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This paper presents 2 past and 1 on-going works on the applications of Artificial Neural Networks (ANN). The 2 past works are river flow forecasting in Bangladesh (a country with vast delta and severe floods perennially) and forecasting of maximum wave height and its time of arrival of the devastating Aceh tsunami in 2004. The on-going work is on deriving a cost effective and high accuracy Digital Elevation Model (DEM) from publicly accessible satellite data. The paper first demonstrates the ANN application on the river stage forecasting at Dhaka, Bangladesh, from 1 to 7 lead day forecast horizons. Although the input nodes used only the water levels at most upstream reaches of 3 main transboundary rivers (Ganges, Brahmaputra, Meghna), the goodness-of-fit R2 values are very high ranging from 0.99 (for 1 lead day) to 0.91 (for 7 lead days). The reason, even without rainfall data in the input nodes, is that 80–95% of the catchments of these 3 large rivers lie in India; thus, the main flow contributions come from India. The high degree of accuracy, accompanied with very short computational time (less than 1 min), makes ANN a desirable advanced warning flow forecasting tool. The paper continues with a second ANN application demonstrating its effectiveness and efficiency as a forecasting tool for devastating Indian Ocean/Aceh tsunami in 2004. The ANN was trained with simulation output data of a widely used process-based tsunami propagation model, TUNAMI-N2. The input nodes comprised, among others, the earthquake magnitude and epicenter with spatial values of maximum tsunami heights and tsunami arrival times (snapshots) for the most probable ocean floor rupture scenarios as its target. Validation tests demonstrated that with a given earthquake magnitude and location, the ANN method provides accurate and near instantaneous forecasting of the maximum tsunami heights and arrival times for the entire computational domain covering South China sea (the Philippines inclusive) and the Indian Ocean (India inclusive). The 3rd ANN application shows the on-going DEM improvement scheme, which significantly improves DEM originating from a publicly accessible satellite SRTM (Shuttle Radar Topography Mission). The scheme uses the DEM data of SRTM and the multispectral data of another publicly accessible satellite Sentinel-2 as the input to the ANN while the target is the high spatially resolution and high accuracy DEM from German Aerospace Center (DLR). Thus far the present improvement scheme manages to reduce the Root Mean Square Error up to 42.3%. Equally interesting is that the trained ANN can also be used to provide DEM in another part of the world with accuracy much higher than the raw DEM from SRTM.
Shie-Yui Liong; DongEon Kim; Jiandong Liu; Philippe Gourbesville; Ludovic Andres. Possible Roles of Artificial Neural Networks in Hydraulic and Hydrological Models. Springer Water 2020, 529 -543.
AMA StyleShie-Yui Liong, DongEon Kim, Jiandong Liu, Philippe Gourbesville, Ludovic Andres. Possible Roles of Artificial Neural Networks in Hydraulic and Hydrological Models. Springer Water. 2020; ():529-543.
Chicago/Turabian StyleShie-Yui Liong; DongEon Kim; Jiandong Liu; Philippe Gourbesville; Ludovic Andres. 2020. "Possible Roles of Artificial Neural Networks in Hydraulic and Hydrological Models." Springer Water , no. : 529-543.
Digital elevation models (DEMs) are crucial in flood modeling as DEM data reflects the actual topographic characteristics where water can flow in the model. However, a high-quality DEM is very difficult to acquire as it is very time consuming, costly, and, often restricted. 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 SRTM’s DEM. High-quality DEM is used as target data in the training of ANN. The trained ANN will then be ready to efficiently and effectively generate a high-quality DEM, at low cost, for places where ground truth DEM data is not available. In this paper, the performance of the DEM improvement scheme is evaluated over two dense urban cities, Nice (France) and Singapore; with the performance criteria using various matrices, e.g., visual clarity, scatter plots, root mean square error (RMSE) and flood maps. The DEM resulting from the improved SRTM (iSRTM) showed significantly better results than the original SRTM DEM, with about 38% RMSE reduction. Flood maps from iSRTM DEM show much more reasonable flood patterns than SRTM DEM’s flood map.
