SavedAdd your journal

Journals / Teknik Dergi / 2021 / Cilt: 32 - Sayı: 4

Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting

Ali DANANDEH MEHR
Mir Jafar Sadegh SAFARI
Vahid NOURANI
Pages
11029–11050
Publication Date
2021-07-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed that the WPGP is a robust model, superior to AR1 and RF, and significantly increases the predictive accuracy of the standalone GP model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents developing procedures and verification of a new hybrid model, namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast.
📊
Key Findings & Data:To characterize the stochastic component, different theoretical probability distribution functions were assessed, and the best one was selected to integrate with the GP-evolved function.
💡
Conclusion & Impact:The results showed that the WPGP is a robust model, superior to AR1 and RF, and significantly increases the predictive accuracy of the standalone GP model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Drought · SPEI · wavelet packet

Abstract

This study presents developing procedures and verification of a new hybrid model, namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast. To this end, the multi-temporal standardized precipitation evapotranspiration index (SPEI) has been used as the drought quantifying parameter at two meteorological stations at Ankara province, Turkey. The new WPGP model comprises two main steps. In the first step, the wavelet packet, which is a generalization of the well-known wavelet transform, is used to decompose the SPEI series into deterministic and stochastic sub-signals. Then, classic genetic programming (GP) is applied to formulate the deterministic sub-signal considering its effective lags. To characterize the stochastic component, different theoretical probability distribution functions were assessed, and the best one was selected to integrate with the GP-evolved function. The efficiency of the new model was cross-validated with the first order autoregressive (AR1), GP, and random forest (RF) models developed as the benchmarks in the present study. The results showed that the WPGP is a robust model, superior to AR1 and RF, and significantly increases the predictive accuracy of the standalone GP model.

Özet

This study presents developing procedures and verification of a new hybrid model, namely wavelet packet-genetic programming (WPGP) for short-term meteorological drought forecast. To this end, the multi-temporal standardized precipitation evapotranspiration index (SPEI) has been used as the drought quantifying parameter at two meteorological stations at Ankara province, Turkey. The new WPGP model comprises two main steps. In the first step, the wavelet packet, which is a generalization of the well-known wavelet transform, is used to decompose the SPEI series into deterministic and stochastic sub-signals. Then, classic genetic programming (GP) is applied to formulate the deterministic sub-signal considering its effective lags. To characterize the stochastic component, different theoretical probability distribution functions were assessed, and the best one was selected to integrate with the GP-evolved function. The efficiency of the new model was cross-validated with the first order autoregressive (AR1), GP, and random forest (RF) models developed as the benchmarks in the present study. The results showed that the WPGP is a robust model, superior to AR1 and RF, and significantly increases the predictive accuracy of the standalone GP model.

🔖

Cite This Article

MEHR, A. D., SAFARI, M. J. S., NOURANI, V. (2021). Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting. Teknik Dergi, Cilt: 32 - Sayı: 4, 11029–11050. https://ajindex.com/articles/219416

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. McKee, T.B., Doesken, N.J., and Kleist, J., (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the International 8th Conference on Applied Climatology. American Meteorological Society, Anaheim, CA, USA, 17–22 January. pp. 179–184
  2. Bhalme, H.N., and Mooley, D. A. (1980). Large-scale droughts/floods and monsoon circulation. Monthly Weather Review, 108(8), 1197-1211
  3. Palmer, W.C. (1965). Meteorological Drought, Weather Bureau Research Paper No. 45, U.S. Department of Commerce, Washington, D.C
  4. Vicente-Serrano, S.M., Beguería, S., and López-Moreno, J.I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7), 1696-1718
  5. Mishra, A. K., & Desai, V. R. (2005). Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment, 19(5), 326-339
  6. Bacanli, U. G., Firat, M., & Dikbas, F. (2009). Adaptive neuro-fuzzy inference system for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23(8), 1143-1154
  7. Keskin, M. E., Terzi, O., Taylan, E. D., & Küçükyaman, D. (2009). Meteorological drought analysis using data-driven models for the Lakes District, Turkey. Hydrological sciences journal, 54(6), 1114-1124
  8. Durdu, Ö. F. (2010). Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stochastic Environmental Research and Risk Assessment, 24(8), 1145-1162
  9. Özger, M., Mishra, A. K., & Singh, V. P. (2012). Long lead time drought forecasting using a wavelet and fuzzy logic combination model: A case study in Texas. Journal of Hydrometeorology, 13(1), 284-297
  10. Danandeh Mehr, A., Kahya, E., & Özger, M. (2014). A gene–wavelet model for long lead time drought forecasting. Journal of Hydrology, 517, 691-699
  11. Bazrafshan, O., Salajegheh, A., Bazrafshan, J., Mahdavi, M., & Fatehi Maraj, A. (2015). Hydrological drought forecasting using ARIMA models (case study: Karkheh Basin). Ecopersia, 3(3), 1099-1117
  12. Karavitis, C. A., Vasilakou, C. G., Tsesmelis, D. E., Oikonomou, P. D., Skondras, N. A., Stamatakos, D.,. & Alexandris, S. (2015). Short-term drought forecasting combining stochastic and geo-statistical approaches. European Water, 49, 43-63
  13. Deo, R. C., Tiwari, M. K., Adamowski, J. F., & Quilty, J. M. (2017). Forecasting effective drought index using a wavelet extreme learning machi