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Hydrological and Meteorological Drought Forecasting for the Yesilirmak River Basin, Turkey

Yıl 2021, Cilt: 4 Sayı: 2, 121 - 135, 23.12.2021

Alyar Boustani Hezarani Utku Zeybekoğlu Aslı Ülke Keskin

https://doi.org/10.51764/smutgd.993792
Cited By: 3

Öz

Drought is the most dangerous natural disaster. It differs from the other disasters in that it occurs insidiously, its effects are revealed gradually, and it persists for a long period. Drought has huge, negative effects on both society and natural ecosystems. In this study, values from the Standardized Precipitation Index (SPI) were used to generate drought estimation models by using Artificial Neural Networks (ANN). In addition, the probability of hydrological drought was determined by using SPI values to predict Streamflow Drought Index (SDI) values with ANN. Also, the SPI and SDI were used as the meteorological and hydrological drought indices, respectively, in conjunction with Feed Forward Neural Networks (FFNN), in ANN models. For this purpose, three rainfall and three flow gauging stations located in the Yesilirmak River Basin of Turkey were selected as the study units. The SPI and SDI values for the stations were calculated in order to create ANN estimation models. Different ANN forecasting models for SPI and SDI were trained and tested. In addition, the effects of the spatial distribution of precipitation on flows were determined by using the Thiessen Method to develop the SDI prediction model. The results generated by the ANN prediction models and resulting values were compared and the performances of the models were analyzed. The combination of ANN and SPI predicted meteorological drought with high accuracy but the combination of ANN and SDI was not as good in predicting hydrological drought.

Anahtar Kelimeler

ANN Drought Indices Hydrological Drought Meteorological Drought Yesilirmak Basin Turkey.

