Research Article
BibTex RIS Cite

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

Year 2021, Volume: 32 Issue: 4, 11029 - 11050, 01.07.2021
https://doi.org/10.18400/tekderg.605453

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.

References

  • [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 machine (W-ELM) model. Stochastic environmental research and risk assessment, 31(5), 1211-1240.
  • [14] Katip, A. (2018). Meteorological Drought Analysis Using Artificial Neural Networks for Bursa City, Turkey. Applied Ecology and Environmental Research, 16(3), 3315-3332.
  • [15] Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2103-2111.
  • [16] Barua, S., Ng, A. W. M., & Perera, B. J. C. (2012). Artificial neural network–based drought forecasting using a nonlinear aggregated drought index. Journal of Hydrologic Engineering, 17(12), 1408-1413.
  • [17] Mokhtarzad, M., Eskandari, F., Vanjani, N. J., & Arabasadi, A. (2017). Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental earth sciences, 76(21), 729.
  • [18] Labat, D. (2005). Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology, 314(1-4), 275-288.
  • [19] Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358-377.
  • [20] Kim, T. W., & Valdés, J. B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319-328.
  • [21] Belayneh, A., Adamowski, J., Khalil, B., & Ozga-Zielinski, B. (2014). Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508, 418-429.
  • [22] Maity, R., & Suman, M. (2019). Predictability of Hydrological Systems Using the Wavelet Transformation: Application to Drought Prediction. In Hydrology in a Changing World (pp. 109-137). Springer, Cham.
  • [23] Gyamfi, C., Amaning-Adjei, K., Anornu, G. K., Ndambuki, J. M., & Odai, S. N. (2019). Evolutional characteristics of hydro-meteorological drought studied using standardized indices and wavelet analysis. Modeling Earth Systems and Environment, 5(2), 455-469.
  • [24] Soh, Y. W., Koo, C. H., Huang, Y. F., & Fung, K. F. (2018). Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia. Computers and electronics in agriculture, 144, 164-173.
  • [25] Ahmadalipour, A., Moradkhani, H., and Demirel, M.C. (2017). A comparative assessment of projected meteorological and hydrological droughts: Elucidating the role of temperature. Journal of Hydrology, 553, 785-797.
  • [26] Danandeh Mehr, A., Sorman, A. U., Kahya, E., & Hesami Afshar, M. (2019). Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, DOI: 10.1080/02626667.2019.1691218
  • [27] Meresa, H. K., Osuch, M., and Romanowicz, R. (2016). Hydro-meteorological drought projections into the 21-st century for selected Polish catchments. Water, 8(5), 206.
  • [28] Khan, M., Muhammad, N., & El-Shafie, A. (2018). Wavelet-ANN versus ANN-based model for hydrometeorological drought forecasting. Water, 10(8), 998.
  • [29] Breiman, L., 2001. Random forests. Mach. Learn. 45 (1), 5–32.
  • [30] Chen, J., Li, M., & Wang, W. (2012). Statistical uncertainty estimation using random forests and its application to drought forecast. Mathematical Problems in Engineering, 2012.
  • [31] Yu, P.S., Yang, T.C., Chen, S.Y., Kuo, C.M., Tseng, H.W., 2017. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. Journal of hydrology 552, 92-104.
  • [32] Zhao, W., Sánchez, N., Lu, H., Li, A., (2018). A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of hydrology 563, 1009-1024.
  • [33] Sadler, J.M., Goodall, J.L., Morsy, M.M., Spencer, K., (2018). Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest. Journal of hydrology 559, 43-55.
  • [34] Şarlak, N., & Güven, A. (2016). Global güneş radyasyon tahmini: Gaziantep uygulaması. Teknik Dergi, 27(3), 7561-7568.
  • [35] Danandeh Mehr, A., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M., & Yaseen, Z. M. (2018). Genetic programming in water resources engineering: A state-of-the-art review. Journal of hydrology 566, 643-667.
  • [36] Hu, J., Liu, B., & Peng, S. (2019). Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques. Stochastic Environmental Research and Risk Assessment, 1-19.
  • [37] Coifman, R.R.; M.V. Wickerhauser, (1992), "Entropy-based algorithms for best basis selection," IEEE Trans. on Inf. Theory, vol. 38, 2, pp. 713–718
  • [38] Hrnjica, B., & Danandeh Mehr, A. (2018). Optimized Genetic Programming Applications: Emerging Research and Opportunities: Emerging Research and Opportunities. IGI Global.
  • [39] Rahmani-Rezaeieh, A., Mohammadi, M., & Danandeh Mehr, A. (2019). Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model. Theoretical and Applied Climatology, 1-16.
  • [40] Danandeh Mehr, A., & Safari, M. J. S. (2020). Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts. Environmental Monitoring and Assessment, 192(1), 25.
  • [41] Danandeh Mehr, A., Kahya, E., & Olyaie, E. (2013). Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. Journal of Hydrology, 505, 240-249.
  • [42] Dikbaş, F. (2016). Büyük Menderes Akımlarının Frekans Tabanlı Tahmini. Teknik Dergi, 27(1), 7325-7343.

