Full Name
Vladan Babovic
Variants
Babovic, V.
Babovic Vladan
 
 
 
Email
ceebv@nus.edu.sg
 

Publications

Results 1-20 of 24 (Search time: 0.017 seconds).

Issue DateTitleAuthor(s)
12012A real options approach to the design and architecture of water supply systems using innovative water technologies under uncertaintyZhang, S.X.; Babovic, V. 
21-Jan-2016Adaptation Pathways and Real Options Analysis: An approach to deep uncertainty in climate change adaptation policiesJoost Buurman ; Vladan Babovic 
321-Sep-2017Antifragility and the Development of Urban Water InfrastructureFilip Babovic; Vladan Babovic ; Ana Mijic
4Jan-2013Application of data assimilation for improving forecast of water levels and residual currents in Singapore regional watersKarri, R.R.; Badwe, A. ; Wang, X.; El Serafy, G.; Sumihar, J.; Babovic, V. ; Gerritsen, H.
530-Jun-2009Applying local model approach for tidal prediction in a deterministic modelSun, Y. ; Sisomphon, P. ; Babovic, V. ; Chan, E.S. 
6May-2012Artificial neural networks as routine for error correction with an application in Singapore regional modelSun, Y. ; Babovic, V. ; Chan, E.S. 
728-Feb-2008Data assimilation of forecasted errors in hydrodynamic models using inter-model correlationsMancarella, D.; Babovic, V. ; Keijzer, M.; Simeone, V.
82015Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic ProgrammingMeshgi A.; Schmitter Petra ; Chui Ting Fong May; Babovic Vladan 
9Oct-2009Efficient data assimilation method based on chaos theory and Kalman filter with an application in Singapore Regional ModelSun, Y. ; Sisomphon, P.; Babovic, V. ; Chan, E.S. 
1010-May-2014Enhancing water level prediction through model residual correction based on Chaos theory and KrigingWang, X.; Babovic, V. 
1112-Apr-2021Genetic programming for hydrological applications: To model or to forecast that is the questionHerath, Herath Mudiyanselage Viraj Vidura; Chadalawada, Jayashree ; Babovic, Vladan 
122002Genetic Programming: A new paradigm in rainfall runoff modelingLiong, S.-Y. ; Gautam, T.R. ; Soon, T.K. ; Babovic, V. ; Keijzer, M.; Muttil, N. 
1311-Aug-2021Hydrologically informed machine learning for rainfall-runoff modelling: Towards distributed modellingHerath, Herath Mudiyanselage Viraj Vidura; Chadalawada, Jayashree ; Babovic, Vladan 
14Apr-2020Hydrologically Informed Machine Learning for Rainfall‐Runoff Modeling: A Genetic Programming‐Based Toolkit for Automatic Model InductionChadalawada, Jayashree ; Herath, HMVV; Babovic, Vladan 
151-Oct-2021Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United StatesCai, Hejiang; Shi, Haiyun; Liu, Suning; Babovic, Vladan 
16Jul-2009Introducing knowledge into learning based on genetic programmingBabovic, V. 
176-Dec-2010Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theorySun, Y. ; Babovic, V. ; Chan, E.S. 
182007On inducing equations for vegetation resistanceBaptist, M.J.; Babovic, V. ; Uthurburu, J.R.; Keijzer, M.; Uittenbogaard, R.E.; Mynett, A.; Verwey, A.
192019Projections of future climate change in Singapore based on a multi-site multivariate downscaling approachLi, X.; Zhang, K.; Babovic, V. 
202013Real options and CO2 reduction policies for climate changeChan, L.G.; Babovic, V.