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Dalgacık-Gen İfade Programlama ile Meteorolojik Kuraklık Tahmini: Çanakkale Örneği

Yıl 2022, Cilt: 13 Sayı: Ek (Suppl.) 1, 361 - 369, 31.12.2022
https://doi.org/10.29048/makufebed.1177323

Öz

Son yıllardaki küresel iklim değişikliğinden dolayı, yağış miktarı ve süresindeki farklılıklar kuraklık üzerinde büyük etkiye sahip olmaktadır. Bu nedenle, sürdürülebilir su kaynakları çalışmalarında kuraklık dikkate alınması gereken parametrelerden biridir. Bu çalışmada, ilk olarak, 1975-2010 yılları arasında bulunan tarihi yağış kayıtları kullanılarak Çanakkale, Bozcaada ve Gökçeada istasyonlarının standart yağış indisi (SYİ) ile 3-, 6-, 9- ve 12- aylık kuraklık indisleri belirlenmiştir. Daha sonra, Çanakkale ilinin kuraklık tahmini için Bozcaada ve Gökçeada istasyonlarının kuraklık değerlerinin girdi parametreleri olarak seçildiği gen ifade programlama (GEP) modelleri geliştirilmiştir. Ayrıca, aynı girdilerin dalgacık dönüşümü (D) ile üretilen alt serileri kullanılarak D-GEP modelleri üretilmiştir. Geliştirilen modeller incelendiğinde, 6-, 9- ve 12- aylık dönemlere ait belirleyicilik katsayıları (R2), GEP ve D-GEP modelleri için genel olarak 0,80'den yüksek bulunurken, 3- aylık dönem için R2 değerleri yaklaşık olarak sırasıyla 0,657 ve 0,704 elde edilmiştir. En yüksek R2 değeri 6- aylık dönemde D-GEP modeli için 0,868 olarak belirlenmiştir. Sonuç olarak, GEP ve D-GEP yaklaşımlarının kuraklık tahmininde başarılı oldukları görülmüştür.

Kaynakça

  • Abbasi, A., Khalili, K., Behmanesh, J., Shirzad, A. (2019). Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake. Theoretical and Applied Climatology, 138(1): 553-567.
  • Aksever, F. (2019). Drought analysis with standard precipitation index (SPI) method and groundwater exchange in the Kaklik (Honaz-Denizli) Plain. Journal of Engineering Sciences and Design, 7(1): 152-160.
  • Arslan, O., Bilgil, A., Veske, O. (2016). Meteorological drought analysis in Kızılırmak Basin using standardized precipitation index method. Nigde University Journal of Engineering Sciences, 5(2): 188-194.
  • Aytek, A., Kisi., O. (2008). A genetic programming approach to suspended sediment modelling. Journal of Hydrology, 351(3): 288-298.
  • Bacanlı, Ü.G., Kargı, P.G. (2019). Drought analysis in long and short term periods: Bursa case. Journal of Natural Hazards and Environment, 5(1): 166-174.
  • 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.
  • Belayneh, A., Adamowski, J., Khalil, B. (2016). Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustainable Water Resources Management, 2(1): 87-101.
  • Dadu, K. S., Deka, P. C. (2016). Applications of wavelet transform technique in hydrology—a brief review. In: Urban Hydrology, Watershed Management and Socio-Economic Aspects. Sarma, A.K., Singh, V.P., Kartha, S.A., Bhattachariya, R.K. (eds.), Springer Cham, Switzerland, 241-253.
  • Djerbouai, S., Souag-Gamane, D. (2016). Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management, 30(7): 2445-2464.
  • Efe, B., Özgür, E. (2014). Drought analysis of Konya and surroundings by standardized precipitation index (SPI) percent of normal index (PNI). 2nd International Drought and Desertification Symposium, September 16-18, 2014, Konya, Turkey, Book of Proceedings, 1-6.
  • Emel, G.G., Taşkın, Ç. (2002). Genetic algorithms and application areas. Bursa Uludağ Journal of Economy and Society, 21(1): 129-152.
  • Ferreira C. (2002). Gene expression programming in problem solving. In: Soft Computing and Industry. Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds), 635-653, Springer, London: DOI:10.1007/978-1-4471-0123-9_54.
  • Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2): 87–129.
  • Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Boston, United States.
  • Gümüş, V., Başak, A., Oruç, N. (2016). Drought analysis of Şanlıurfa Station with standard precipitation index (SPI). Harran University Journal of Engineering, 1(1): 36-44.
  • Karimi, S., Shiri, J., Kisi, O., Shiri, A.A. (2016). Short-term and long-term streamflow prediction by using 'wavelet–gene expression' programming approach. ISH Journal of Hydraulic Engineering, 22(2): 148-162.
  • Koza, J.R. (1992). Genetic programming: On the programming of computers by means of natural selection. A Bradford Book, Massachusetts London, England.
  • Malakiya, A.D., Suryanarayana, T.M.V. (2016). Assessment of drought using standardized precipitation index (SPI) and reconnaissance drought index (RDI): A Case Study of Amreli District. International Journal of Science and Research, 5(8): 1995-2002.
  • McKee, T.B., Doesken, N.J., Kleist, J. (1993). The relationship of drought frequency and duration to time scale. 8th Conference on Applied Climatology. January 17-22, 1993, Anaheim, CA, USA, Book of Proceeding, 1-6.
  • Mehdizadeh, S., Ahmadi, F., Mehr, A.D., Safari, M.J.S. (2020). Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology, 587, 125017; DOI:10.1016/j.jhydrol.2020.125017.
  • Mehr, A.D. (2018). An improved gene expression programming model for streamflow forecasting in intermittent streams. Journal of Hydrology, 563: 669-678.
  • Shoaib, M., Shamseldin, A.Y., Melville, B.W., Khan, M.M. (2015). Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach. Journal of Hydrology, 527: 326-344.
  • Solgi, A., Pourhaghi, A., Bahmani, R., Zarei, H. (2017). Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Journal of Earth System Science, 126(5): 65; DOI:10.1007/s12040-017-0850-y
  • Şen, Z. (2003). Su bilimi ve yöntemleri. Su Vakfı Yayınları, İstanbul, Türkiye.
  • Şen, Z. (2009). Kuraklık afet ve modern hesaplama yöntemleri. Su Vakfı Yayınları, İstanbul, Türkiye.
  • Taylan, E.D., Terzi, Ö., Baykal, T. (2021). Hybrid wavelet–artificial intelligence models in meteorological drought estimation. Journal of Earth System Science, 130(1): 1-13: DOI: 10.1007/s12040-020-01488-9
  • Terzi, Ö., Taylan, E. D., Özcanoğlu, O., Baykal, T. (2019). Drought estimation of Çanakkale with data mining. Düzce University Journal of Science & Technology, 7(1): 124-135.
  • Thakur, R., Manekar, V.L. (2022). Artificial intelligence-based image classification techniques for hydrologic applications. Applied Artificial Intelligence, 36(1): 2014185; DOI: 10.1080/08839514.2021.2014185
  • Tsakiris, G., Pangalou, D., Vangelis, H. (2007). Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21(5): 821-833.
  • URL-1 (2021). Coğrafi yapı https://www.canakkale.bel.tr/tr/sayfa/1125-cografi-yapi (Access Date: 18.05.2021)
  • URL-2 (2021). İzmir Meteorology Directorate, http://izmir.mgm.gov.tr/FILES/iklim/canakkale_iklim.pdf (Access Date: 18.05.2021)
  • Wang, W., Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1(1): 67–71.

