Araştırma Makalesi
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On the Calibration of Multigene Genetic Programming to Simulate Low Flows in the Moselle River

Yıl 2016, Cilt: 21 Sayı: 2, 365 - 376, 16.12.2016

Ali Danandeh Mehr Mehmet Cüneyd Demirel

https://doi.org/10.17482/uumfd.278107
Cited By: 6

Öz

The aim of this paper is to
calibrate a data-driven model to simulate Moselle River flows and compare the
performance with three different hydrologic models from a previous study. For
consistency a similar set up and error metric are used to evaluate the model
results. Precipitation, potential evapotranspiration and streamflow from
previous day have been used as inputs. Based on the calibration and validation
results, the proposed multigene genetic programming model is the best
performing model among four models. The timing and the magnitude of extreme low
flow events could be captured even when we use root mean squared error as the
objective function for model calibration. Although the model is developed and calibrated
for Moselle River flows, the multigene genetic algorithm offers a great
opportunity for hydrologic prediction and forecast problems in the river basins
with scarce data issues.

Anahtar Kelimeler

Low flows calibration genetic programming ANN HBV and GR4J

Kaynakça

  • Arsenault, R., Poulin, A., Côté, P., Brissette, F., 2014. Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration. J. Hydrol. Eng. 19, 1374–1384. doi:10.1061/(ASCE)HE.1943-5584.0000938
  • Danandeh Mehr, A., Kahya, E., Olyaie, E., 2013. Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J. Hydrol. 505, 240–249. doi:10.1016/j.jhydrol.2013.10.003
  • Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2015. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models. Hydrol. Earth Syst. Sci. 19, 275–291. doi:10.5194/hess-19-275-2015
  • Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2013a. Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations. Hydrol. Earth Syst. Sci. 17, 4241–4257. doi:10.5194/hess-17-4241-2013
  • Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2013b. Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models. Water Resour. Res. 49, 4035–4053. doi:10.1002/wrcr.20294
  • Demirel, M.C., Venancio, A., Kahya, E., 2009. Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv. Eng. Softw. 40, 467–473. doi:10.1016/j.advengsoft.2008.08.002
  • Duan, Q.Y., Gupta, V.K., Sorooshian, S., 1993. Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl. 76, 501–521. doi:10.1007/BF00939380
  • Gandomi, A.H., Alavi, A.H., 2012. A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput. Appl. 21, 171–187. doi:10.1007/s00521-011-0734-z
  • Ghorbani, M.A., Khatibi, R., Aytek, A., Makarynskyy, O., Shiri, J., 2010. Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks. Comput. Geosci. 36, 620–627. doi:10.1016/j.cageo.2009.09.014
  • Griffin, D., Anchukaitis, K.J., 2014. How unusual is the 2012-2014 California drought? Geophys. Res. Lett. 41, 9017–9023. doi:10.1002/2014GL062433
  • Gupta, H. V, Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 377, 80–91. doi:10.1016/j.jhydrol.2009.08.003
  • Guven, A., 2009. Linear genetic programming for time-series modelling of daily flow rate. J. Earth Syst. Sci. 118, 137–146. doi:10.1007/s12040-009-0022-9
  • Hansen, N., Ostermeier, A., 2001. Completely Derandomized Self-Adaptation in Evolution Strategies. Evol. Comput. 9, 159–195. doi:10.1162/106365601750190398
  • Hesami, A.M., Sorman, A., Yilmaz, M., 2016. Conditional Copula-Based Spatial–Temporal Drought Characteristics Analysis—A Case Study over Turkey. Water 8, 426. doi:10.3390/w8100426
  • Khan, M., Azamathulla, H.M., Tufail, M., 2012. Gene-expression programming to predict pier scour depth using laboratory data. J. Hydroinformatics 14, 628. doi:10.2166/hydro.2011.008
  • Koza, J.R., 1992. Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge, MA.
