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Update flooding.md
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fix heading format
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aragong authored Jun 4, 2024
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Expand Up @@ -48,7 +48,7 @@ The methodology consists of creating a surrogate statistical model based on a se
3. Numerical modeling
4. Statistical model training
5. Statistical model evaluation
## 1 Numerical model setup
### 1. Numerical model setup
A 2DH XBeach simulation in a 10x10m grid is setup considering both topobathymetry of the San Lorenzo beach in Gijón consisting of 45k numerical cells.
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## 2 Event selection
### 2. Event selection
20 historical extreme events are selected considering a peaks over treshold (POT) method. 100 events are evaluated by combining the selected historical cases with 5 SLR scenarios.
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## 3 Numerical simulation
### 3. Numerical simulation
The numerical simulation of the 100 cases yields a training dataset consisting of 100 realizations of the 45k grid (representing the Y predictand variable) against the 100 wave and water level parameters representing the storms (X predictor variable).
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## 4 Training the statistical model
### 4. Training the statistical model
The statistical model consists of projecting the training predictand dataset (N=45000xM=100) into a reduced subset using principal component analysis (PCA).

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## 5 Model evaluation
### 5. Model evaluation
The surrogate GP model is evaluated against the true numerical solution and results highlight a slight underprediction of the total flooded area.
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## 6 Way forward
### 6. Way forward
In order to improve the results we are exploring the following:
* Crop the training dataset to the inland area (main interest is overland flooding)
* Improve the GP surrogate model
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