Meteorological Drought Estimation by Wavelet-Gene Expression Programming: Case Study of Çanakkale, Türkiye

Yıl 2022, Cilt: 13 Sayı: Ek (Suppl.) 1, 361 - 369, 31.12.2022
https://doi.org/10.29048/makufebed.1177323

Öz

The differences in duration and amount of precipitation significantly affect the drought due to global climate change in the last decades. Therefore, drought is one of the parameters to be considered for sustainable water resources studies. In this study, firstly, by using historical precipitation records between the years 1975-2010, the drought indices of 3-, 6-, 9- and 12- months of Çanakkale, Bozcaada, and Gökçeada stations were determined with the standardized precipitation index (SPI). Then, gene expression programming (GEP) models were developed in which the drought values of Bozcaada and Gökçeada stations were selected as input parameters for the drought prediction of Çanakkale province. In addition, W-GEP models were developed using a sub-series of the same inputs produced with wavelet transform (W). Examining the developed models, the determination coefficients (R2) for the 6-, 9- and 12-months periods were generally higher than 0.80 for GEP and W-GEP models. In contrast, the R2 value for the 3- month period was approximately 0.657 and 0.704, respectively. The highest R2 value was determined as 0.868 for the W-GEP model during the 6- month period. As a result, the GEP and W-GEP approaches were found to be successful in the estimation of drought.

