141
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

Pages 1211-1225 | Received 17 Jun 2016, Accepted 18 Aug 2016, Published online: 20 Sep 2016

References

  • Box, G. E., and G. M. Jenkins. 1976. Time Series Analysis: Forecasting and Control. revised ed. San Francisco: Holden-Day.
  • Chang, L. C., F. J. Chang, and Y. M. Chiang. 2004. “A Two-Step-Ahead Recurrent Neural Network for Stream-Flow Forecasting.” Hydrological Processes 18 (1): 81–92. doi: 10.1002/hyp.1313
  • Chang, F., L. C. Chang, and H. L. Huang. 2002. “Real-Time Recurrent Learning Neural Network for Stream-Flow Forecasting.” Hydrological Processes 16 (13): 2577–2588. doi: 10.1002/hyp.1015
  • Chen, S. H., Y. H. Lin, L. C. Chang, and F. J. Chang. 2006. “The Strategy of Building a Flood Forecast Model by Neuro-fuzzy Network.” Hydrological Processes 20 (7): 1525–1540. doi: 10.1002/hyp.5942
  • Chen, C. Y., L. Y. Lin, F. C. Yu, C. S. Lee, C. C. Tseng, A. H. Wang, and K. W. Cheung. 2007. “Improving Debris Flow Monitoring in Taiwan by using High-Resolution Rainfall Products from QPESUMS.” Natural Hazards 40 (2): 447–461. doi: 10.1007/s11069-006-9004-2
  • Dawson, C. W., and R. Wilby. 1998. “An Artificial Neural Network Approach to Rainfall-Runoff Modelling.” Hydrological Sciences Journal 43 (1): 47–66. doi: 10.1080/02626669809492102
  • Del Giudice, G., R. Gargano, G. Rasulo, and D. Siciliano. 2014a. “Preliminary Estimate of Detention Basin Efficiency at Watershed Scale.” Water Resources Management 28 (4): 897–913. doi: 10.1007/s11269-014-0518-1
  • Del Giudice, G., G. Rasulo, D. Siciliano, and R. Padulano. 2014b. “Combined Effects of Parallel and Series Detention Basins for Flood Peak Reduction.” Water Resources Management 28 (10): 3193–3205. doi: 10.1007/s11269-014-0668-1
  • Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston, MA: Addison Wesley.
  • Gourley, J. J., R. A. Maddox, K. W. Howard, and D. W. Burgess. 2002. “An Exploratory Multisensor Technique for Quantitative Estimation of Stratiform Rainfall.” Journal of Hydrometeorology 3 (2): 166–180. doi: 10.1175/1525-7541(2002)003<0166:AEMTFQ>2.0.CO;2
  • Granata, F., R. Gargano, and G. de Marinis. 2016. “Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA’s Storm Water Management Model.” Water 8 (3): 69. doi: 10.3390/w8030069
  • Grossmann, A., and J. Morlet. 1984. “Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape.” SIAM Journal on Mathematical Analysis 15 (4): 723–736. doi: 10.1137/0515056
  • Holland, J. H. 1973. “Genetic Algorithms and the Optimal Allocation of Trials.” SIAM Journal on Computing 2 (2): 88–105. doi: 10.1137/0202009
  • Jain, A., and S. Srinivasulu. 2004. “Development of Effective and Efficient Rainfall-Runoff Models using Integration of Deterministic, Real-Coded Genetic Algorithms and Artificial Neural Network Techniques.” Water Resources Research 40 (4): W04302. doi: 10.1029/2003WR002355
  • Jain, A., K. P. Sudheer, and S. Srinivasulu. 2004. “Identification of Physical Processes Inherent in Artificial Neural Network Rainfall Runoff Models.” Hydrological Processes 18 (3): 571–581. doi: 10.1002/hyp.5502
  • Kalteh, A. M. 2016. “Improving Forecasting Accuracy of Streamflow Time Series using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques.” Water Resources Management 30 (2): 747–766. doi: 10.1007/s11269-015-1188-3
  • Karlsson, M., and S. Yakowitz. 1987. “Rainfall-Runoff Forecasting Methods, Old and New.” Stochastic Hydrology and Hydraulics 1 (4): 303–318. doi: 10.1007/BF01543102
  • Kia, M. B., S. Pirasteh, B. Pradhan, A. R. Mahmud, W. N. A. Sulaiman, and A. Moradi. 2012. “An Artificial Neural Network Model for Flood Simulation using GIS: Johor River Basin, Malaysia.” Environmental Earth Sciences 67 (1): 251–264. doi: 10.1007/s12665-011-1504-z
  • Lee, C. S., L. R. Huang, H. S. Shen, and S. T. Wang. 2006. “A Climatology Model for Forecasting Typhoon Rainfall in Taiwan.” Natural Hazards 37 (1-2): 87–105. doi: 10.1007/s11069-005-4658-8
  • Najafzadeh, M. 2015a. “Neuro-fuzzy GMDH Based Particle Swarm Optimization for Prediction of Scour Depth at Downstream of Grade Control Structures.” Engineering Science and Technology, an International Journal 18 (1): 42–51. doi: 10.1016/j.jestch.2014.09.002
  • Najafzadeh, M. 2015b. “Neurofuzzy-Based GMDH-PSO to Predict Maximum Scour Depth at Equilibrium at Culvert Outlets.” Journal of Pipeline Systems Engineering and Practice 7 (1): 06015001. doi: 10.1061/(ASCE)PS.1949-1204.0000204
  • Najafzadeh, M., and H. M. Azamathulla. 2013. “Neuro-fuzzy GMDH to Predict the Scour Pile Groups due to Waves.” Journal of Computing in Civil Engineering 29 (5): 04014068. doi: 10.1061/(ASCE)CP.1943-5487.0000376