Kaynakça

  • Abramowitz, M., & Stegun, I. (1965). Handbook of mathematical functions. National bureau of standards, applied mathematics series–55. Washington, D.C.
  • Altın, T. B., Sarış, F., & Altın, B. N. (2019). Determination of drought intensity in Seyhan and Ceyhan River Basins, Turkey, by hydrological drought analysis. Theoretical and Applied Climatology, 139(1-2), 95-107. https://doi.org/10.1007/s00704-019-02957-y
  • Azimi, S., & Moghaddam, M. A. (2020). Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index. Water Resour Manage 34(4), 1369-1405. https://doi.org/10.1007/s11269-020-02507-6
  • Bacanli, U. G., Firat, M., & Dikbas, F. (2009). Adaptive neuro-fuzzy ınference system for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23, 1143-1154. https://doi.org/10.1007/s00477-008-0288-5
  • Bacanli, U. G. (2017). Trend analysis of precipitation and drought in the Aegean region, Turkey. Meteorologıcal Applıcatıons, 24(2), 239-249. https://doi.org/10.1002/met.1622
  • Boyogueno, S. H., Mbessa, M., & Tatietse, T. T. (2012). Prediction of flow-rate of Sanaga Basin in Cameroon USING HEC-HMS hydrological system: application to the Djerem sub-basin at Mbakaou. Energy Environ Res, 2(1), 205-216. https://doi.org/10.5539/eer.v2n1p205
  • Buckland, C. E., Bailey, R. M., & Thomas, D. S. G. (2019). Using artificial neural networks to predict future dryland responses to human and climate disturbances. Sci Rep, 9, 1-13. https://doi.org/10.1038/s41598-019-40429-5
  • Cigizoglu H.K. (2008). Artificial Neural Networks In Water Resources. In: Coskun H.G., Cigizoglu H.K., Maktav M.D. (eds) Integration of Information for Environmental Security. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6575-0_8
  • Dawson, C. W. & Wily, R. L. (2001). Hydrological modelling using artificial neural networks. Progress in Physical Geography: Earth and Environment, 25(1), 80-108. https://doi.org/10.1177/030913330102500104
  • Demir, V. & Ülke Keskin, A. (2020). Yapay Sinir Ağları Yardımıyla Yükseklik Modellemesi (Samsun-Mert Irmağı Havzası Örneği). Gazi Mühendislik Bilimleri Dergisi (GMBD), 6 (1), 54-61.
  • Deo, R. C., & Sahin, M. (2015) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmospheric Research, 153, 512-525. https://doi.org/10.1016/j.atmosres.2014.10.016
  • Dogan, E. , Isik, S. & Sandalcı, M. (2007). Günlük Buharlaşmanın Yapay Sinir Ağları Kullanarak Tahmin Edilmesi. Teknik Dergi, 18(87), 4119-4131.
  • Durdu, O. F. (2010). Application of linear stochastic models for drought forecasting in the Buyuk Menderes river basin, Western Turkey. Stochastic Environmental Research and Risk Assessment, 24, 1145-1162. https://doi.org/10.1007/s00477-010-0366-3
  • Erinç, S. (1949). The climates of Turkey according to Thornthwaite’s classifications. Annals of the Association of American Geographers, 39, 26-46.
  • Erogluer, T. A., & Apaydin, H. (2020). Estimation of Drought by Streamflow Drought Index (SDI) and Artificial Neural Networks (ANNs) in Ankara-Nallihan Region. Turkish Journal of Agriculture - Food Science and Technology, 8(2), 348-357. https://doi.org/10.24925/turjaf.v8i2.348-357.3045
  • Feng, L. & Hong, W. (2008). On hydrologic calculation using artificial neural networks. Applied Mathematics Letters, 21(5), 453-458. https://doi.org/10.1016/j.aml.2007.06.004
  • Fırat, M. & Güngör, M. (2004). Askı Madde Konsantrasyonu ve Miktarının Yapay Sinir Ağları ile Belirlenmesi. Teknik Dergi, 15(73), 3267-3282.
  • Firat, M. (2008). Comparison of Artificial Intelligence Techniques for river flow forecasting. Hydrology and Earth System Sciences, 12, 123-139. https://doi.org/10.5194/hess-12-123-2008
  • Firat, M., & Gungor, M. (2009). Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 40(8), 731-737. https://doi.org/10.1016/j.advengsoft.2008.12.001
  • Gumus, V., & Algin, H. M. (2017). Meteorological and hydrological drought analysis of the Seyhan−Ceyhan River Basins, Turkey. Meteorological Applications, 24(1), 62-73. https://doi.org/10.1002/met.1605
