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|Title:||FLOOD RISK MAPPING USING REMOTE SENSING AND HYDRAULIC MODELLING IN KHLONG BANG SAPHAN YAI RIVER BASIN, THAILAND|
การทำแผนที่ความเสี่ยงน้ำท่วมโดยใช้การสำรวจระยะไกล (Remote Sensing) และแบบจำลองทางชลศาสตร์ (Hydraulic Modelling) ในพื้นที่ลุ่มน้ำคลองบางสะพานใหญ่ ประเทศไทย
Burapha University. Faculty of Geoinformatics
|Keywords:||Flood Risk Mapping|
|Abstract:||Over the past decades, floods have been one of the most common and damaging natural disasters in Thailand and in my study area. The Khlong Bang Saphan Yai River Basin is in the southern region of Thailand, which is influenced by the monsoons and rainstorms, resulting in exposure to flooding yearly. As a result, it impacts human lives, economic loss, and severe damage to communities and agriculture. Significantly, floods pose a threat to the exposures and vulnerabilities in the Khlong Bang Saphan Yai River Basin. Therefore, this research focuses on flood risk assessment in the river basin for flood management and risk mitigation.
In this research, the Sentinel-1 SAR remote sensing techniques were combined with the HEC-RAS 1D modelling for flood risk mapping of the Khlong Bang Saphan Yai river basin in Thailand for flood events. The flood inundation maps from the HEC-RAS model were validated by using the flood extent maps from the Sentinel-1 SAR remotely sensed technique based on the histogram thresholding analysis.
The HEC-RAS models were used for flood inundation simulation in this study area. It also used the topographic data (ALOS 30m DSM) for defining the geometry data (network river, riverbank, flow path, cross-section, and hydraulic structures) input into the HEC-RAS model. The hydraulic model was used to simulate the unsteady flow of the expected flood hydrographs. The observed water level data were used to calibrate and validate the performance of the HEC-RAS model. It also requires the geometry data, Manning roughness coefficient “n”, and daily discharge data at the upstream (GT.7) station, which is the input data. Such flood simulation results (2018 and 2019) were calibrated and validated using four performance indicators (NSE, RMSE, R2, and PBIAS) and the water level and discharge data from the observed station at the downstream station (GT.20) for performing the HEC-RAS model.
Moreover, the flood inundation maps were required to be validated using the flood extent area from the Sentinel-1 SAR imagery data for comparison and analysis. The Sentinel-1 SAR data were used to extract flood extent by using the histogram thresholding analysis in the SNAP software, and the flood extent results were used to calibrate and validate flood inundation maps from the HEC-RAS models. The flood extent maps from the Sentinel-1 SAR technique were compared and referenced by using the permanent water data from the Sentinel-1 data technique based after flooding. When the validation outputs for the 2019 flood event were compared to the flood extent maps derived from SAR image data, the overlapping area for the 2019 flood event was 23.18 percent. And it is a closed verified check for the flood area, with the total flood inundation area of the 2019 flood event being 0.27 sq.km. The flood inundation from the HEC-RAS model is like the flood extent from the Sentinel-1 SAR images. The research results serve as a valuable reference to support policy and decision-making for future planning and development in the current study area.|
|Description:||Master Degree of Science (M.Sc.)|
|Appears in Collections:||Faculty of Geoinformatics|
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