  • Livneh, B., Kumar, R., Samaniego, L., 2015. Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin. Hydrol. Process. 29, 4638–4655. doi:10.1002/hyp.10601
  • Madadgar, S., Afshar, A., 2009. An Improved Continuous Ant Algorithm for Optimization of Water Resources Problems. Water Resour. Manag. 23, 2119–2139. doi:10.1007/s11269-008-9373-2
  • Madsen, H., 2000. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J. Hydrol. 235, 276–288. doi:10.1016/S0022-1694(00)00279-1
  • Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290. doi:10.1016/0022-1694(70)90255-6
  • Nicolle, P., Pushpalatha, R., Perrin, C., François, D., Thiéry, D., Mathevet, T., Le Lay, M., Besson, F., Soubeyroux, J.-M., Viel, C., Regimbeau, F., Andréassian, V., Maugis, P., Augeard, B., Morice, E., 2014. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments. Hydrol. Earth Syst. Sci. 18, 2829–2857. doi:10.5194/hess-18-2829-2014
  • Nourani, V., Komasi, M., Mano, A., 2009. A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling. Water Resour. Manag. 23, 2877–2894. doi:10.1007/s11269-009-9414-5
  • Pal, I., Towler, E., Livneh, B., 2015. How Can We Better Understand Low River Flows as Climate Changes? Eos (Washington. DC). 96. doi:10.1029/2015EO033875
  • Poli, R., Langdon, W.B., McPhee, N.F., 2008. A Field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, (With contributions by J. R. Koza).
  • Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., Andréassian, V., 2011. A downward structural sensitivity analysis of hydrological models to improve low-flow simulation. J. Hydrol. 411, 66–76. doi:10.1016/j.jhydrol.2011.09.034
  • Pushpalatha, R., Perrin, C., Moine, N. Le, Andréassian, V., 2012. A review of efficiency criteria suitable for evaluating low-flow simulations. J. Hydrol. 420–421, 171–182. doi:10.1016/j.jhydrol.2011.11.055
  • Roushangar, K., Mouaze, D., Shiri, J., 2014. Evaluation of genetic programming-based models for simulating friction factor in alluvial channels. J. Hydrol. 517, 1154–1161. doi:10.1016/j.jhydrol.2014.06.047
  • Samaniego, L., Kumar, R., Attinger, S., 2010. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res. 46, W05523. doi:10.1029/2008WR007327
  • Sattar, A.M.A., Gharabaghi, B., 2015. Gene expression models for prediction of longitudinal dispersion coefficient in streams. J. Hydrol. 524, 587–596. doi:10.1016/j.jhydrol.2015.03.016
  • Searson, D. P., Leahy, D. E., Willis, M.J., 2010. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression, in: In Proceedings of the International Multi Conference of Engineers and Computer Scientists. p. Vol. 1, 77-80.
  • Searson, D., 2015. GPTIPS 2: an open-source software platform for symbolic data mining., in: Al., A.H.G. et (Ed.), Chapter 22 in Handbook of Genetic Programming Applications. Springer, New York, NY.
  • Searson, D., 2009. GPTIPS: Genetic Programming & Symbolic Regression for MATLAB [WWW Document]. URL http://gptips.sourceforge.net.
  • Smakhtin, V.U., 2001. Low flow hydrology: a review. J. Hydrol. 240, 147–186.
  • Tian, Y., Booij, M.J., Xu, Y.-P., 2014. Uncertainty in high and low flows due to model structure and parameter errors. Stoch. Environ. Res. Risk Assess. 28, 319–332. doi:10.1007/s00477-013-0751-9
  • Uyumaz, A., Danandeh Mehr, A., Kahya, E., Erdem, H., 2014. Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach. J. Hydroinformatics 16, 1318. doi:10.2166/hydro.2014.112
  • Vormoor, K., Lawrence, D., Heistermann, M., Bronstert, A., 2015. Climate change impacts on the seasonality and generation processes of floods – projections and uncertainties for catchments with mixed snowmelt/rainfall regimes. Hydrol. Earth Syst. Sci. 19, 913–931. doi:10.5194/hess-19-913-2015
  • Zhang, X., Booij, M.J., Xu, Y.-P.Y.-P., 2015. Improved simulation of peak flows under climate change: Postprocessing or composite objective calibration? J. Hydrometeorol. 16, 2187–2208. doi:10.1175/JHM-D-14-0218.1

MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ

Yıl 2016, Cilt: 21 Sayı: 2, 365 - 376, 16.12.2016

Ali Danandeh Mehr Mehmet Cüneyd Demirel

https://doi.org/10.17482/uumfd.278107
Cited By: 6

Öz

Bu çalışmanın amacı Moselle nehrinin düşük
debilerini çoklu genetik programlama modeli ile benzetmek ve ayarı yapılan
modelin performansini daha önceki modellerle kiyaslamaktir. Tutarlılık için aynı
performans kriterleri ve model girdi çıktı düzenekleri kullanılmıştır. Tek değişen,
model yapısıdır. Yağiş, buharlaşma ve nehir debisi için dünkü değerler
kullanilarak bugünku nehir debisi benzetilmeye çalışılmıştır. Sonuçlar önerilen
genetik programlama modelinin dört model arasında en iyi sonuçlar verdiğini göstermektedir.
Az görülen düşük akımların zamanlama ve seviyesi amaç fonksiyonu etkin değerler
seçildiğinde dahi başariyla benzetilebilmektedir. Bu geliştirilen ve önerilen
model yapısı her ne kadar Moselle nehri için olsa da çoklu genetik programlama
algoritmasi genel olarak tüm nehir tahmin modelleri icin bir alternatif sunmaktadır.