  • Guttman, N. B. (1998). Comparing the Palmer drought index and the standardized precipitation index. Journal of the American Water Resources Association, 34(1), 113-121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x
  • Guttman, N. B. (1999). Accepting the standardized precipitation index: a calculation algorithm. Journal of the American Water Resources Association, 35(2), 311-322. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x
  • Hejazizadeh, Z., & Javizadeh, S. (2011). Introduction to drought and its indices. Iran: Samt Publications.
  • Hong, X., Guo, S., Zhou Y., & Xiong, L. (2015). Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin. Stochastic Environmental Research and Risk Assessment 29, 1235-1247. https://doi.org/10.1007/s00477-014-0949-5
  • Keskin, M. E., Terzi, O., Taylan, E. D., & Kucukyaman, D. (2009). Meteorological drought analysis using data-driven models for the Lakes District, Turkey. Hydrological Sciences Journal, 54(6), 1114-1124. https://doi.org/10.1623/hysj.54.6.1114
  • Kitanidis, P. K., & Bras, R. L. (1980). Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resour Res, 16(6), 1034-1044. https://doi.org/10.1029/WR016i006p01034
  • Lowe, D., & Tipping, M. (1996). Feed-forward neural networks and topographic mappings for exploratory data analysis. Neural Comput & Applic, 4(2), 83-95. https://doi.org/10.1007/BF01413744
  • Masinde, M. (2014). Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability. Mitig Adapt Strateg Glob Change, 19(8), 1139-1162. (2014). https://doi.org/10.1007/s11027-013-9464-0
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. 8th. Conference on Applied Climatology, Anaheim, California.
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1995, January). Drought Monitoring with Multiple Time Scales. 9th Conference on Applied Climatology, Dallas. Texas.
  • Merkoci, A. L., Mustaqi, V., Mucaj, L., & Dvorani, M. (2013). Drought and implementation of Standardised Precipitation Index (Spi) on The Albanian Territory. Journal of the Faculty of Engineering and Architecture of Gazi University, 28(1), 161-166.
  • Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. https://doi.org/10.1016/j.jhydrol.2010.07.012
  • Mishra, A. K., & Singh, V. P. (2011). Drought modeling- a review. Journal of Hydrology, 403(1-2), 157-175. https://doi.org/10.1016/j.jhydrol.2011.03.049
  • Nalbantis, I. (2008). Evaluation of a hydrological drought index. European Water, 23(24), 67-77.
  • Nalbantis, I., & Tsakiris, G. (2009). Assessment of Hydrological Drought Revisited. Water Resources Management, 23(5), 881-897. https://doi.org/10.1007/s11269-008-9305-1
  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. I. A discussion of principles. Journal of Hydrology, 10, 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  • Oguzturk, G. (2010). A drought analysis of the Kızılırmak basin using the standardized precipitation index (SPI) method and drought estimation using the artificial neural networks method. MSc Thesis, Kırıkkale University, Kirikkale, Turkey.
  • Oguztürk, G., & Yıldız, O. (2016). Assessing hydrological responses to droughts in the Hirfanli dam basin, Turkey. International Journal of Advances in Mechanical and Civil Engineering, 3(5),116-123.
  • Palmer, W. C. (1960). Meteorological Drought. Research Paper No.45, US.
  • Oyebode, O. & Stretch, D. (2019). Neural network modeling of hydrologicalsystems: A review of implementation techniques. Natural Resource Modeling, 32(1), 1-14. https://doi.org/10.1002/nrm.12189
  • Poornima, S., & Pushpalatha, M. (2019). Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network. Soft Comput, 23(18), 8399-8412. https://doi.org/10.1007/s00500-019-04120-1
  • Salinger, J. (1995). Conditions leading to drought in New Zealand. Water Atmosphere, 3(1), 11-12.
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Yeşilırmak Havzası için Hidrolojik ve Meteorolojik Kuraklık Tahmini, Türkiye

