land cover change.  So, this is the land cover change of 2007 which we have been already done and 2007 land cover change is extracted from rapid eye image.  So, recent land cover changes also been investigated by field work.  This photo taken by which this build up areas before it was agricultural areas or paddy fields now it became build up.  And this is also some of the constructing bridges or something like that.  This same method we have used to get the flood potential base on land cover changes which is used rapid eye before 2007 land set image.  And the same method and the done same digital elevation model used to estimate the flood potential base on land cover changes.  So, in this we can see previously these areas were agriculture.  Now it is staying to artificial field then it became low potential.  Before it was high potential area.  In the resultant discussion we evaluate the results in the right side we can see flood map in 2007 at maximum level which is a bit away from the density.  So, the results show here is the project by history.  And the black dots you can see the pillars collected from the field and red which is almost matching with the pillars.  And one more thing this flood is due to flood and not at the extreme level of flood which was that but the density one was during the extreme flood.  So, there is a slight change in the flood.  This is the flood potential in 2007.  High potential is a green and other areas as a low.  So, in the flood potential and density and the high potential is almost matching.  So, this results shows the good estimation of potential areas.  And we have used our lake series in the grass areas which is used to see the water level changes during the flood.  So, when you have 0.5 meter water level this much area will be covered.  And if you have 1 meter then 1.5 meter 2 meter 2.5 and 3.  So, during 3 meter you can see that all the pillar points are covering the maximum height which has been investigated in the field was 2.8 by the pillar survey.  So, you can see that most of the pillar survey points are covering the 3 meter flooded area.  And this comparison also showing the same thing which I have said just before.  This is flood potential based on land cover, rice and land cover changes.  And in the right side you can see the flood scenario for the probability 5 percent.  It is actually a prediction done by Danang city.  And you can see this area is changed to build up area and which has become low potential in our estimation.  And that is not inundating during the prediction of flood.  So, conclusions of our studies.  We also think that this method is better than IW and RST to generate high resolution dump from field elevation points.  Alipaz-Palsam-Hathers supports for creating flood denomination map easily and accurately.  Flood map, dump and land cover data are much more effective in life and classification in our estimation estimating flood potential.  The methodology developed in this study would be used in life relief areas.  Future where we have to get more about these conclusions introduced by man-made objects like roads and bridges,  developing more coastal algorithm for micro-landform classification in this low land area,  flood simulation and flood depth estimation using digital elevation model, rainfall, river discharger and land cover data.  Thank you.  Yeah, here we use grass.  Yeah, I look at show, it's okay, here we have time to talk.  I don't know if you have anything else to expect.  Now we have to talk about the version, but we have used one program called B-Solvation here to estimate which is a fault-run based one.  We are talking about processor.  What are the functions in the grass, if you are using them in less complex environment, why are you using them from the command line or running a Python script?  Both we use, but interestingly we have just used the modules which are available, sometimes we do not develop but integrate with Python and make some more calculation or something like that.  You are the last speaker, so your time, so I don't have to stop my session here.  Maybe I can stop.  I am teaching my class with grass as well as TGIS, sometimes I am using ArcGIS and LSE model, but I am still, I also look at this, it will come out, the name of this, open source and stage revolution.  It will come out, but still I am out doubting about open source of TGIS.  There are so many other kinds of TGIS, probably I am not very good at operating TGIS and the class as well.  So how about your opinion, audience opinion?  It is reliable, open TGIS, I wrote some text book yesterday, I am curious.  No, when you get bugs I think you learn more.  I was also using ArcGIS before, I didn't know what is happening inside, I was just clicking and clicking.  Now I know something, not everything, but I know something what is happening inside.  When I get bugs, forever.  Can you use this still, commercial software?  No, I don't use now.  I said one over study.  In fact the grass is very stable and it is very fast.  I have never used that this in my life, so I cannot tell you, very large is expected.  Grass works quite well and of course QGIS also uses many of the grass, so it should work as well.  My experience is I started using ArcGIS and I learned GIS from ArcGIS, but I stopped using it quite a year after I started and then I used only this grass.  No problem.  So grass is great and it is much more, I think it is no stepping on the level of speed, but grass is much more stable than QGIS.  Imagine.  It is much more stable, but it is a difference, there is two different, it is depending on what you are doing.  So I am using QGIS for some tasks, but mostly grass because it is safer or it is more reliable.  If you get a problem, you should send an email and then respond in one of the ways.  If you get a bug and you should run it to a Linux computer.  