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Mehmet Melih Koşucu

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Letter to the editor
Published: 02 March 2021 in Environmental Science and Pollution Research
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ACS Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu. Letter to the Editor “Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran”. Environmental Science and Pollution Research 2021, 1 -3.

AMA Style

Eyyup Ensar Başakın, Ömer Ekmekcioğlu. Letter to the Editor “Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran”. Environmental Science and Pollution Research. 2021; ():1-3.

Chicago/Turabian Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu. 2021. "Letter to the Editor “Estimation of global solar radiation data based on satellite-derived atmospheric parameters over the urban area of Mashhad, Iran”." Environmental Science and Pollution Research , no. : 1-3.

Journal article
Published: 05 November 2020 in Computers and Electronics in Agriculture
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Drought is a major area of interest within the field of water resources management, agriculture, energy resources and community health. Recently researchers have examined not only the mathematical expression of drought indices but also statistical predictions. Accordingly, high accuracy results were obtained using stand-alone machine learning techniques such as artificial neural networks (ANN) and support vector machine (SVM). However, lately, hybrid models have been introduced, which are created by integrating different time series decomposition techniques into standalone models, since the accuracy of stand-alone models used in the drought prediction being low particularly for mid-term and long-term drought predictions. In this study, self-calibrated Palmer Drought Severity Index (sc-PDSI) values were predicted by using three different standalone models and six hybrid models which are performed by two different decomposition techniques, such as Empirical mode decomposition (EMD) and Wavelet decomposition (WD). The main purpose of this study is to evaluate the effect of using EMD and WD for decomposing time series into their sub-bands on drought prediction. sc-PDSI time series were used to achieve 1, 3 and 6-month lead time predictions for Adana and Antalya cities located in the southern part of Turkey. Model performance indicators such as mean square error (MSE), Nash-Sutcliffe efficiency coefficient (NSE) and determination coefficient (R2) were employed to compare the proposed models. The results revealed that the accuracy of the stand-alone models, particularly in mid-term sc-PDSI predictions, was unsatisfactory. However, the prediction accuracy has been increased significantly with the introduction of EMD and WD techniques. Considering the Adana region, the hybrid wavelet models outperformed the models hybridized by EMD for not only 1-month lead time (NSEWD-ANFIS = 0.981 and NSEEMD-M5 = 0.890), but also for 3-month (NSEWD-SVM = 0.878 and NSEEMD-ANFIS = 0.811) and 6-month (NSEWD-ANFIS = 0.857 and NSEEMD-ANFIS = 0.783) lead times. According to the obtained results for Antalya region, similar findings were also observed among hybrid models. Thus, it is concluded that the predictions made using WD have higher accuracy than EMD, and the correct wavelet type selection has a significant effect on the results.

ACS Style

Mehmet Özger; Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Volkan Hacısüleyman. Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction. Computers and Electronics in Agriculture 2020, 179, 105851 .

AMA Style

Mehmet Özger, Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Volkan Hacısüleyman. Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction. Computers and Electronics in Agriculture. 2020; 179 ():105851.

Chicago/Turabian Style

Mehmet Özger; Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Volkan Hacısüleyman. 2020. "Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction." Computers and Electronics in Agriculture 179, no. : 105851.

Original article
Published: 21 October 2020 in Modeling Earth Systems and Environment
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Drought is one of the most significant natural disaster and prediction of drought is a key aspect in effective management of water resources and reducing the effect of a drought with preliminary studies plays significant role. In this study, we predicted one of the meteorological drought indices, the self-calibrated Palmer Drought Severity Index (sc-PDSI), values for Adana, Turkey. First, we used adaptive neuro fuzzy inference system (ANFIS) as a standalone technique to predict sc-PDSI. Second, we used empirical mode decomposition (EMD) as a pre-processing technique to decompose the sc-PDSI time series into the sub-series and applied ANFIS to each sub-series. Following the prediction, results are summed each other and final prediction of the hybrid EMD-ANFIS method is obtained. Within the scope of the study, 1, 3and 6-months lead time sc-PDSI values are predicted. We utilized the mean square error (MSE) and Nash–Sutcliffe efficiency coefficient (NSE) as performance indicators in order to perform statistical evaluation. For ANFIS, we obtained NSE = 0.52 and NSE = 0.17 for 3-month and 6-month lead times, respectively. Also, NSE values are obtained as 0.81 and 0.77 for the hybrid model in 3-month and 6-month lead time predictions, respectively. The results revealed that the hybrid EMD-ANFIS model outperforms the standalone ANFIS model. Also, the predicted and actual sc-PDSI series investigated according to the statistical distributions. At last, error histograms of both predicted and actual series are compared according to the Kolmogorov–Smirnov test and the p values are calculated. The results illustrated the predictions are statistically significant.

ACS Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Mehmet Özger. Drought prediction using hybrid soft-computing methods for semi-arid region. Modeling Earth Systems and Environment 2020, 1 -9.

AMA Style

Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Mehmet Özger. Drought prediction using hybrid soft-computing methods for semi-arid region. Modeling Earth Systems and Environment. 2020; ():1-9.

Chicago/Turabian Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Mehmet Özger. 2020. "Drought prediction using hybrid soft-computing methods for semi-arid region." Modeling Earth Systems and Environment , no. : 1-9.