Anahtar Kelimeler

Düşük debi model ayarlanma çoklu genetik programlama ANN HBV ve GR4J

Kaynakça

  • Arsenault, R., Poulin, A., Côté, P., Brissette, F., 2014. Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration. J. Hydrol. Eng. 19, 1374–1384. doi:10.1061/(ASCE)HE.1943-5584.0000938
  • Danandeh Mehr, A., Kahya, E., Olyaie, E., 2013. Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique. J. Hydrol. 505, 240–249. doi:10.1016/j.jhydrol.2013.10.003
  • Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2015. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models. Hydrol. Earth Syst. Sci. 19, 275–291. doi:10.5194/hess-19-275-2015
  • Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2013a. Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations. Hydrol. Earth Syst. Sci. 17, 4241–4257. doi:10.5194/hess-17-4241-2013
  • Demirel, M.C., Booij, M.J., Hoekstra, A.Y., 2013b. Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models. Water Resour. Res. 49, 4035–4053. doi:10.1002/wrcr.20294
  • Demirel, M.C., Venancio, A., Kahya, E., 2009. Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Adv. Eng. Softw. 40, 467–473. doi:10.1016/j.advengsoft.2008.08.002
  • Duan, Q.Y., Gupta, V.K., Sorooshian, S., 1993. Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl. 76, 501–521. doi:10.1007/BF00939380
  • Gandomi, A.H., Alavi, A.H., 2012. A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput. Appl. 21, 171–187. doi:10.1007/s00521-011-0734-z
  • Ghorbani, M.A., Khatibi, R., Aytek, A., Makarynskyy, O., Shiri, J., 2010. Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks. Comput. Geosci. 36, 620–627. doi:10.1016/j.cageo.2009.09.014
  • Griffin, D., Anchukaitis, K.J., 2014. How unusual is the 2012-2014 California drought? Geophys. Res. Lett. 41, 9017–9023. doi:10.1002/2014GL062433
  • Gupta, H. V, Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 377, 80–91. doi:10.1016/j.jhydrol.2009.08.003
  • Guven, A., 2009. Linear genetic programming for time-series modelling of daily flow rate. J. Earth Syst. Sci. 118, 137–146. doi:10.1007/s12040-009-0022-9
  • Hansen, N., Ostermeier, A., 2001. Completely Derandomized Self-Adaptation in Evolution Strategies. Evol. Comput. 9, 159–195. doi:10.1162/106365601750190398
  • Hesami, A.M., Sorman, A., Yilmaz, M., 2016. Conditional Copula-Based Spatial–Temporal Drought Characteristics Analysis—A Case Study over Turkey. Water 8, 426. doi:10.3390/w8100426
  • Khan, M., Azamathulla, H.M., Tufail, M., 2012. Gene-expression programming to predict pier scour depth using laboratory data. J. Hydroinformatics 14, 628. doi:10.2166/hydro.2011.008
  • Koza, J.R., 1992. Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge, MA.
  • Livneh, B., Kumar, R., Samaniego, L., 2015. Influence of soil textural properties on hydrologic fluxes in the Mississippi river basin. Hydrol. Process. 29, 4638–4655. doi:10.1002/hyp.10601
  • Madadgar, S., Afshar, A., 2009. An Improved Continuous Ant Algorithm for Optimization of Water Resources Problems. Water Resour. Manag. 23, 2119–2139. doi:10.1007/s11269-008-9373-2
  • Madsen, H., 2000. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J. Hydrol. 235, 276–288. doi:10.1016/S0022-1694(00)00279-1
  • Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290. doi:10.1016/0022-1694(70)90255-6
  • Nicolle, P., Pushpalatha, R., Perrin, C., François, D., Thiéry, D., Mathevet, T., Le Lay, M., Besson, F., Soubeyroux, J.-M., Viel, C., Regimbeau, F., Andréassian, V., Maugis, P., Augeard, B., Morice, E., 2014. Benchmarking hydrological models for low-flow simulation and forecasting on French catchments. Hydrol. Earth Syst. Sci. 18, 2829–2857. doi:10.5194/hess-18-2829-2014
  • Nourani, V., Komasi, M., Mano, A., 2009. A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling. Water Resour. Manag. 23, 2877–2894. doi:10.1007/s11269-009-9414-5
  • Pal, I., Towler, E., Livneh, B., 2015. How Can We Better Understand Low River Flows as Climate Changes? Eos (Washington. DC). 96. doi:10.1029/2015EO033875
  • Poli, R., Langdon, W.B., McPhee, N.F., 2008. A Field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, (With contributions by J. R. Koza).
  • Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., Andréassian, V., 2011. A downward structural sensitivity analysis of hydrological models to improve low-flow simulation. J. Hydrol. 411, 66–76. doi:10.1016/j.jhydrol.2011.09.034
  • Pushpalatha, R., Perrin, C., Moine, N. Le, Andréassian, V., 2012. A review of efficiency criteria suitable for evaluating low-flow simulations. J. Hydrol. 420–421, 171–182. doi:10.1016/j.jhydrol.2011.11.055
  • Roushangar, K., Mouaze, D., Shiri, J., 2014. Evaluation of genetic programming-based models for simulating friction factor in alluvial channels. J. Hydrol. 517, 1154–1161. doi:10.1016/j.jhydrol.2014.06.047
  • Samaniego, L., Kumar, R., Attinger, S., 2010. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res. 46, W05523. doi:10.1029/2008WR007327
  • Sattar, A.M.A., Gharabaghi, B., 2015. Gene expression models for prediction of longitudinal dispersion coefficient in streams. J. Hydrol. 524, 587–596. doi:10.1016/j.jhydrol.2015.03.016
  • Searson, D. P., Leahy, D. E., Willis, M.J., 2010. GPTIPS: an open source genetic programming toolbox for multigene symbolic regression, in: In Proceedings of the International Multi Conference of Engineers and Computer Scientists. p. Vol. 1, 77-80.
  • Searson, D., 2015. GPTIPS 2: an open-source software platform for symbolic data mining., in: Al., A.H.G. et (Ed.), Chapter 22 in Handbook of Genetic Programming Applications. Springer, New York, NY.
  • Searson, D., 2009. GPTIPS: Genetic Programming & Symbolic Regression for MATLAB [WWW Document]. URL http://gptips.sourceforge.net.
  • Smakhtin, V.U., 2001. Low flow hydrology: a review. J. Hydrol. 240, 147–186.
  • Tian, Y., Booij, M.J., Xu, Y.-P., 2014. Uncertainty in high and low flows due to model structure and parameter errors. Stoch. Environ. Res. Risk Assess. 28, 319–332. doi:10.1007/s00477-013-0751-9
  • Uyumaz, A., Danandeh Mehr, A., Kahya, E., Erdem, H., 2014. Rectangular side weirs discharge coefficient estimation in circular channels using linear genetic programming approach. J. Hydroinformatics 16, 1318. doi:10.2166/hydro.2014.112
  • Vormoor, K., Lawrence, D., Heistermann, M., Bronstert, A., 2015. Climate change impacts on the seasonality and generation processes of floods – projections and uncertainties for catchments with mixed snowmelt/rainfall regimes. Hydrol. Earth Syst. Sci. 19, 913–931. doi:10.5194/hess-19-913-2015
  • Zhang, X., Booij, M.J., Xu, Y.-P.Y.-P., 2015. Improved simulation of peak flows under climate change: Postprocessing or composite objective calibration? J. Hydrometeorol. 16, 2187–2208. doi:10.1175/JHM-D-14-0218.1
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Ali Danandeh Mehr

