Machine learning in real-time control of water systems

Abebe, 2000, Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events, Hydrological Sciences Journal, 45, 425, 10.1080/02626660009492339 Bardossy, 1995 Bazartseren, B. (1999). Use of neural networks and fuzzy adaptive systems for controlling polder water level in the Netherlands. MSc thesis, IHE-Delft Bhattacharya, B. (2000). Machine Learning in Real Time Control of Regional Water Systems. MSc thesis, IHE-Delft Harris, 1994 Lobbrecht, 1997 Lobbrecht, 1999, Dynamic control of the delfland polders and storage basin, Netherlands, Water Science and Technology, 39, 269, 10.1016/S0273-1223(99)00079-7 Lobbrecht, A. H., & Solomatine, D. P. (1999). Control of water levels in polder areas using neural networks and fuzzy adaptive systems. Water industry systems (pp. 509–518). Research Study Press Ltd Lobbrecht, A. H., Maskey, S., & Samedov, J. N. (2000). aquarius-based decision support systems using distributed computing. Fourth International Conference on Hydroinformatics, Iowa, USA Omidvar, 1997 Panigrahi, D. P., & Mujumdar, P. P. (1997). Application of Fuzzy Logic to Reservoir Operation Modelling. National Conference on Fuzzy Sets and Their Applications. Madras Russel, S. O., & Campbell, P. F. (1996). Reservoir operation rules with fuzzy programming. ASCE Journal of Water Resources Planning and Management, 122, 165–170 Solomatine, D. P., & Torres, L. A. (1996). Neural network approximation of a hydrodynamic model in optimizing reservoir operation. Second International Conference on Hydroinformatics (pp. 201–206), Zurich. Rotterdam: Balkema