| 研究課題/領域番号 |
23K04328
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分25030:防災工学関連
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| 研究機関 | 京都大学 |
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研究代表者 |
AHMED M.Saber 京都大学, 防災研究所, 特定准教授 (00818403)
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| 研究分担者 |
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)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,810千円 (直接経費: 3,700千円、間接経費: 1,110千円)
2025年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2024年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2023年度: 2,470千円 (直接経費: 1,900千円、間接経費: 570千円)
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| キーワード | global Model / Machine Learning / Flood susceptibility |
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| 研究開始時の研究の概要 |
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)
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| 研究実績の概要 |
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.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
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.
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| 今後の研究の推進方策 |
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.
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