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Simple and yet novel approach in flood assessment to overcome data scarcity : high quality DEM and rainfall proxies

Abstract : Many urban cities in Southeast Asia witness severe flooding associated to increasing rainfall intensity and rapid urbanization often due to poor urban planning. Two important inputs required in flood hazard assessment are: (1) high accuracy Digital Elevation Model (DEM), and (2) long rainfall record. High accuracy DEM is both expensive and time consuming to acquire. Long rainfall records for areas of interest are often not available or not sufficiently long to determine the probable extremes. This thesis presents a notably cost-effective and efficient approach to derive high accuracy DEM, and suggests proxies for long rainfall data.DEM data from a publicly accessible satellite, Shuttle Radar Topography Mission (SRTM), and Sentinel 2 multispectral imagery are selected and used to train the Artificial Neural Network (ANN) to improve the quality of the DEM. In the training of ANN, high quality observed DEM is the key leading to a well-trained ANN. The trained ANN will then be ready to efficiently and effectively generate high quality DEM, at low cost, for places where DEM data is not available.The performance of the DEM improvement scheme is evaluated in places of various land-use types (e.g. dense urban city, forested areas), and in different countries (Nice, France; Singapore; Jakarta, Indonesia) through various matrices, e.g. whenever possible visual clarity, scatter plots, Root Mean Square Error (RMSE) and/or drainage networks. The DEM resulting from the latest version of improved SRTM (iSRTM_v2 DEM) shows (1) significantly better than the original SRTM DEM, a 34 % to 57 % RMSE reduction; (2) the visual clarity is so much clearer as well; and (3) much closer drainage network with the actual. The much improved DEM allows flood modelling to proceed with high confidence.Rainfall data resulting from a high spatial resolution Regional Climate Model (RCM), Weather Research and Forecasting driven by ERA-Interim (WRF/ERAI) dataset, is extracted, analyzed, and compared its accuracy with high quality observed rainfall data of Singapore. The comparisons are performed, among others, on their Intensity-Duration-Frequency (IDF) curves, the essential design curves for flood risk assessment; they matched quite well. The rainfall data (from the RCM) are then used as proxies for Greater Jakarta (Indonesia), where no rainfall data made available, to derive the IDF curves required for the flood analysis.MIKE 21 Flow Model Flexible Mesh (MIKE 21 FM) is applied to Greater Jakarta, with input data from the above mentioned much improved DEM and precipitation proxy data, for flood simulations of 2 return periods (50- and 100-years). Finally flood maps are generated. This demonstrates the applications of the approaches/methodologies, proposed in this thesis, on catchments where most essential data for flood risk assessment (high resolution and high accuracy DEM and long and high accuracy rainfall data) are not available.This thesis should be of interest to readers in the areas of remote sensing, artificial intelligence and flood management, especially for the policy makers in proposing relevant flood mitigation measures under climate change with increasing devastating flood damages and casualties.
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Submitted on : Wednesday, February 26, 2020 - 6:08:18 PM
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  • HAL Id : tel-02492281, version 1



Dong Eon Kim. Simple and yet novel approach in flood assessment to overcome data scarcity : high quality DEM and rainfall proxies. Risques. COMUE Université Côte d'Azur (2015 - 2019), 2019. English. ⟨NNT : 2019AZUR4029⟩. ⟨tel-02492281⟩



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