Mehmet Cüneyd Demirel

Yayımlanma Tarihi 16 Aralık 2016
Gönderilme Tarihi 25 Temmuz 2016
Kabul Tarihi 27 Kasım 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 21 Sayı: 2

Kaynak Göster

APA Danandeh Mehr, A., & Demirel, M. C. (2016). MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 21(2), 365-376. https://doi.org/10.17482/uumfd.278107
AMA Danandeh Mehr A, Demirel MC. MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ. UUJFE. Kasım 2016;21(2):365-376. doi:10.17482/uumfd.278107
Chicago Danandeh Mehr, Ali, ve Mehmet Cüneyd Demirel. “MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 21, sy. 2 (Kasım 2016): 365-76. https://doi.org/10.17482/uumfd.278107.
EndNote Danandeh Mehr A, Demirel MC (01 Kasım 2016) MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 21 2 365–376.
IEEE A. Danandeh Mehr ve M. C. Demirel, “MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ”, UUJFE, c. 21, sy. 2, ss. 365–376, 2016, doi: 10.17482/uumfd.278107.
ISNAD Danandeh Mehr, Ali - Demirel, Mehmet Cüneyd. “MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 21/2 (Kasım 2016), 365-376. https://doi.org/10.17482/uumfd.278107.
JAMA Danandeh Mehr A, Demirel MC. MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ. UUJFE. 2016;21:365–376.
MLA Danandeh Mehr, Ali ve Mehmet Cüneyd Demirel. “MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 21, sy. 2, 2016, ss. 365-76, doi:10.17482/uumfd.278107.
Vancouver Danandeh Mehr A, Demirel MC. MOSELLE NEHRİ’NDEKİ DÜŞÜK DEBİLERİN BENZETİMİ İÇİN ÇOKLU GENETİK PROGRAMLAMA MODELİNİN KALİBRE EDİLMESİ. UUJFE. 2016;21(2):365-76.

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https://doi.org/10.1007/s13762-018-1674-2

DUYURU:

30.03.2021- Nisan 2021 (26/1) sayımızdan itibaren TR-Dizin yeni kuralları gereği, dergimizde basılacak makalelerde, ilk gönderim aşamasında Telif Hakkı Formu yanısıra, Çıkar Çatışması Bildirim Formu ve Yazar Katkısı Bildirim Formu da tüm yazarlarca imzalanarak gönderilmelidir. Yayınlanacak makalelerde de makale metni içinde "Çıkar Çatışması" ve "Yazar Katkısı" bölümleri yer alacaktır. İlk gönderim aşamasında doldurulması gereken yeni formlara "Yazım Kuralları" ve "Makale Gönderim Süreci" sayfalarımızdan ulaşılabilir. (Değerlendirme süreci bu tarihten önce tamamlanıp basımı bekleyen makalelerin yanısıra değerlendirme süreci devam eden makaleler için, yazarlar tarafından ilgili formlar doldurularak sisteme yüklenmelidir).  Makale şablonları da, bu değişiklik doğrultusunda güncellenmiştir. Tüm yazarlarımıza önemle duyurulur.

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