Yıl 2021, Cilt: 4 Sayı: 2, 121 - 135, 23.12.2021

Alyar Boustani Hezarani Utku Zeybekoğlu Aslı Ülke Keskin

https://doi.org/10.51764/smutgd.993792
Cited By: 3

Öz

Kuraklık en tehlikeli doğal afettir. Diğer afetlerden farkı, sinsi bir şekilde gerçekleşmesi, etkilerinin yavaş yavaş ortaya çıkması ve uzun süre devam etmesidir. Kuraklığın hem toplum hem de doğal ekosistemler üzerinde çok büyük, olumsuz etkileri vardır. Bu çalışmada, Yapay Sinir Ağları (YSA) kullanılarak kuraklık tahmin modelleri oluşturmak için Standardize Yağış İndeksi (SPI) değerleri kullanılmıştır. Ek olarak, YSA ile Akarsu Kuraklık İndeksi (SDI) değerlerini tahmin etmek için SPI değerleri kullanılarak hidrolojik kuraklık olasılığı belirlenmiştir. Ayrıca YSA modellerinde İleri Beslemeli Sinir Ağları (FFNN) ile birlikte sırasıyla meteorolojik ve hidrolojik kuraklık indeksleri olarak SPI ve SDI kullanılmıştır. Bu amaçla, Türkiye Yeşilırmak Havzasında bulunan üç yağış ve üç akış ölçme istasyonu çalışma birimi olarak seçilmiştir. YSA tahmin modellerini oluşturmak için istasyonlara ait SPI ve SDI değerleri hesaplanmıştır. SPI ve SDI için farklı YSA tahmin modelleri eğitilmiş ve test edilmiştir. Ayrıca, SDI tahmin modelini geliştirmek için Thiessen Metodu kullanılarak yağışların mekansal dağılımının akışlar üzerindeki etkileri belirlenmiştir. YSA tahmin modellerinin ürettiği sonuçlar ve elde edilen değerler karşılaştırılarak modellerin performansları analiz edilmiştir. ANN ve SPI kombinasyonu meteorolojik kuraklığı yüksek doğrulukla öngördü, ancak ANN ve SDI kombinasyonu hidrolojik kuraklığı tahmin etmede o kadar iyi değildir.

Anahtar Kelimeler

ANN Kuraklık Endeksleri Hidrolojik Kuraklık Meteorolojik Kuraklık Yeşilırmak Havzası Türkiye.

