Skip to main content
Erschienen in: Water Resources Management 12/2012

01.09.2012

Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152]

verfasst von: Darren J. Beriro, Robert J. Abrahart, Nick J. Mount, C. Paul Nathanail

Erschienen in: Water Resources Management | Ausgabe 12/2012

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Excerpt

We write in response to “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” (Kisi and Shiri 2011). Gene Expression Programming (GEP: Ferreira 2001) and Adaptive Neuro-Fuzzy Inference System (ANFIS: Jang 1993; Jang and Sun 1995) solutions were compared and contrasted using a common methodology: an extended version of that employed by Partal and Kisi (2007). Kisi and Shiri (2011) combined precipitation records and an integrated wavelet-based series according to lag. We comment below on issues regarding their GEP precipitation forecasting solution for the rain gauge station at Izmir:
$$ Pt = 2.15\sqrt {{DW{{H}_{{t - 2}}}}} + 2{{P}_{{t - 3}}} - DW{{H}_{{t - 2}}} + 6.96 \sqrt {[3] {{{{P}_{{t - 2}}}}}} + \frac{{DW{{H}_{{t - 1}}}}}{{arctg\left[ {{{P}_{{t - 3}}} - {{P}_{{t - 2}}} - {{P}_{{t - 4}}} + DW{{H}_{{t - 1}}} - {{P}_{{t - 1}}}{{P}_{{t - 3}}}} \right]}} $$
(1)
where 2.15 and 6.96 are random constants derived by GEP, DWHt-i (Discrete Wavelet Hybrid) is a combined series in which selected transformations derived from discrete wavelet decomposition (Goswami and Chan 2011) of the original series are summed, and Pt-i comprises raw lagged precipitation records. Their notation in the original paper was D for Discrete Wavelet Transformation (DWT) and DW for DWH. …