One more problem is in ArcGIS, production is okay.  But QGIS, you have a production problem, it is okay.  In fact, I think the two different grass, no problem, no production, same projection.  Is there any Korean?  Korean is not, I am not expert in QGIS, but is there any, all that there are.  Actually, Korean production is not okay, that is my own experience, but I am not sure.  Is there any other Korean, who shares the same problem?  No Korean.  There is no expert in.  I think there is one problem that is not Korean.  In general, they are not so good in making shiny user interfaces and less marketing codes for others.  So one project, when you do cool algorithms and technologies and processing chains and command lines,  and you use a button, they find somewhere, so it is, I think, for country software, there is a lot of technology.  How it works, it is an experienced user, and yeah, so that is just the biggest problem.  Okay, thank you.  So he was, he is actually, okay.  Anyway, I was so happy you moved.  So this session is over, and any announcements from the, or any local announcement?  Maybe you have a keynote, we have a keynote speech, before we end.  Later on, not this moment.  No, we have a keynote speech.  Okay, next session is over.  See you again.  Thank you.
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Question number one, my name is Vinay. I am a PhD candidate from Osaka City University. This time I have been joined by Tati and Tran. She is also a PhD candidate from my university. Because of our personal commitments, she could not manage to come here, so I will be presenting. I hope that I could manage your questions. Evaluating flood hazard potential in the noncity with the initial first 4G that is the title today. So, in the presentation I will briefly talk about the study area and the data sets where I am. And in methodology there are three methodologies that have been proposed to generate. In data preparation it is a generation of digitalization model from our application polls. And in flood inundation map we create from 2007 Palsar data. And after that we will do characterization topographic features and find the relation between flood inundation. Then we will evaluate the result with field data as well as the simulation. So, this is the UGA2 by the UGA based in Vietnam during flooding. This place is affected by flood in the floods. So, this is the coastal lowland area. You can see the figure here. This is the name. This is just described in the evaluation data we have collected. We have not collected actually the information that you see from Vietnam is collected. So, and this is the Palsar data which have been collected just before the flooding. And this is the Palsar data which is collected during the flood. And you can see the change. This is the same data which is used to class the land cover. This is also before the flood. And this is the rapidization data which is also used to show the land cover, recent land cover changes. So, these are the data sets we have. This is again the same elevation points which we are using to generate high accuracy digitalization model. So, this is BS Horizon data which is proposed by NaNoWgAkli. He is one of the others in this paper. So, this is it is creating a bitmap which the points you have it is an interpolation method. And the only simple you have you just over on the bitmap and you will be having equality and inequality constraint. Which means that wherever you have you have your points I mean elevation points as vector that whatever. So, these points wherever you have the points the same pixel will be considered as 0 which means you have 0. And the other places it will be have any inequality constraint which means the range of the data which we have collected here is 0 to 10 meter. So, we need to control the points when you do interpolation between 0 and 10. You don't want to underestimate or underestimate. So, this inequality constraint as if it is it means that minus 1 and minus minus 1 and plus 1 we give it means that minus 1 is less than 10 and 1 is greater than 0. Which needs to control the points between range of 0 and 10 meter. This is a graph which actually is this is a graph which to show the test of optimal parameter which which have to get good accuracy digital detection model. And the red red line is Jx which means that so controls smoothness of surface and Rx is controls violation of constraints data. Which means if you if you have we have made 3 zones and B zones is giving good good accuracy results. So, we have Jx axis you have alpha and Y axis you have Jx and Rx. So, we have selected we want to estimate the alpha and Mx and My is nothing but it is the size of the tail. Even if you change the tail size the estimation results can be changed. So, here we estimate an optimal parameter. So, the alpha is this much and Mx is this much, My is this much and you can see the when we compare with the results. Here minimum should be close to 0 and maximum should be close to 10 because our data range is that much. And Rx is also showing better in this optimal parameter. So, we fix this as the optimal parameter which can be used for further studies when you use BS horizon algorithm to interpolate. And this is the 5 meter resolution which is produced. And then we compare with other algorithm which has been used by so many people IW and RST. The BS horizon algorithm gives better R score and RMSE. So, this is just a diverse scatter plot to show the correlation of BS horizon model with the ground truth data. So, since we have collected we have generated good accuracy digitalization model. We are going to estimate the inundation area by using pulsar data. So, using normalised back scattering coefficient, this was the equation used to generate the normalised back scattering coefficient from a low spacer data. So, while this image