Center for Environmental Modeling and Sensing
Center for Environmental Modeling and Sensing
Vu M.T.,National University of Singapore |
Vu M.T.,Center for Environmental Modeling and Sensing |
Raghavan S.V.,National University of Singapore |
Raghavan S.V.,Center for Environmental Modeling and Sensing |
And 4 more authors.
Journal of Hydrology | Year: 2015
The Standardized Precipitation Index (SPI) has been computed based on the monthly precipitation for different observed and modelled datasets over the Central Highland, Vietnam during the period 1990-2005. Station data from a total of 13 stations were collected from the study region and used for benchmarking to compare gridded observation data and two regional climate models (RCMs). Various characteristics of drought across the study region were analyzed using spatial and temporal distributions, number of drought events, their frequency and their deficit. The RCMs were able to capture the SPI temporal distributions of station data fairly well. The analysis from RCMs showed close estimation for the number of drought events to station data and gridded observations. In terms of Drought Deficit and frequency, the RCMs matched the station data better than gridded observations. The drought trend was carried out using a Modified Mann-Kendall trend test which yielded no clear trends that suggested the need for longer records of data. The results also highlight uncertainties in gridded data and the need for robust station data quality and record lengths. The regional climate models proved to be appropriate tools in assessing drought over the study area as they can serve as good proxies over data sparse regions, especially in developing countries, for studying detailed climate features at sub regional and local scales. © 2014 Elsevier B.V.
Sun Y.,National University of Singapore |
Wendi D.,National University of Singapore |
Kim D.E.,National University of Singapore |
Liong S.-Y.,National University of Singapore |
And 2 more authors.
Hydrology and Earth System Sciences | Year: 2016
Accurate prediction of groundwater table is important for the efficient management of groundwater resources. Despite being the most widely used tools for depicting the hydrological regime, numerical models suffer from formidable constraints, such as extensive data demanding, high computational cost, and inevitable parameter uncertainty. Artificial neural networks (ANNs), in contrast, can make predictions on the basis of more easily accessible variables, rather than requiring explicit characterization of the physical systems and prior knowledge of the physical parameters. This study applies ANN to predict the groundwater table in a freshwater swamp forest of Singapore. The inputs to the network are solely the surrounding reservoir levels and rainfall. The results reveal that ANN is able to produce an accurate forecast with a leading time of 1 day, whereas the performance decreases when leading time increases to 3 and 7 days. © 2016 Author(s).
Allen M.,Center for Environmental Modeling and Sensing |
Preis A.,SMART |
Iqbal M.,Nanyang Technological University |
Srirangarajan S.,Nanyang Technological University |
And 3 more authors.
Journal - American Water Works Association | Year: 2011
The Wireless Water Sentinel project in Singapore (WaterWiSe@SG) has demonstrated great potential to improve the operational efficiency of the water supply system. The project has provided a unique opportunity to develop an integrated decision-support system in collaboration with a water utility. The WaterWiSe@SG software infrastructure enables integrated and real-time sensing, anomaly detection, and hydraulic modeling. WaterWiSe@SG currently monitors a 60-km2 area of downtown Singapore that is supplied by a gravity-fed water distribution system consisting of two service reservoirs, more than 19,000 junctions, and more than 20,000 pipes. A vital component of the system is interactive data display and retrieval, which is provided through a web-based control panel. Data displayed on the WaterWiSe@SG portal are primarily drawn from the summary statistics stored in the database, which facilitates observation of trends on the order of minutes to months.
Cloitre A.,Massachusetts Institute of Technology |
Subramaniam V.,Center for Environmental Modeling and Sensing |
Patrikalakis N.,Massachusetts Institute of Technology |
Y Alvarado P.V.,Center for Environmental Modeling and Sensing
Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics | Year: 2012
This paper presents our latest results in the development of biomimetic batoid robots. Our goal is to utilize these robots for autonomous environmental exploration and monitoring missions in coastal environments. These new robots will be part of a larger heterogeneous robotic network already being developed by our group which combines traditional robotic vehicles with biomimetic ones to leverage advantages of both approaches. The robot described in this paper is designed to be fully field deployable and applies important lessons learned during the development of previous flexible underactuated batoid robots. The robot design including its flexible underactuated continuous body, communications, and control hardware and software approaches are described. Preliminary trajectory control results are also detailed. © 2012 IEEE.