@misc{KimSunWendietal.2018,
  author    = {Kim, Dongeon and Sun, Yabin and Wendi, Dadiyorto and Jiang, Ze and Liong, Shie-Yui and Gourbesville, Philippe},
  title     = {Flood modelling framework for Kuching City, Malaysia},
  series = {Advances in Hydroinformatics: SimHydro 2017 - Choosing The Right Model in Applied Hydraulics},
  journal   = {Advances in Hydroinformatics: SimHydro 2017 - Choosing The Right Model in Applied Hydraulics},
  publisher = {Springer},
  address   = {Singapore},
  isbn      = {978-981-10-7218-5},
  issn      = {2364-6934},
  doi       = {10.1007/978-981-10-7218-5_39},
  pages     = {559 -- 568},
  year      = {2018},
  abstract  = {Several areas in Southeast Asia are very vulnerable to climate change and unable to take immediate/effective actions on countermeasures due to insufficient capabilities. Malaysia, in particular the east coast of peninsular Malaysia and Sarawak, is known as one of the vulnerable regions to flood disaster. Prolonged and intense rainfall, natural activities and increase in runoff are the main reasons to cause flooding in this area. In addition, topographic conditions also contribute to the occurrence of flood disaster. Kuching city is located in the northwest of Borneo Island and part of Sarawak river catchment. This area is a developing state in Malaysia experiencing rapid urbanization since 2000s, which has caused the insufficient data availability in topography and hydrology. To deal with these challenging issues, this study presents a flood modelling framework using the remote sensing technologies and machine learning techniques to acquire the digital elevation model (DEM) with improved accuracy for the non-surveyed areas. Intensity-duration-frequency (IDF) curves were derived from climate model for various scenario simulations. The developed flood framework will be beneficial for the planners, policymakers, stakeholders as well as researchers in the field of water resource management in the aspect of providing better ideas/tools in dealing with the flooding issues in the region.},
  language  = {en}
}
@article{WendiLiongSunetal.2016,
  author    = {Wendi, Dadiyorto and Liong, Shie-Yui and Sun, Yabin and Doan, Chi Dung},
  title     = {An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network},
  series = {Journal of advances in modeling earth systems},
  volume    = {8},
  journal   = {Journal of advances in modeling earth systems},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1942-2466},
  doi       = {10.1002/2015MS000536},
  pages     = {691 -- 702},
  year      = {2016},
  abstract  = {Although the Shuttle Radar Topography Mission [SRTM) data are a publicly accessible Digital Elevation Model [DEM) provided at no cost, its accuracy especially at forested area is known to be limited with root mean square error (RMSE) of approx. 14 m in Singapore's forested area. Such inaccuracy is attributed to the 5.6 cm wavelength used by SRTM that does not penetrate vegetation well. This paper considers forested areas of central catchment of Singapore as a proof of concept of an approach to improve the SRTM data set. The approach makes full use of (1) the introduction of multispectral imagery (Landsat 8), of 30 m resolution, into SRTM data; (2) the Artificial Neural Network (ANN) to flex its known strengths in pattern recognition and; (3) a reference DEM of high accuracy (1 m) derived through the integration of stereo imaging of worldview-1 and extensive ground survey points. The study shows a series of significant improvements of the SRTM when assessed with the reference DEM of 2 different areas, with RMSE reduction of \&\#8764;68\% (from 13.9 m to 4.4 m) and \&\#8764;52\% (from 14.2 m to 6.7 m). In addition, the assessment of the resulting DEM also includes comparisons with simple denoising methodology (Low Pass Filter) and commercially available product called NEXTMap® World 30™.},
  language  = {en}
}