  • Najafzadeh, M., M. R. Balf, and E. Rashedi. 2016. “Prediction of Maximum Scour Depth Around Piers with Debris Accumulation using EPR, MT, and GEP Models.” Journal of Hydroinformatics. jh2016212. doi: 10.2166/hydro.2016.212
  • Najafzadeh, M., and G. A. Barani. 2011. “Comparison of Group Method of Data Handling Based Genetic Programming and Back Propagation Systems to Predict Scour Depth Around Bridge Piers.” Scientia Iranica 18 (6): 1207–1213. doi: 10.1016/j.scient.2011.11.017
  • Najafzadeh, M., and G. A. Barani. 2013. “Discussion of ‘Genetic Programming to Predict River Pipeline Scour’ by H. Md. Azamathulla and Aminuddin Ab Ghani.” Journal of Pipeline Systems Engineering and Practice 4 (4): 07013001. doi: 10.1061/(ASCE)PS.1949-1204.0000146
  • Najafzadeh, M., G. A. Barani, and M. R. Hessami-Kermani. 2015. “Evaluation of GMDH Networks for Prediction of Local Scour Depth at Bridge Abutments in Coarse Sediments with Thinly Armored Beds.” Ocean Engineering 104: 387–396. doi: 10.1016/j.oceaneng.2015.05.016
  • Najafzadeh, M., and H. Bonakdari. 2016. “Application of a Neuro-fuzzy GMDH Model for Predicting the Velocity at Limit of Deposition in Storm Sewers.” Journal of Pipeline Systems Engineering and Practice. doi: 10.1061/(ASCE)PS.1949-1204.0000249
  • Najafzadeh, M., A. Etemad-Shahidi, and S. Y. Lim. 2016. “Scour Prediction in Long Contractions using ANFIS and SVM.” Ocean Engineering 111: 128–135. doi: 10.1016/j.oceaneng.2015.10.053
  • Najafzadeh, M., and S. Y. Lim. 2015. “Application of Improved Neuro-fuzzy GMDH to Predict Scour Depth at Sluice Gates.” Earth Science Informatics 8 (1): 187–196. doi: 10.1007/s12145-014-0144-8
  • Najafzadeh, M., and A. M. Sattar. 2015. “Neuro-fuzzy GMDH Approach to Predict Longitudinal Dispersion in Water Networks.” Water Resources Management 29 (7): 2205–2219. doi: 10.1007/s11269-015-0936-8
  • Najafzadeh, M., and A. Zahiri. 2015. “Neuro-fuzzy GMDH-Based Evolutionary Algorithms to Predict Flow Discharge in Straight Compound Channels.” Journal of Hydrologic Engineering 20 (12): 04015035. doi: 10.1061/(ASCE)HE.1943-5584.0001185
  • Nash, J., and J. V. Sutcliffe. 1970. “River Flow Forecasting through Conceptual Models Part I—A Discussion of Principles.” Journal of Hydrology 10 (3): 282–290. doi: 10.1016/0022-1694(70)90255-6
  • Nayak, P. C., K. P. Sudheer, and K. S. Ramasastri. 2005. “Fuzzy Computing Based Rainfall-Runoff Model for Real Time Flood Forecasting.” Hydrological Processes 19 (4): 955–968. doi: 10.1002/hyp.5553
  • Pan, T. Y., L. Y. Chang, J. S. Lai, H. K. Chang, C. S. Lee, and Y. C. Tan. 2014. “Coupling Typhoon Rainfall Forecasting with Overland-flow Modeling for Early Warning of Inundation.” Natural Hazards 70 (3): 1763–1793. doi: 10.1007/s11069-011-0061-9
  • Rajurkar, M. P., U. C. Kothyari, and U. C. Chaube. 2004. “Modeling of the Daily Rainfall-Runoff Relationship with Artificial Neural Network.” Journal of Hydrology 285 (1): 96–113. doi: 10.1016/j.jhydrol.2003.08.011
  • Sajikumar, N., and B. S. Thandaveswara. 1999. “A Non-linear Rainfall–Runoff Model using an Artificial Neural Network.” Journal of Hydrology 216 (1): 32–55. doi: 10.1016/S0022-1694(98)00273-X
  • Talei, A., L. H. C. Chua, and T. S. Wong. 2010. “Evaluation of Rainfall and Discharge Inputs used by Adaptive Network-Based Fuzzy Inference Systems (ANFIS) in Rainfall–Runoff Modeling.” Journal of Hydrology 391 (3): 248–262. doi: 10.1016/j.jhydrol.2010.07.023
  • Toth, E., A. Brath, and A. Montanari. 2000. “Comparison of Short-Term Rainfall Prediction Models for Real-Time Flood Forecasting.” Journal of Hydrology 239 (1): 132–147. doi: 10.1016/S0022-1694(00)00344-9
  • Vieux, B. E., J. E. Vieux, C. Chiarong, and K. W. Howard. 2003. “Operational Deployment of a Physics-Based Distributed Rainfall-Runoff Model for Flood Forecasting in Taiwan.” In International Symposium on Weather Radar Information and Distributed Hydrological Modeling, edited by Y. Tachikawa, B. E. Vieux, K. P. Georgakakos, and E. Nakakita, 251–257. Wallingford: IAHS-AISH Publication.
  • de Vos, N. J., and T. H. M. Rientjes. 2005. “Constraints of Artificial Neural Networks for Rainfall-Runoff Modelling: Trade-offs in Hydrological State Representation and Model Evaluation.” Hydrology and Earth System Sciences Discussions 2 (1): 365–415. doi: 10.5194/hessd-2-365-2005
  • Yule, G. U. 1927. “On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer’s Sunspot Numbers.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 226: 267–298. doi: 10.1098/rsta.1927.0007

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.