A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network
Abstract Many urban cities in Southeast Asia are vulnerable to climate change. However, these cities are unable to take effective countermeasures to address vulnerabilities and adaptation due to insufficient data for flood analysis. Two important inputs required in flood analysis are high accuracy Digital Elevation Model (DEM), and long term rainfall record. This paper presents an innovative and cost-effective flood hazard assessment using remote sensing technology and Artificial Neural Network (ANN) to overcome such lack of data. Shuttle Radar Topography Mission (SRTM) and multispectral imagery of Sentinel-2 are used to derive a high-accuracy DEM using ANN. The improvement of SRTM’s DEM is significant with a 42.3% of reduction on Root Mean Square Error (RMSE) which allows the flood modelling to proceed with confidence. The Intensity Duration Frequency (IDF) curves that were constructed from precipitation outputs from a Regional Climate Model (RCM) Weather Research and Forecasting (WRF) were used in this study. Design storms, calculated from these IDF curves with different return periods were then applied to numerical flood simulations to identify flood prone areas. The approach is demonstrated in a flood hazard study in Kendal Regency, Indonesia. Flood map scenarios were generated using improved SRTM and design storms of 10-, 50- and 100-year re-turn periods were constructed using the MIKE 21 hydrodynamic model. This novel approach is innovative and cost-effective for flood hazard assessment using remote sensing and ANN to overcome lack of data. The results are useful for policy makers to understand the flood issues and to proceed flood mitigation adaptation/measures in addressing the impacts of climate change.
Overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network
Abstract Many urban cities in Southeast Asia are vulnerable to climate change. However, these cities are unable to take effective countermeasures to address vulnerabilities and adaptation due to insufficient data for flood analysis. Two important inputs required in flood analysis are high accuracy Digital Elevation Model (DEM), and long term rainfall record. This paper presents an innovative and cost-effective flood hazard assessment using remote sensing technology and Artificial Neural Network (ANN) to overcome such lack of data. Shuttle Radar Topography Mission (SRTM) and multispectral imagery of Sentinel-2 are used to derive a high-accuracy DEM using ANN. The improvement of SRTM’s DEM is significant with a 42.3% of reduction on Root Mean Square Error (RMSE) which allows the flood modelling to proceed with confidence. The Intensity Duration Frequency (IDF) curves that were constructed from precipitation outputs from a Regional Climate Model (RCM) Weather Research and Forecasting (WRF) were used in this study. Design storms, calculated from these IDF curves with different return periods were then applied to numerical flood simulations to identify flood prone areas. The approach is demonstrated in a flood hazard study in Kendal Regency, Indonesia. Flood map scenarios were generated using improved SRTM and design storms of 10-, 50- and 100-year re-turn periods were constructed using the MIKE 21 hydrodynamic model. This novel approach is innovative and cost-effective for flood hazard assessment using remote sensing and ANN to overcome lack of data. The results are useful for policy makers to understand the flood issues and to proceed flood mitigation adaptation/measures in addressing the impacts of climate change.
Overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network
Kim, Dong-Eon (author) / Gourbesville, Philippe (author) / Liong, Shie-Yui (author)
Smart Water ; 4 ; 1-15
2019-01-09
15 pages
Article (Journal)
Electronic Resource
English
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