Dong Eon Kim; Shie-Yui Liong; Philippe Gourbesville; Ludovic Andres; Jiandong Liu. Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling. Water 2020, 12, 816 .
AMA StyleDong Eon Kim, Shie-Yui Liong, Philippe Gourbesville, Ludovic Andres, Jiandong Liu. Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling. Water. 2020; 12 (3):816.
Chicago/Turabian StyleDong Eon Kim; Shie-Yui Liong; Philippe Gourbesville; Ludovic Andres; Jiandong Liu. 2020. "Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling." Water 12, no. 3: 816.
Active, Beautiful, Clean Waters (ABC Waters) design features—natural systems consisting of plants and soil that detain and treat rainwater runoff—comprise a major part of Sustainable urban Drainage Systems (SuDS) in Singapore. Although it is generally accepted that ABC Waters design features are able to detain runoff and reduce peak flow, their effectiveness in doing so has not been studied or documented locally. This research aims to determine their effectiveness in reducing peak flow based on a newly constructed pilot precinct named Waterway Ridges. Four types of ABC Waters features have been integrated holistically within the development, and designed innovatively to allow the precinct to achieve an effective C-value of 0.55 for the 10-year design storm; the precinct-wide integration and implemented design with the aim of substantially reducing peak flow are firsts in Singapore. The study is based on results from an uncalibrated 1D hydraulic model developed using the Storm Water Management Model (SWMM). Identification of key design elements and performance enhancement of the features via optimisation were also studied. Results show that the features are effective in reducing peak flow for the 10-year design storm, by 33%, and allowed the precinct to achieve an effective C-value of 0.60.
Wing Ken Yau; Mohanasundar Radhakrishnan; Shie-Yui Liong; Chris Zevenbergen; Assela Pathirana. Effectiveness of ABC Waters Design Features for Runoff Quantity Control in Urban Singapore. Water 2017, 9, 577 .
AMA StyleWing Ken Yau, Mohanasundar Radhakrishnan, Shie-Yui Liong, Chris Zevenbergen, Assela Pathirana. Effectiveness of ABC Waters Design Features for Runoff Quantity Control in Urban Singapore. Water. 2017; 9 (8):577.
Chicago/Turabian StyleWing Ken Yau; Mohanasundar Radhakrishnan; Shie-Yui Liong; Chris Zevenbergen; Assela Pathirana. 2017. "Effectiveness of ABC Waters Design Features for Runoff Quantity Control in Urban Singapore." Water 9, no. 8: 577.
An investigation on the performance of artificial neural network (ANN) as a global model over the widely used local models (local averaging technique and local polynomials technique) in chaotic time series prediction is conducted. A theoretical noise-free chaotic time series, a noise added theoretical chaotic time series and two chaotic river flow time series are analyzed in this study. Three prediction horizons (1, 3 and 5 lead times) are considered. A limited number of parameter combinations were considered to select the best ANN models (MLPs) for prediction. This procedure was shown to be effective at least for the time series considered in this study. A remarkable prediction performance was gained with Global ANN models on noise-free chaotic Lorenz series. The overall results showed the superiority of global ANN models over the widely used local prediction models.
Dulakshi S.K. Karunasinghe; Shie-Yui Liong. Chaotic time series prediction with a global model: Artificial neural network. Journal of Hydrology 2006, 323, 92 -105.
AMA StyleDulakshi S.K. Karunasinghe, Shie-Yui Liong. Chaotic time series prediction with a global model: Artificial neural network. Journal of Hydrology. 2006; 323 (1):92-105.
Chicago/Turabian StyleDulakshi S.K. Karunasinghe; Shie-Yui Liong. 2006. "Chaotic time series prediction with a global model: Artificial neural network." Journal of Hydrology 323, no. 1: 92-105.