| Project/Area Number |
23K04328
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Review Section |
Basic Section 25030:Disaster prevention engineering-related
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
AHMED M.Saber 京都大学, 防災研究所, 特定准教授 (00818403)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
角 哲也 京都大学, 防災研究所, 教授 (40311732)
カントウシュ サメ・アハメド 京都大学, 防災研究所, 教授 (70750800)
|
|---|
| Project Period (FY) |
2023-04-01 – 2026-03-31
|
| Project Status |
Granted (Fiscal Year 2023)
|
| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2025: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2024: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2023: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
|
|---|
| Keywords | global Model / Machine Learning / Flood susceptibility |
|---|
| Outline of Research at the Start |
The main outlines of the research plan:
1. Data Acquisition and Processing (identifying flood and non-flood locations)
2. Machine Learning algorithms (Model training and testing including accuracy assessment)
3. Flood susceptibility Mapping & global function(updating model and enhancement the results)
|
| Outline of Annual Research Achievements |
The project goal is to develop a Global Flood Susceptibility Map (GFSM) by using the Machine Learning (ML) model. Up to date, we have examined machine learning algorism in 10 case studies. Then we used cross-validation among the case studies from Japan (Four cases), USA (three Cases), Indonesia (One case), Egypt (One case), Vietnam (One Case), Saudi Arabia (One Case). Now we are comparing the different developed ML functions to be selected to develop the global map.
Within the current year, we are going to develop the first draft of the global flood susceptibility map.
|
| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
We have already conducted the research plan for last year, by collecting datasets, and run the models on several case studies, and now we are working on developing the best function for flood susceptibility map.
The first draft of our planned global map is expected very soon. The accuracy of the model and results are acceptable.
|
| Strategy for Future Research Activity |
The plan as follows:
1. Applying the developed ML model to the collected case studies: Japan (four cases), USA (three cases), Indonesia (one case), Egypt (one case), Vietnam (one Case), Saudi Arabia (One Case).
2. Training and testing by the cross-validation method for all cases.
3. The best function will be used to develop the first draft map for the world.
|