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

Year 2021, Volume: 32 Issue: 4, 11029 - 11050, 01.07.2021
https://doi.org/10.18400/tekderg.605453

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.

References

  • [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 machine (W-ELM) model. Stochastic environmental research and risk assessment, 31(5), 1211-1240.
  • [14] Katip, A. (2018). Meteorological Drought Analysis Using Artificial Neural Networks for Bursa City, Turkey. Applied Ecology and Environmental Research, 16(3), 3315-3332.
  • [15] Morid, S., Smakhtin, V., & Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(15), 2103-2111.
  • [16] Barua, S., Ng, A. W. M., & Perera, B. J. C. (2012). Artificial neural network–based drought forecasting using a nonlinear aggregated drought index. Journal of Hydrologic Engineering, 17(12), 1408-1413.
  • [17] Mokhtarzad, M., Eskandari, F., Vanjani, N. J., & Arabasadi, A. (2017). Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental earth sciences, 76(21), 729.
  • [18] Labat, D. (2005). Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology, 314(1-4), 275-288.
  • [19] Nourani, V., Baghanam, A. H., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology, 514, 358-377.
  • [20] Kim, T. W., & Valdés, J. B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319-328.
  • [21] Belayneh, A., Adamowski, J., Khalil, B., & Ozga-Zielinski, B. (2014). Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508, 418-429.
  • [22] Maity, R., & Suman, M. (2019). Predictability of Hydrological Systems Using the Wavelet Transformation: Application to Drought Prediction. In Hydrology in a Changing World (pp. 109-137). Springer, Cham.
  • [23] Gyamfi, C., Amaning-Adjei, K., Anornu, G. K., Ndambuki, J. M., & Odai, S. N. (2019). Evolutional characteristics of hydro-meteorological drought studied using standardized indices and wavelet analysis. Modeling Earth Systems and Environment, 5(2), 455-469.
  • [24] Soh, Y. W., Koo, C. H., Huang, Y. F., & Fung, K. F. (2018). Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia. Computers and electronics in agriculture, 144, 164-173.
  • [25] Ahmadalipour, A., Moradkhani, H., and Demirel, M.C. (2017). A comparative assessment of projected meteorological and hydrological droughts: Elucidating the role of temperature. Journal of Hydrology, 553, 785-797.
  • [26] Danandeh Mehr, A., Sorman, A. U., Kahya, E., & Hesami Afshar, M. (2019). Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey. Hydrological Sciences Journal, DOI: 10.1080/02626667.2019.1691218
  • [27] Meresa, H. K., Osuch, M., and Romanowicz, R. (2016). Hydro-meteorological drought projections into the 21-st century for selected Polish catchments. Water, 8(5), 206.
  • [28] Khan, M., Muhammad, N., & El-Shafie, A. (2018). Wavelet-ANN versus ANN-based model for hydrometeorological drought forecasting. Water, 10(8), 998.
  • [29] Breiman, L., 2001. Random forests. Mach. Learn. 45 (1), 5–32.
  • [30] Chen, J., Li, M., & Wang, W. (2012). Statistical uncertainty estimation using random forests and its application to drought forecast. Mathematical Problems in Engineering, 2012.
  • [31] Yu, P.S., Yang, T.C., Chen, S.Y., Kuo, C.M., Tseng, H.W., 2017. Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. Journal of hydrology 552, 92-104.
  • [32] Zhao, W., Sánchez, N., Lu, H., Li, A., (2018). A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of hydrology 563, 1009-1024.
  • [33] Sadler, J.M., Goodall, J.L., Morsy, M.M., Spencer, K., (2018). Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest. Journal of hydrology 559, 43-55.
  • [34] Şarlak, N., & Güven, A. (2016). Global güneş radyasyon tahmini: Gaziantep uygulaması. Teknik Dergi, 27(3), 7561-7568.
  • [35] Danandeh Mehr, A., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A. M., & Yaseen, Z. M. (2018). Genetic programming in water resources engineering: A state-of-the-art review. Journal of hydrology 566, 643-667.
  • [36] Hu, J., Liu, B., & Peng, S. (2019). Forecasting salinity time series using RF and ELM approaches coupled with decomposition techniques. Stochastic Environmental Research and Risk Assessment, 1-19.
  • [37] Coifman, R.R.; M.V. Wickerhauser, (1992), "Entropy-based algorithms for best basis selection," IEEE Trans. on Inf. Theory, vol. 38, 2, pp. 713–718
  • [38] Hrnjica, B., & Danandeh Mehr, A. (2018). Optimized Genetic Programming Applications: Emerging Research and Opportunities: Emerging Research and Opportunities. IGI Global.
  • [39] Rahmani-Rezaeieh, A., Mohammadi, M., & Danandeh Mehr, A. (2019). Ensemble gene expression programming: a new approach for evolution of parsimonious streamflow forecasting model. Theoretical and Applied Climatology, 1-16.
  • [40] Danandeh Mehr, A., & Safari, M. J. S. (2020). Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts. Environmental Monitoring and Assessment, 192(1), 25.
  • [41] Danandeh Mehr, A., Kahya, E., & Olyaie, E. (2013). Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. Journal of Hydrology, 505, 240-249.
  • [42] Dikbaş, F. (2016). Büyük Menderes Akımlarının Frekans Tabanlı Tahmini. Teknik Dergi, 27(1), 7325-7343.
There are 42 citations in total.

