A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment.
Nanda Khoirunisa1, Cheng-Yu Ku1, Chih-Yu Liu1
1Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan.
International Journal of Environmental Research and Public Health
|February 3, 2021
Summary
A new geographic information system-based artificial neural network (GANN) model accurately predicts flood susceptibility in Keelung City, Taiwan. This advanced GANN model identifies high-risk areas, crucial for urban planning and disaster mitigation.
Area of Science:
- Environmental Science
- Geoinformatics
- Artificial Intelligence
Background:
- Flood susceptibility assessment is critical for urban planning and disaster risk reduction.
- Traditional methods may not fully capture complex hydrological interactions.
- Keelung City faces significant flood risks due to its topography and weather patterns.
Purpose of the Study:
- To develop and validate a novel Geographic Information System-based Artificial Neural Network (GANN) model for flood susceptibility mapping.
- To assess flood susceptibility in Keelung City, Taiwan, using a comprehensive set of environmental and topographical factors.
- To compare the GANN model's performance against the established SOBEK model.
Main Methods:
- Utilized a Geographic Information System (GIS) to integrate various spatial data layers: elevation, slope, aspect, flow accumulation, flow direction, Topographic Wetness Index (TWI), drainage density, rainfall, and Normalized Difference Vegetation Index (NDVI).
- Employed a Back-Propagation Neural Network (BPNN) within the GANN framework to model flood susceptibility.
- Validated the model using historical flood event data from 2015-2019 (307 events) and compared results with the SOBEK model.
Main Results:
- The GANN model achieved a high correlation coefficient of 0.814, indicating satisfactory predictive accuracy.
- Flood susceptibility was categorized into five classes: Very low (60.5%), low (27.4%), moderate (8.6%), high (2.5%), and very high (1%).
- Approximately 3.5% of Keelung City, including the central business district and densely populated areas, was identified as having high to very high flood susceptibility.
Conclusions:
- The developed GANN model provides a robust and accurate method for flood susceptibility assessment.
- The findings highlight critical areas within Keelung City requiring prioritized flood mitigation strategies.
- The GANN model demonstrates superior or comparable accuracy to the SOBEK model, offering a valuable tool for hydrological risk management.
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