Letter to the editor
Published: 12 April 2020 in Environmental Science and Pollution Research
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The discussers wish to thank the authors of the original paper for investigating the comparing accuracy of artificial intelligence techniques trained to predict chlorophyll-a in US lakes. In the original paper (Luo et al., Environ Sci Pollut Res 26: 30524-30532, 2019), four data-driven models were established to estimate the chlorophyll-a (CHLA) values in natural and man-made lakes. Three of these models are adaptive neuro-fuzzy inference system (ANFIS)-based, while one is (artificial neural network) ANN-based. The authors used total phosphorus (TP), total nitrogen (TN), turbidity (TB), and the Secchi depth (SD) as independent variables in order to predict CHLA. They stated that ANFIS with subtractive clustering method (ANFIS_SC) models and multilayer perceptron neural network (MLPNN) models gives higher accuracy in the prediction of CHLA values for natural lakes and man-made lakes, respectively. In this letter, some of the missing points in the original publication, which is important for the estimation and comparison of CHLA values in two different lake sets that differ according to the type of formation, are highlighted. In addition, several points are mentioned in order to make these points more clarified for potential readers.

ACS Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Babak Mohammadi. Letter to the editor “comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes”. Environmental Science and Pollution Research 2020, 27, 22131 -22134.

AMA Style

Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Babak Mohammadi. Letter to the editor “comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes”. Environmental Science and Pollution Research. 2020; 27 (17):22131-22134.

Chicago/Turabian Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Babak Mohammadi. 2020. "Letter to the editor “comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes”." Environmental Science and Pollution Research 27, no. 17: 22131-22134.

Commentary
Published: 18 February 2020 in Natural Resources Research
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We thank Zhang et al. (Nat Resour Res, 2019. https://doi.org/10.1007/s11053-019-09512-6) for investigating the accuracy of artificial intelligence techniques in the prediction of drought in China. In the paper by Zhang et al. (2019), two data-driven models, namely artificial neural network and support vector machine, and autoregressive integrated moving average (ARIMA) model were established to estimate standardized precipitation evapotranspiration index (SPEI) values. In that paper, temperature and precipitation values were used as independent variables to predict SPEI. They stated that ARIMA models give higher accuracy in the prediction of SPEI values. Here, not only some of the missing points and deficiencies in the original publication, but also improvements that can be made in future studies, were mentioned. In addition, several points are introduced in order to make these points more clarified for potential readers.

ACS Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu. Comment on “Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China” by Yuhu Zhang, Huirong Yang, Hengjian Cui, and Qiuhua Chen, in Natural Resources Research DOI: 10.1007/s11053-019-09512-6. Natural Resources Research 2020, 29, 1465 -1467.

AMA Style

Eyyup Ensar Başakın, Ömer Ekmekcioğlu. Comment on “Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China” by Yuhu Zhang, Huirong Yang, Hengjian Cui, and Qiuhua Chen, in Natural Resources Research DOI: 10.1007/s11053-019-09512-6. Natural Resources Research. 2020; 29 (2):1465-1467.

Chicago/Turabian Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu. 2020. "Comment on “Comparison of the Ability of ARIMA, WNN and SVM Models for Drought Forecasting in the Sanjiang Plain, China” by Yuhu Zhang, Huirong Yang, Hengjian Cui, and Qiuhua Chen, in Natural Resources Research DOI: 10.1007/s11053-019-09512-6." Natural Resources Research 29, no. 2: 1465-1467.

Article
Published: 06 October 2019 in Water
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Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

ACS Style

Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water 2019, 11, 2083 .

AMA Style

Mehmet Cüneyd Demirel, Alparslan Özen, Selen Orta, Emir Toker, Hatice Kübra Demir, Ömer Ekmekcioğlu, Hüsamettin Tayşi, Sinan Eruçar, Ahmet Bilal Sağ, Ömer Sarı, Ecem Tuncer, Hayrettin Hancı, Türkan Irem Özcan, Hilal Erdem, Mehmet Melih Koşucu, Eyyup Ensar Başakın, Kamal Ahmed, Awat Anwar, Muhammet Bahattin Avcuoğlu, Ömer Vanlı, Simon Stisen, Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water. 2019; 11 (10):2083.

Chicago/Turabian Style

Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. 2019. "Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration." Water 11, no. 10: 2083.

Journal article
Published: 01 January 2019 in Pamukkale University Journal of Engineering Sciences
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ACS Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Mehmet Ozger. Drought Analysis with Machine Learning Methods. Pamukkale University Journal of Engineering Sciences 2019, 25, 985 -991.

AMA Style

Eyyup Ensar Başakın, Ömer Ekmekcioğlu, Mehmet Ozger. Drought Analysis with Machine Learning Methods. Pamukkale University Journal of Engineering Sciences. 2019; 25 (8):985-991.

Chicago/Turabian Style

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Mehmet Ozger. 2019. "Drought Analysis with Machine Learning Methods." Pamukkale University Journal of Engineering Sciences 25, no. 8: 985-991.

Journal article
Published: 07 September 2018 in Journal of Polytechnic
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ACS Style

Eyyup Ensar Başakın; Mehmet Özger. Gri Tahmin Yöntemi İle İstanbul Su Tüketiminin Modellenmesi. Journal of Polytechnic 2018, 1 .

AMA Style

Eyyup Ensar Başakın, Mehmet Özger. Gri Tahmin Yöntemi İle İstanbul Su Tüketiminin Modellenmesi. Journal of Polytechnic. 2018; ():1.

Chicago/Turabian Style

Eyyup Ensar Başakın; Mehmet Özger. 2018. "Gri Tahmin Yöntemi İle İstanbul Su Tüketiminin Modellenmesi." Journal of Polytechnic , no. : 1.