Kaynakça

  • Abbasi, A., Khalili, K., Behmanesh, J., Shirzad, A. (2019). Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake. Theoretical and Applied Climatology, 138(1): 553-567.
  • Aksever, F. (2019). Drought analysis with standard precipitation index (SPI) method and groundwater exchange in the Kaklik (Honaz-Denizli) Plain. Journal of Engineering Sciences and Design, 7(1): 152-160.
  • Arslan, O., Bilgil, A., Veske, O. (2016). Meteorological drought analysis in Kızılırmak Basin using standardized precipitation index method. Nigde University Journal of Engineering Sciences, 5(2): 188-194.
  • Aytek, A., Kisi., O. (2008). A genetic programming approach to suspended sediment modelling. Journal of Hydrology, 351(3): 288-298.
  • Bacanlı, Ü.G., Kargı, P.G. (2019). Drought analysis in long and short term periods: Bursa case. Journal of Natural Hazards and Environment, 5(1): 166-174.
  • 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.
  • Belayneh, A., Adamowski, J., Khalil, B. (2016). Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustainable Water Resources Management, 2(1): 87-101.
  • Dadu, K. S., Deka, P. C. (2016). Applications of wavelet transform technique in hydrology—a brief review. In: Urban Hydrology, Watershed Management and Socio-Economic Aspects. Sarma, A.K., Singh, V.P., Kartha, S.A., Bhattachariya, R.K. (eds.), Springer Cham, Switzerland, 241-253.
  • Djerbouai, S., Souag-Gamane, D. (2016). Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management, 30(7): 2445-2464.
  • Efe, B., Özgür, E. (2014). Drought analysis of Konya and surroundings by standardized precipitation index (SPI) percent of normal index (PNI). 2nd International Drought and Desertification Symposium, September 16-18, 2014, Konya, Turkey, Book of Proceedings, 1-6.
  • Emel, G.G., Taşkın, Ç. (2002). Genetic algorithms and application areas. Bursa Uludağ Journal of Economy and Society, 21(1): 129-152.
  • Ferreira C. (2002). Gene expression programming in problem solving. In: Soft Computing and Industry. Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds), 635-653, Springer, London: DOI:10.1007/978-1-4471-0123-9_54.
  • Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2): 87–129.
  • Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Boston, United States.
  • Gümüş, V., Başak, A., Oruç, N. (2016). Drought analysis of Şanlıurfa Station with standard precipitation index (SPI). Harran University Journal of Engineering, 1(1): 36-44.
  • Karimi, S., Shiri, J., Kisi, O., Shiri, A.A. (2016). Short-term and long-term streamflow prediction by using 'wavelet–gene expression' programming approach. ISH Journal of Hydraulic Engineering, 22(2): 148-162.
  • Koza, J.R. (1992). Genetic programming: On the programming of computers by means of natural selection. A Bradford Book, Massachusetts London, England.
  • Malakiya, A.D., Suryanarayana, T.M.V. (2016). Assessment of drought using standardized precipitation index (SPI) and reconnaissance drought index (RDI): A Case Study of Amreli District. International Journal of Science and Research, 5(8): 1995-2002.
  • McKee, T.B., Doesken, N.J., Kleist, J. (1993). The relationship of drought frequency and duration to time scale. 8th Conference on Applied Climatology. January 17-22, 1993, Anaheim, CA, USA, Book of Proceeding, 1-6.
  • Mehdizadeh, S., Ahmadi, F., Mehr, A.D., Safari, M.J.S. (2020). Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology, 587, 125017; DOI:10.1016/j.jhydrol.2020.125017.
  • Mehr, A.D. (2018). An improved gene expression programming model for streamflow forecasting in intermittent streams. Journal of Hydrology, 563: 669-678.
  • Shoaib, M., Shamseldin, A.Y., Melville, B.W., Khan, M.M. (2015). Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach. Journal of Hydrology, 527: 326-344.
  • Solgi, A., Pourhaghi, A., Bahmani, R., Zarei, H. (2017). Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Journal of Earth System Science, 126(5): 65; DOI:10.1007/s12040-017-0850-y
  • Şen, Z. (2003). Su bilimi ve yöntemleri. Su Vakfı Yayınları, İstanbul, Türkiye.
  • Şen, Z. (2009). Kuraklık afet ve modern hesaplama yöntemleri. Su Vakfı Yayınları, İstanbul, Türkiye.
  • Taylan, E.D., Terzi, Ö., Baykal, T. (2021). Hybrid wavelet–artificial intelligence models in meteorological drought estimation. Journal of Earth System Science, 130(1): 1-13: DOI: 10.1007/s12040-020-01488-9
  • Terzi, Ö., Taylan, E. D., Özcanoğlu, O., Baykal, T. (2019). Drought estimation of Çanakkale with data mining. Düzce University Journal of Science & Technology, 7(1): 124-135.
  • Thakur, R., Manekar, V.L. (2022). Artificial intelligence-based image classification techniques for hydrologic applications. Applied Artificial Intelligence, 36(1): 2014185; DOI: 10.1080/08839514.2021.2014185
  • Tsakiris, G., Pangalou, D., Vangelis, H. (2007). Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21(5): 821-833.
  • URL-1 (2021). Coğrafi yapı https://www.canakkale.bel.tr/tr/sayfa/1125-cografi-yapi (Access Date: 18.05.2021)
  • URL-2 (2021). İzmir Meteorology Directorate, http://izmir.mgm.gov.tr/FILES/iklim/canakkale_iklim.pdf (Access Date: 18.05.2021)
  • Wang, W., Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1(1): 67–71.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Özlem Terzi 0000-0001-6429-5176

Dilek Taylan 0000-0003-0734-1900

Yayımlanma Tarihi 31 Aralık 2022
Kabul Tarihi 7 Kasım 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 13 Sayı: Ek (Suppl.) 1

Kaynak Göster

APA Terzi, Ö., & Taylan, D. (2022). Meteorological Drought Estimation by Wavelet-Gene Expression Programming: Case Study of Çanakkale, Türkiye. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 13(Ek (Suppl.) 1), 361-369. https://doi.org/10.29048/makufebed.1177323