Flood Risk Map Using Weighted Multi-Criteria Analysis (MCA)
Flooding is one of the most devastating natural disasters, affecting millions of people worldwide. To mitigate its impact, accurate flood risk assessment is crucial. One effective method for generating a flood risk map is Weighted Multi-Criteria Analysis (MCA) combined with fuzzy logic to handle uncertainty in spatial data.
Flood inundation maps for Rivers, Delta, Akwaibom, Crossriver, Entire Ops Area (Note there may be a delay in map rendering)
Weighted MCA: A Technical Overview
Weighted Multi-Criteria Analysis (MCA) is a decision-making tool that evaluates multiple factors (criteria) by assigning weights based on their relative importance. In flood risk mapping, MCA helps integrate various environmental and hydrological datasets to produce a composite risk score.
Steps in Weighted MCA for Flood Risk Mapping:
- Criteria Selection – Identify key factors influencing flood risk (e.g., rainfall, elevation, soil type).
- Data Standardization – Normalize all datasets to a common scale (0-1 or 1-5).
- Weight Assignment – Assign weights based on each factor’s contribution to flooding.
- Aggregation – Combine weighted layers using overlay analysis.
- Validation – Compare results with historical flood data for accuracy.
Fuzzy Membership Functions for Flood Risk Assessment
Fuzzy logic helps manage uncertainty in spatial data by converting crisp values into a continuous risk scale (0 to 1). Two types of fuzzy membership functions are used:
- Positive Linear Fuzzy Membership (Direct Relationship)
- Definition: As the input value increases, flood risk increases.
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Examples:
- Precipitation amount
- Rainfall intensity
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Characteristics (for this project)
- Minimum value = 1 (lowest risk)
- Maximum value = 5 (highest risk)
- Linear transition between them
- Negative Linear Fuzzy Membership (Inverse Relationship)
- Definition: As the input value increases, flood risk decreases.
-
Examples:
- Distance from rivers
- Elevation (higher altitude = lower risk)
- Soil permeability (higher permeability = lower risk)
-
Characteristics:
- Minimum value = 1 (highest risk)
- Maximum value = 5 (lowest risk)
- Linear transition between them
Practical Considerations for Fuzzy Logic
- Threshold Selection: Choose min/max values based on hydrological studies (e.g., precipitation threshold for flood initiation).
- Data Normalization: Ensure all fuzzy rasters are on the same scale (0-1) before combining.
- Combining Criteria: Use fuzzy operators (e.g., fuzzy AND, OR, or weighted overlay).
- Local Adjustments: Modify scores based on local conditions (e.g., urban drainage efficiency, crop types like rice paddies).
Data Preparation & Resampling Techniques
To ensure consistency, all datasets where resampled to the same resolution (10x10m grid) and coordinate system.
Some of the Key Resampling Methods are shown below
Method |
Best For |
Description |
Nearest Neighbour |
Categorical data (land use) |
Preserves original values |
Bilinear Interpolation |
Continuous data (elevation) |
Smoother output |
Cubic Convolution |
Continuous data (better edges) |
Higher precision |
Average |
Down sampling large datasets |
Reduces noise |
Important Considerations
✔ Coordinate System: All rasters must be in the same CRS (Coordinate Reference System).
✔ Snap Raster: Ensures perfect cell alignment.
✔ Performance Tips:
- Use LZW compression to reduce file size.
- Enable multithreaded processing for large datasets.
For this project the Nearest Neighbour and Bilinear Interpolation methods were used
- Key Datasets used for the AOI & Their Weights in Flood Risk Modelling
The flood risk model integrates multiple datasets, each weighted based on its influence:
Dataset |
Source |
Weight |
Reclassified Scale (1-5) |
Precipitation (June-July avg.) |
WorldClim |
0.30 |
1 (low) – 5 (high) |
Nearness to River |
Flow Accumulation |
0.25 |
1 (far) – 5 (close) |
Slope (from DEM) |
SRTM 30m DEM |
0.15 |
1 (steep) – 5 (flat) |
Land Use/Land Cover |
Sentinel-2 |
0.10 |
1 (urban) – 5 (wetlands) |
Elevation (DEM) |
SRTM 30m DEM |
0.10 |
1 (high) – 5 (low) |
Drainage Density |
River Network |
0.05 |
1 (low) – 5 (high) |
Clay Content (Soil Type) |
Soil Grids |
0.05 |
1 (sandy) – 5 (clay-rich) |
Interpretation of Weights
- Precipitation (30%) and Nearness to River (25%) have the highest influence.
- Slope (15%) affects water runoff speed (flatter areas = higher risk).
- Land Use (10%) accounts for impervious surfaces (urban areas) vs. absorbent land (forests).
Flood Inundation mapping
In addition to the flood risk map, flood inundation maps for different water levels are also generated. The water levels are historical water levels measured between (2012-2017) at Nun River. These water levels are then referenced to the SRTM 30m DEM to ascertain which areas lie under water at different
Flood inundation maps for Rivers, Delta, Akwaibom, Crossriver, Entire Ops Area (Note there may be a delay in map rendering)
Conclusion:
By combining Weighted MCA, fuzzy logic, and high-resolution datasets, we can generate an accurate flood risk map. Key takeaways:
- Fuzzy logic helps handle uncertainty in spatial data.
- Resampling ensures consistency across datasets.
- Weighted overlay prioritizes the most critical flood factors.
Such maps are vital for urban planning, disaster preparedness, and insurance risk assessment. Future improvements could integrate real-time rainfall data and machine learning for dynamic flood prediction.
Caveats & Local Considerations
While this model provides a strong foundation for flood risk assessment, real-world conditions may require additional adjustments. Key considerations include:
Urban Infrastructure & Drainage Systems
- Highly developed cities may have better drainage, reducing flood risk despite high precipitation.
- Informal settlements with poor drainage may flood even with moderate rainfall.
- Recommendation: Adjust weights for urban areas based on drainage efficiency studies.
Local Hydrological Features
- Small streams or artificial canals not captured in global datasets may influence flooding.
- Groundwater levels (not included in this model) can exacerbate flooding in some regions.
- Recommendation: Incorporate local hydrological surveys for higher accuracy.
Climate Change & Extreme Weather
- Increasing rainfall variability may make historical precipitation data less reliable.
- Recommendation: Integrate climate projections into long-term flood risk models.
Human Interventions
- Dams, levees, and flood barriers can drastically alter flood patterns.
- Deforestation and land degradation may increase runoff and flood risk.
- Recommendation: Include human-modified landscapes in risk assessments.
Validation with Historical Data
- Past flood events should be used to calibrate and validate the model.
- Community knowledge (e.g., local flood-prone zones) can refine risk maps.
Need a flood risk analysis for your assets? Contact our geospatial risk & decisions specialists for a customized assessment. (info@leelaurelgs.com, +2348062905881, +234817409633)