itself we can identify some of the part which is already covered by water inundated. So, normalised back scattering coefficient estimated by 2007 September data and October data which is during the flood. And we just difference the NNDC and you get this result. And this can be considered as the flood inundation but it has some anomalies and we have to remove it. So, we have used digital animation used to integrate that which means that the maximum water line during the flood was around 3 meter. So, we have removed the other other masses of more than 3 meter. So, the final output will be looking like this. So, this is almost matching with the pulsar. So, then we are estimated the land per classification map to estimate the topographic features. So, green cover is agriculture, yellow is built up and science water and brown is burnt land here. And the area is covered by the naturalization is more mass from agriculture area and built up. Water is of course, it is a permanent water and the land. So, the percentage of inundation you can see over there. This is the two extracts and geomorphological features using a cross section which means that HD means higher terrace which if you have above, then higher than 4 meter which is considered as higher terrain and this is lower terrain. If you have flood basin which is lower than 2 meter and this is again lower terrain and flood basin and then natural way you can see the figure. And here, now we have three kinds of input which is digitalization and land cover map, then pulsar and from the pulsar aqua inundation map. So, these all three input data we have rule based classification structure to find the outside potential, flood outside potential. Here, permanent water if permanent water this is the rule like permanent if it is land cover water then it is permanent water. And in case of flood basin, land cover if land cover is of agriculture or not and or flood map is flooded or in the dam it is less than 2 meter. Then you can consider that area as flood basin. And in case of natural levee if you can cover water, land cover is natural water and the distance to the river channel should be less than 5 meter and the dam should be less than 3 meter. Then you can consider it as a natural levee and lower terrace area also the same if it is non water and dam less than 4 meter and higher terrace if it is greater than 4 meter. So, that way you are classified. So, using Hari Amal et al. So, we made a flood potential if it is permanent water and water level raised but no damage such case there is no flood damage. And if submerged for long time with deep water which is high flood potential and submerged in other flood which is moderate which has moderate potential flood. Submissive flood in major flood which is low potential and never submerged in other flood potential. This is the land cover base classification potential base on land form classification which means permanent water and high potential natural levee is moderate potential, lower terrace is low potential and higher terrace is low potential. So, we need to analyze the evaluation which we have been generated. So, this is the field data collected during the flood 2007 and 1999. So, we have carried out a field work to collect this data from the pillars of flood pillars. So, flood potential base done raised land cover change. So, this is the land cover change of 2007 which we have been already done and 2007 land cover change is extracted from rapid eye image. So, recent land cover changes also been investigated by field work. This photo taken by which this build up areas before it was agricultural areas or paddy fields now it became build up. And this is also some of the constructing bridges or something like that. This same method we have used to get the flood potential base on land cover changes which is used rapid eye before 2007 land set image. And the same method and the done same digital elevation model used to estimate the flood potential base on land cover changes. So, in this we can see previously these areas were agriculture. Now it is staying to artificial field then it became low potential. Before it was high potential area. In the resultant discussion we evaluate the results in the right side we can see flood map in 2007 at maximum level which is a bit away from the density. So, the results show here is the project by history. And the black dots you can see the pillars collected from the field and red which is almost matching with the pillars. And one more thing this flood is due to flood and not at the extreme level of flood which was that but the density one was during the extreme flood. So, there is a slight change in the flood. This is the flood potential in 2007. High potential is a green and other areas as a low. So, in the flood potential and density and the high potential is almost matching. So, this results shows the good estimation of potential areas. And we have used our lake series in the grass areas which is used to see the water level changes during the flood. So, when you have 0.5 meter water level this much area will be covered. And if you have 1 meter then 1.5 meter 2 meter 2.5 and 3. So, during 3 meter you can see that all the pillar points are covering the maximum height which has been investigated in the field was 2.8 by the pillar survey. So, you can see that most of the pillar survey points are covering the 3 meter flooded area. And this comparison also showing the same thing which I have said just before. This is flood potential based on land cover, rice and land cover changes. And in the right side you can see the flood scenario for the probability 5 percent. It is actually a prediction done by Danang city. And you can see this area is changed to build up area and which has become low potential in our estimation. And that is not inundating during the prediction of flood. So, conclusions