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Abrahart RJ, See LM (2007) Neural network modelling of non-linear hydrological relationships. Hydrol Earth Syst Sc 11:1563–1579 Abrahart RJ, See LM (2007) Neural network modelling of non-linear hydrological relationships. Hydrol Earth Syst Sc 11:1563–1579
Zurück zum Zitat Alexandrov GA, Ames D, Bellocchi G, Bruen M, Crout N, Erechtchoukova M, Hildebrandt A, Hoffman F, Jackisch C, Khaiter P, Mannina G, Matsunaga T, Purucker ST, Rivington M, Samaniego L (2011) Technical assessment and evaluation of environmental models and software: Letter to the Editor. Environ Model Softw 26:328–336CrossRef Alexandrov GA, Ames D, Bellocchi G, Bruen M, Crout N, Erechtchoukova M, Hildebrandt A, Hoffman F, Jackisch C, Khaiter P, Mannina G, Matsunaga T, Purucker ST, Rivington M, Samaniego L (2011) Technical assessment and evaluation of environmental models and software: Letter to the Editor. Environ Model Softw 26:328–336CrossRef
Zurück zum Zitat Alkroosh I, Nikraz H (2011) Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming. Geotech Geol Eng 29:725–748CrossRef Alkroosh I, Nikraz H (2011) Correlation of Pile Axial Capacity and CPT Data Using Gene Expression Programming. Geotech Geol Eng 29:725–748CrossRef
Zurück zum Zitat Bellocchi G, Rivington M, Donatelli M, Matthews K (2010) Validation of biophysical models: Issues and methodologies. A review. Agron Sustain Dev 30:109–130CrossRef Bellocchi G, Rivington M, Donatelli M, Matthews K (2010) Validation of biophysical models: Issues and methodologies. A review. Agron Sustain Dev 30:109–130CrossRef
Zurück zum Zitat Beriro DJ, Abrahart RJ, Nathanail CP (2012) Comments on “Empirical modelling of plate load test moduli of soil via gene expression programming” by Ali Mollahasani, Amir Hossein Alavi and Amir Hossein Gandomi [Computers and Geotechnics 38 (2011) 281–286]. Comput Geotech 39:75–78 Beriro DJ, Abrahart RJ, Nathanail CP (2012) Comments on “Empirical modelling of plate load test moduli of soil via gene expression programming” by Ali Mollahasani, Amir Hossein Alavi and Amir Hossein Gandomi [Computers and Geotechnics 38 (2011) 281–286]. Comput Geotech 39:75–78
Zurück zum Zitat Colbourn EA, Roskilly SJ, Rowe RC, York P (2011) Modelling formulations using gene expression programming—A comparative analysis with artificial neural networks. Eur J Pharm Sci 44:366–374CrossRef Colbourn EA, Roskilly SJ, Rowe RC, York P (2011) Modelling formulations using gene expression programming—A comparative analysis with artificial neural networks. Eur J Pharm Sci 44:366–374CrossRef
Zurück zum Zitat Curry B, Morgan PH (2003) Neural networks, linear functions and neglected non-linearity. Comp Man Sci 1:15–29CrossRef Curry B, Morgan PH (2003) Neural networks, linear functions and neglected non-linearity. Comp Man Sci 1:15–29CrossRef
Zurück zum Zitat Eldrandaly KA (2009) Integrating gene expression programming and geographic information systems for solving a multi site land use allocation problem. Am J Appl Sci 6:1021–1027CrossRef Eldrandaly KA (2009) Integrating gene expression programming and geographic information systems for solving a multi site land use allocation problem. Am J Appl Sci 6:1021–1027CrossRef
Zurück zum Zitat Ferreira C (2001) Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems 13:87–129 Ferreira C (2001) Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems 13:87–129
Zurück zum Zitat Gaume E, Gosset R (2003) Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology? Hydrol Earth Syst Sc 7:693–706 Gaume E, Gosset R (2003) Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology? Hydrol Earth Syst Sc 7:693–706
Zurück zum Zitat Goswami JC, Chan AK (2011) Fundamentals of Wavelets: Theory, Algorithms, and Applications. John Wiley & Sons Inc, New JerseyCrossRef Goswami JC, Chan AK (2011) Fundamentals of Wavelets: Theory, Algorithms, and Applications. John Wiley & Sons Inc, New JerseyCrossRef
Zurück zum Zitat Haar A (1910) Zur Theorie der orthogonalen Funktionensysteme. Math Ann 69:331–371CrossRef Haar A (1910) Zur Theorie der orthogonalen Funktionensysteme. Math Ann 69:331–371CrossRef
Zurück zum Zitat Han D, Moghaddamnia A (2009) Reply to comments on “Evaporation estimation using artificial neural networks and adaptive neurofuzzy inference system techniques” by A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin and D. Han [Adv. Water Resour. 32 (2009) 88–97]. Adv Water Resour 32:967–968 Han D, Moghaddamnia A (2009) Reply to comments on “Evaporation estimation using artificial neural networks and adaptive neurofuzzy inference system techniques” by A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin and D. Han [Adv. Water Resour. 32 (2009) 88–97]. Adv Water Resour 32:967–968
Zurück zum Zitat Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23:665–685CrossRef Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23:665–685CrossRef
Zurück zum Zitat Jang J-SR, Sun C-T (1995) Neuro-fuzzy modeling and control. P IEEE 83:378–406CrossRef Jang J-SR, Sun C-T (1995) Neuro-fuzzy modeling and control. P IEEE 83:378–406CrossRef
Zurück zum Zitat Jerri AJ (2011) Introduction to Wavelets. Sampling Publishing, New York Jerri AJ (2011) Introduction to Wavelets. Sampling Publishing, New York
Zurück zum Zitat Kisi O (2009) Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol Process 23:3583–3597CrossRef Kisi O (2009) Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting. Hydrol Process 23:3583–3597CrossRef
Zurück zum Zitat Kisi O, Shiri J (2011) Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models. Water Resour Manag 25:3135–3152CrossRef Kisi O, Shiri J (2011) Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models. Water Resour Manag 25:3135–3152CrossRef
Zurück zum Zitat Mount NJ, Abrahart RJ, Dawson CW, Ghani ABN (2012) The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hydrol Process Mount NJ, Abrahart RJ, Dawson CW, Ghani ABN (2012) The need for operational reasoning in data-driven rating curve prediction of suspended sediment. Hydrol Process
Zurück zum Zitat Mount NJ, Abrahart RJ (2011) Discussion of “River flow estimation from upstream flow records by artificial intelligence methods” by M.E. Turan, M.A. Yurdusev [J. Hydrol. 369 (2009) 71–77]. J Hydrol 396:193–196 Mount NJ, Abrahart RJ (2011) Discussion of “River flow estimation from upstream flow records by artificial intelligence methods” by M.E. Turan, M.A. Yurdusev [J. Hydrol. 369 (2009) 71–77]. J Hydrol 396:193–196
Zurück zum Zitat Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intel 22:466–472CrossRef Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intel 22:466–472CrossRef
Zurück zum Zitat Partal T, Cigizoglu HK (2009) Prediction of daily precipitation using wavelet-neural networks. Hydrol Sci J 54:234–246CrossRef Partal T, Cigizoglu HK (2009) Prediction of daily precipitation using wavelet-neural networks. Hydrol Sci J 54:234–246CrossRef
Zurück zum Zitat Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212CrossRef Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212CrossRef
Zurück zum Zitat Rivera D, Lillo M, Uvo CB, Billib M, Arumí JL (2012) Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature. Theor and Applied Climatology. doi:10.1007/s00704-011-0453-5 Rivera D, Lillo M, Uvo CB, Billib M, Arumí JL (2012) Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature. Theor and Applied Climatology. doi:10.​1007/​s00704-011-0453-5
Zurück zum Zitat Shiri J, Kisi O (2011) Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput Geosci 37:1692–1701CrossRef Shiri J, Kisi O (2011) Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput Geosci 37:1692–1701CrossRef
Zurück zum Zitat Wang W, Ding J (2003) Wavelet Network Model and Its Application to the Prediction of Hydrology. Nature and Science 1:67–71 Wang W, Ding J (2003) Wavelet Network Model and Its Application to the Prediction of Hydrology. Nature and Science 1:67–71
Metadaten
Titel
Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152]
verfasst von
Darren J. Beriro
Robert J. Abrahart
Nick J. Mount
C. Paul Nathanail
Publikationsdatum
01.09.2012
Verlag
Springer Netherlands
Erschienen in
Water Resources Management / Ausgabe 12/2012
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-012-0049-6

Weitere Artikel der Ausgabe 12/2012

Water Resources Management 12/2012 Zur Ausgabe