Kaynakça

  • Abramowitz, M., & Stegun, I. (1965). Handbook of mathematical functions. National bureau of standards, applied mathematics series–55. Washington, D.C.
  • Altın, T. B., Sarış, F., & Altın, B. N. (2019). Determination of drought intensity in Seyhan and Ceyhan River Basins, Turkey, by hydrological drought analysis. Theoretical and Applied Climatology, 139(1-2), 95-107. https://doi.org/10.1007/s00704-019-02957-y
  • Azimi, S., & Moghaddam, M. A. (2020). Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index. Water Resour Manage 34(4), 1369-1405. https://doi.org/10.1007/s11269-020-02507-6
  • Bacanli, U. G., Firat, M., & Dikbas, F. (2009). Adaptive neuro-fuzzy ınference system for drought forecasting. Stochastic Environmental Research and Risk Assessment, 23, 1143-1154. https://doi.org/10.1007/s00477-008-0288-5
  • Bacanli, U. G. (2017). Trend analysis of precipitation and drought in the Aegean region, Turkey. Meteorologıcal Applıcatıons, 24(2), 239-249. https://doi.org/10.1002/met.1622
  • Boyogueno, S. H., Mbessa, M., & Tatietse, T. T. (2012). Prediction of flow-rate of Sanaga Basin in Cameroon USING HEC-HMS hydrological system: application to the Djerem sub-basin at Mbakaou. Energy Environ Res, 2(1), 205-216. https://doi.org/10.5539/eer.v2n1p205
  • Buckland, C. E., Bailey, R. M., & Thomas, D. S. G. (2019). Using artificial neural networks to predict future dryland responses to human and climate disturbances. Sci Rep, 9, 1-13. https://doi.org/10.1038/s41598-019-40429-5
  • Cigizoglu H.K. (2008). Artificial Neural Networks In Water Resources. In: Coskun H.G., Cigizoglu H.K., Maktav M.D. (eds) Integration of Information for Environmental Security. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6575-0_8
  • Dawson, C. W. & Wily, R. L. (2001). Hydrological modelling using artificial neural networks. Progress in Physical Geography: Earth and Environment, 25(1), 80-108. https://doi.org/10.1177/030913330102500104
  • Demir, V. & Ülke Keskin, A. (2020). Yapay Sinir Ağları Yardımıyla Yükseklik Modellemesi (Samsun-Mert Irmağı Havzası Örneği). Gazi Mühendislik Bilimleri Dergisi (GMBD), 6 (1), 54-61.
  • Deo, R. C., & Sahin, M. (2015) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmospheric Research, 153, 512-525. https://doi.org/10.1016/j.atmosres.2014.10.016
  • Dogan, E. , Isik, S. & Sandalcı, M. (2007). Günlük Buharlaşmanın Yapay Sinir Ağları Kullanarak Tahmin Edilmesi. Teknik Dergi, 18(87), 4119-4131.
  • Durdu, O. F. (2010). Application of linear stochastic models for drought forecasting in the Buyuk Menderes river basin, Western Turkey. Stochastic Environmental Research and Risk Assessment, 24, 1145-1162. https://doi.org/10.1007/s00477-010-0366-3
  • Erinç, S. (1949). The climates of Turkey according to Thornthwaite’s classifications. Annals of the Association of American Geographers, 39, 26-46.
  • Erogluer, T. A., & Apaydin, H. (2020). Estimation of Drought by Streamflow Drought Index (SDI) and Artificial Neural Networks (ANNs) in Ankara-Nallihan Region. Turkish Journal of Agriculture - Food Science and Technology, 8(2), 348-357. https://doi.org/10.24925/turjaf.v8i2.348-357.3045
  • Feng, L. & Hong, W. (2008). On hydrologic calculation using artificial neural networks. Applied Mathematics Letters, 21(5), 453-458. https://doi.org/10.1016/j.aml.2007.06.004
  • Fırat, M. & Güngör, M. (2004). Askı Madde Konsantrasyonu ve Miktarının Yapay Sinir Ağları ile Belirlenmesi. Teknik Dergi, 15(73), 3267-3282.
  • Firat, M. (2008). Comparison of Artificial Intelligence Techniques for river flow forecasting. Hydrology and Earth System Sciences, 12, 123-139. https://doi.org/10.5194/hess-12-123-2008
  • Firat, M., & Gungor, M. (2009). Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers. Advances in Engineering Software, 40(8), 731-737. https://doi.org/10.1016/j.advengsoft.2008.12.001
  • Gumus, V., & Algin, H. M. (2017). Meteorological and hydrological drought analysis of the Seyhan−Ceyhan River Basins, Turkey. Meteorological Applications, 24(1), 62-73. https://doi.org/10.1002/met.1605
  • Guttman, N. B. (1998). Comparing the Palmer drought index and the standardized precipitation index. Journal of the American Water Resources Association, 34(1), 113-121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x
  • Guttman, N. B. (1999). Accepting the standardized precipitation index: a calculation algorithm. Journal of the American Water Resources Association, 35(2), 311-322. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x
  • Hejazizadeh, Z., & Javizadeh, S. (2011). Introduction to drought and its indices. Iran: Samt Publications.
  • Hong, X., Guo, S., Zhou Y., & Xiong, L. (2015). Uncertainties in assessing hydrological drought using streamflow drought index for the upper Yangtze River basin. Stochastic Environmental Research and Risk Assessment 29, 1235-1247. https://doi.org/10.1007/s00477-014-0949-5
  • Keskin, M. E., Terzi, O., Taylan, E. D., & Kucukyaman, D. (2009). Meteorological drought analysis using data-driven models for the Lakes District, Turkey. Hydrological Sciences Journal, 54(6), 1114-1124. https://doi.org/10.1623/hysj.54.6.1114
  • Kitanidis, P. K., & Bras, R. L. (1980). Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resour Res, 16(6), 1034-1044. https://doi.org/10.1029/WR016i006p01034
  • Lowe, D., & Tipping, M. (1996). Feed-forward neural networks and topographic mappings for exploratory data analysis. Neural Comput & Applic, 4(2), 83-95. https://doi.org/10.1007/BF01413744
  • Masinde, M. (2014). Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability. Mitig Adapt Strateg Glob Change, 19(8), 1139-1162. (2014). https://doi.org/10.1007/s11027-013-9464-0