Details

Primary Language English
Subjects Civil Engineering
Journal Section Articles
Authors

Ali Danandeh Mehr 0000-0003-2769-106X

Mir Jafar Sadegh Safarı 0000-0003-0559-5261

Vahid Nouranı This is me 0000-0002-6931-7060

Publication Date July 1, 2021
Submission Date August 15, 2019
Published in Issue Year 2021 Volume: 32 Issue: 4

Cite

APA Danandeh Mehr, A., Safarı, M. J. S., & Nouranı, V. (2021). Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting. Teknik Dergi, 32(4), 11029-11050. https://doi.org/10.18400/tekderg.605453
AMA Danandeh Mehr A, Safarı MJS, Nouranı V. Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting. Teknik Dergi. July 2021;32(4):11029-11050. doi:10.18400/tekderg.605453
Chicago Danandeh Mehr, Ali, Mir Jafar Sadegh Safarı, and Vahid Nouranı. “Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting”. Teknik Dergi 32, no. 4 (July 2021): 11029-50. https://doi.org/10.18400/tekderg.605453.
EndNote Danandeh Mehr A, Safarı MJS, Nouranı V (July 1, 2021) Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting. Teknik Dergi 32 4 11029–11050.
IEEE A. Danandeh Mehr, M. J. S. Safarı, and V. Nouranı, “Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting”, Teknik Dergi, vol. 32, no. 4, pp. 11029–11050, 2021, doi: 10.18400/tekderg.605453.
ISNAD Danandeh Mehr, Ali et al. “Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting”. Teknik Dergi 32/4 (July 2021), 11029-11050. https://doi.org/10.18400/tekderg.605453.
JAMA Danandeh Mehr A, Safarı MJS, Nouranı V. Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting. Teknik Dergi. 2021;32:11029–11050.
MLA Danandeh Mehr, Ali et al. “Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting”. Teknik Dergi, vol. 32, no. 4, 2021, pp. 11029-50, doi:10.18400/tekderg.605453.
Vancouver Danandeh Mehr A, Safarı MJS, Nouranı V. Wavelet Packet-Genetic Programming: A New Model for Meteorological Drought Hindcasting. Teknik Dergi. 2021;32(4):11029-50.

Cited By