of our studies. We also think that this method is better than IW and RST to generate high resolution dump from field elevation points. Alipaz-Palsam-Hathers supports for creating flood denomination map easily and accurately. Flood map, dump and land cover data are much more effective in life and classification in our estimation estimating flood potential. The methodology developed in this study would be used in life relief areas. Future where we have to get more about these conclusions introduced by man-made objects like roads and bridges, developing more coastal algorithm for micro-landform classification in this low land area, flood simulation and flood depth estimation using digital elevation model, rainfall, river discharger and land cover data. Thank you. Yeah, here we use grass. Yeah, I look at show, it's okay, here we have time to talk. I don't know if you have anything else to expect. Now we have to talk about the version, but we have used one program called B-Solvation here to estimate which is a fault-run based one. We are talking about processor. What are the functions in the grass, if you are using them in less complex environment, why are you using them from the command line or running a Python script? Both we use, but interestingly we have just used the modules which are available, sometimes we do not develop but integrate with Python and make some more calculation or something like that. You are the last speaker, so your time, so I don't have to stop my session here. Maybe I can stop. I am teaching my class with grass as well as TGIS, sometimes I am using ArcGIS and LSE model, but I am still, I also look at this, it will come out, the name of this, open source and stage revolution. It will come out, but still I am out doubting about open source of TGIS. There are so many other kinds of TGIS, probably I am not very good at operating TGIS and the class as well. So how about your opinion, audience opinion? It is reliable, open TGIS, I wrote some text book yesterday, I am curious. No, when you get bugs I think you learn more. I was also using ArcGIS before, I didn't know what is happening inside, I was just clicking and clicking. Now I know something, not everything, but I know something what is happening inside. When I get bugs, forever. Can you use this still, commercial software? No, I don't use now. I said one over study. In fact the grass is very stable and it is very fast. I have never used that this in my life, so I cannot tell you, very large is expected. Grass works quite well and of course QGIS also uses many of the grass, so it should work as well. My experience is I started using ArcGIS and I learned GIS from ArcGIS, but I stopped using it quite a year after I started and then I used only this grass. No problem. So grass is great and it is much more, I think it is no stepping on the level of speed, but grass is much more stable than QGIS. Imagine. It is much more stable, but it is a difference, there is two different, it is depending on what you are doing. So I am using QGIS for some tasks, but mostly grass because it is safer or it is more reliable. If you get a problem, you should send an email and then respond in one of the ways. If you get a bug and you should run it to a Linux computer. One more problem is in ArcGIS, production is okay. But QGIS, you have a production problem, it is okay. In fact, I think the two different grass, no problem, no production, same projection. Is there any Korean? Korean is not, I am not expert in QGIS, but is there any, all that there are. Actually, Korean production is not okay, that is my own experience, but I am not sure. Is there any other Korean, who shares the same problem? No Korean. There is no expert in. I think there is one problem that is not Korean. In general, they are not so good in making shiny user interfaces and less marketing codes for others. So one project, when you do cool algorithms and technologies and processing chains and command lines, and you use a button, they find somewhere, so it is, I think, for country software, there is a lot of technology. How it works, it is an experienced user, and yeah, so that is just the biggest problem. Okay, thank you. So he was, he is actually, okay. Anyway, I was so happy you moved. So this session is over, and any announcements from the, or any local announcement? Maybe you have a keynote, we have a keynote speech, before we end. Later on, not this moment. No, we have a keynote speech. Okay, next session is over. See you again. Thank you.
candidate from Osaka City University. This time I hazard potential in the noncity with the initial area and the data sets where I am. And in have been proposed to generate. In data inundation map we create from 2007 Palsar data. the result with field data as well as the in Vietnam during flooding. This place is coastal lowland area. You can see the figure used to class the land cover. This is also before which we are using to generate high accuracy IW and RST. The BS horizon algorithm gives better estimated by 2007 September data and October data considered as the flood inundation but it has two extracts and geomorphological features using generated. So, this is the field data collected this is the land cover change of 2007 which we see flood map in 2007 at maximum level which is a flood and not at the extreme level of flood which High potential is a green and other areas as a was 2.8 by the pillar survey. So, you can see that most of the pillar survey points are based on land cover, rice and land cover changes. scenario for the probability 5 percent. It is actually a prediction done by Danang city. And you can see this area is changed to build up area than IW and RST to generate high resolution dump methodology developed in this study would be used in life relief areas. Future where we have to get micro-landform classification in this low land