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. 8th. Conference on Applied Climatology, Anaheim, California.
  • McKee, T. B., Doesken, N. J., & Kleist, J. (1995, January). Drought Monitoring with Multiple Time Scales. 9th Conference on Applied Climatology, Dallas. Texas.
  • Merkoci, A. L., Mustaqi, V., Mucaj, L., & Dvorani, M. (2013). Drought and implementation of Standardised Precipitation Index (Spi) on The Albanian Territory. Journal of the Faculty of Engineering and Architecture of Gazi University, 28(1), 161-166.
  • Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. https://doi.org/10.1016/j.jhydrol.2010.07.012
  • Mishra, A. K., & Singh, V. P. (2011). Drought modeling- a review. Journal of Hydrology, 403(1-2), 157-175. https://doi.org/10.1016/j.jhydrol.2011.03.049
  • Nalbantis, I. (2008). Evaluation of a hydrological drought index. European Water, 23(24), 67-77.
  • Nalbantis, I., & Tsakiris, G. (2009). Assessment of Hydrological Drought Revisited. Water Resources Management, 23(5), 881-897. https://doi.org/10.1007/s11269-008-9305-1
  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. I. A discussion of principles. Journal of Hydrology, 10, 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  • Oguzturk, G. (2010). A drought analysis of the Kızılırmak basin using the standardized precipitation index (SPI) method and drought estimation using the artificial neural networks method. MSc Thesis, Kırıkkale University, Kirikkale, Turkey.
  • Oguztürk, G., & Yıldız, O. (2016). Assessing hydrological responses to droughts in the Hirfanli dam basin, Turkey. International Journal of Advances in Mechanical and Civil Engineering, 3(5),116-123.
  • Palmer, W. C. (1960). Meteorological Drought. Research Paper No.45, US.
  • Oyebode, O. & Stretch, D. (2019). Neural network modeling of hydrologicalsystems: A review of implementation techniques. Natural Resource Modeling, 32(1), 1-14. https://doi.org/10.1002/nrm.12189
  • Poornima, S., & Pushpalatha, M. (2019). Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network. Soft Comput, 23(18), 8399-8412. https://doi.org/10.1007/s00500-019-04120-1
  • Salinger, J. (1995). Conditions leading to drought in New Zealand. Water Atmosphere, 3(1), 11-12.
  • Selçuk, D. (2017). Drought analysis and estimation in Kizilirmak basin using hydrometeorological parameters. Master’s thesis, Ondokuz Mayıs University, Samsun, Turkey.
  • Shin, C. S., Huang, B., Dirmeyer, P. A., Halder, S., & Kumar, A. (2020). Sensitivity of U.S. Drought Prediction Skill to Land Initial States. Journal of Hydrometeorology, 21(12), 2793-2811. https://doi.org/10.1175/JHM-D-20-0025.1
  • Sırdas, S. (2002). Meteorological drought modelling and application to Turkey. Doctoral thesis, Istanbul Technical University, Istanbul, Turkey.
  • Soleimani H, Ahmadi H, & Zehtabian G. 2013. Comparison of temporal and spatial trend of SPI, DI and CZI as important drought indices to map using IDW Method in Taleghan watershed. Annals of Biological Research, 4(6), 46-55.
  • Sönmez, F. K., Komuscu, A. U., Erkan, A., & Turgu, E. (2005). An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Natural Hazards, 35(2), 243-264. https://doi.org/10.1007/s11069-004-5704-7
  • Şen, Z. (2004). Yapay Sinir Ağları İlkeleri. İstanbul: Su Vakfı Yayınları.
  • Tabari, H., Abghari, H., & Talaee, H. (2012). Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrological Processes, 26, 3351-3361. https://doi.org/10.1002/hyp.8460
  • Tallaksen, L. M., & Van Lanen, H. A. J. (2004). Hydrological Drought: Processes and estimation methods for streamflow and groundwater. Elsevier, Netherlands.
  • Tanoglu, A. (1943). Indices D'aridite De La Turquie. Turkish Geographical Review, 1, 36-41.
  • Taylan, E.D., Terzi, Ö., & Baykal, T. (2021). Hybrid wavelet–artificial intelligence models in meteorological drought estimation. J Earth Syst Sci, 130(1), 1-13. https://doi.org/10.1007/s12040-020-01488-9
  • Thom, H. C. S. (1958). A note on the gamma distribution. Monthly Weather Review, 86(4), 117-122.
  • The Scientific and Technological Research Council of Turkey (TUBITAK) (2010) Havza Koruma Eylem Planlarının Hazırlanması-Yesilirmak Havzası, 557 pp (in Turkish).
  • White, D. H., & Walcott, J. J. (2009). The role of seasonal indices in monitoring and assessing agricultural and other droughts: a review. Crop and Pasture Science, 60, 599–616. https://doi.org/10.1071/CP08378
  • Wilhite, D. A. (2000). Drought: A global assessment. Routledge Press, London and New York, Volume Ι.
  • Yacoub, E., & Tayfur, G. (2017). Evaluation and assessment of meteorological drought by different methods in Trarza region, Mauritania. Water Resour Manage, 31, 825-845. https://doi.org/10.1007/s11269-016-1510-8
  • Yacoub, E., & Tayfur, G. (2020). Spatial and temporal of variation of meteorological drought and precipitation trend analysis over whole Mauritania. Journal of African Earth Sciences, 163, 1-12. https://doi.org/10.1016/j.jafrearsci.2020.103761
  • Zhang, G., Patuwo, B.E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: the state of the art. Int J Forecast, 14(1), 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Alyar Boustani Hezarani 0000-0001-8763-6607

Utku Zeybekoğlu 0000-0001-5307-8563

Aslı Ülke Keskin 0000-0002-9676-8377

Yayımlanma Tarihi 23 Aralık 2021
Gönderilme Tarihi 10 Eylül 2021
Kabul Tarihi 7 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 2

Kaynak Göster

APA Boustani Hezarani, A., Zeybekoğlu, U., & Ülke Keskin, A. (2021). Hydrological and Meteorological Drought Forecasting for the Yesilirmak River Basin, Turkey. Sürdürülebilir Mühendislik Uygulamaları Ve Teknolojik Gelişmeler Dergisi, 4(2), 121-135. https://doi.org/10.51764/smutgd.993792

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