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In this research, monthly wind speed time series of the Kirsehir was investigated using the stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process regression, support vector machines and multivariate adaptive regression splines were employed as stand-alone machine learning models, while the discrete wavelet transform was utilized as a pre-processing technique to create hybrid models. Moreover, for the first time in wind speed predictions, we generated a multi-stage ensemble model by using the M5 Model Tree (M5) algorithm to increase the model accuracies. Two major tasks considered to be necessary, in which the first is to obtain the lag times by using autocorrelation functions, and the latter is to determine the optimum mother wavelet as well as the decomposition level to reduce the uncertainties in wavelet modeling. The results revealed that the hybrid wavelet models outperformed the stand-alone models, while a significant improvement was also observed in M5 ensemble models as the highest Nash–Sutcliffe efficiency coefficient values were obtained in M5 hybrid wavelet multi-stage ensemble models for each lead time prediction. The findings of the study were assessed with respect to the various performance indicators and Kruskal–Wallis test to indicate whether the results are statically significant. The proposed multi-stage ensemble framework also benchmarked with the classical tree-based ensembles, such as Random forest, AdaBoost and XGBoost.
Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Hatice Çıtakoğlu; Mehmet Özger. A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Computing and Applications 2021, 1 -30.
AMA StyleEyyup Ensar Başakın, Ömer Ekmekcioğlu, Hatice Çıtakoğlu, Mehmet Özger. A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Computing and Applications. 2021; ():1-30.
Chicago/Turabian StyleEyyup Ensar Başakın; Ömer Ekmekcioğlu; Hatice Çıtakoğlu; Mehmet Özger. 2021. "A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment." Neural Computing and Applications , no. : 1-30.
The construction industry is among the riskiest industries around the world. Hence, the preliminary studies exploring the consequences of occupational accidents have received considerable attention in research society. This study aims to develop a comprehensive framework to predict the post-accident disability status of construction workers. The dataset comprising 47,938 construction accidents recorded in Turkey was subjected to a detailed multi-step feature engineering approach, including data encoding, data scaling, dimension reduction, and data resampling. Predictions were performed through four tree-based ensemble machine learning models: Random Forest, XGBoost, AdaBoost, and Extra Trees, as well as a state-of-the-art optimization method for hyperparameter tuning, Genetic Algorithm (GA). GA-XGBoost presented the highest prediction rate with 0.8292 in terms of accuracy and 0.8120 with respect to AUROC. The findings may aid in predicting construction workers' post-accident disability status, resulting in a safer working environment and productivity planning in construction projects.
Kerim Koc; Ömer Ekmekcioğlu; Asli Pelin Gurgun. Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers. Automation in Construction 2021, 131, 103896 .
AMA StyleKerim Koc, Ömer Ekmekcioğlu, Asli Pelin Gurgun. Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers. Automation in Construction. 2021; 131 ():103896.
Chicago/Turabian StyleKerim Koc; Ömer Ekmekcioğlu; Asli Pelin Gurgun. 2021. "Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers." Automation in Construction 131, no. : 103896.
As an initial exploration, preliminary studies which are conducted for the identification of key points to be addressed in flood risk management (FRM) processes are crucial to mitigate potential impacts of floods. Generating the flood risk maps with the participation of diverse stakeholders at each level of administration is essential to develop effective FRM strategies. Hence, the objectives of this study are twofold: i) to produce district-based vulnerability, hazard, and flood risk maps for Istanbul with a hybrid fuzzy AHP-TOPSIS model, ii) to generate these maps by considering the perceptions of different stakeholders separately, which is an initial attempt in the literature, ensuring the comparative analysis of stakeholder perceptions in FRM. Local and metropolitan municipalities, disaster management and coordination centres, water and sewerage administrations, and universities were considered as the leading stakeholders since they are chiefly responsible decision-making bodies in FRM practices in Istanbul. Pearson's correlation coefficient and Spearman's rank correlation coefficient tests were used to obtain a more accurate understanding of the agreement levels between stakeholders. The results revealed the need for the involvement of various stakeholders to generate flood risk maps since significantly different perspectives were observed; and the need for changing the generated flood risk maps. The findings of this study are critical because generated maps show distinct differences according to the mentality of the organizations and experts, which inevitably change the most flood-prone areas and possible mitigation investments.
Ömer Ekmekcioğlu; Kerim Koc; Mehmet Özger. Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey. International Journal of Disaster Risk Reduction 2021, 60, 102327 .
AMA StyleÖmer Ekmekcioğlu, Kerim Koc, Mehmet Özger. Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey. International Journal of Disaster Risk Reduction. 2021; 60 ():102327.
Chicago/Turabian StyleÖmer Ekmekcioğlu; Kerim Koc; Mehmet Özger. 2021. "Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey." International Journal of Disaster Risk Reduction 60, no. : 102327.
In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.
Ömer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. 2021, 1 .
AMA StyleÖmer EkmekcioğluiD, Mehmet Cüneyd DemireliD, Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. . 2021; ():1.
Chicago/Turabian StyleÖmer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. 2021. "Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution." , no. : 1.
In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.
Ömer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. 2021, 1 .
AMA StyleÖmer EkmekcioğluiD, Mehmet Cüneyd DemireliD, Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. . 2021; ():1.
Chicago/Turabian StyleÖmer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. 2021. "Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution." , no. : 1.
Floods, among the most frequent and severe hazards in the world, threaten the sustainability of the built environment by causing immense damage to infrastructures, buildings, economies, social activities and beyond all, cause loss of lives. Istanbul is the most densely populated industrial, commercial and cultural center of Turkey. Besides, the population of Istanbul has increased over the last decade since the city attracts immigrants from all over Turkey, along with other countries. Therefore, it is vital to prioritize the districts of Istanbul by determining flood risk mitigation strategies since flood risk management is carried out at district level units in local municipalities in Istanbul. In this study, a new hierarchical procedure that consists of thirteen flood vulnerability and hazard criteria is proposed for the generation of Istanbul’s district-based flood risk map. To obtain the criteria weights the fuzzy analytical hierarchy process (AHP) was adopted. The sensitivity analysis conducted in this study reveals the stability and robustness of the proposed fuzzy AHP model. Among all the criteria, land use and the return period of a storm event were found as the most significant criteria for vulnerability and hazard clusters, respectively. Criteria weights calculated through the fuzzy AHP method were integrated with the data taken from various institutions with respect to each district to calculate risk scores of the districts. Consequently, district risk scores were used to generate a flood risk map of Istanbul. The findings show that high-risk districts are mainly at the center and highly populated areas of the city. Moreover, the accuracy of the proposed approach was validated through observations of the significant flood events experienced in the last two decades. Thus, the fuzzy AHP method can be considered as advantageous to make a quick and regional flood risk assessment. In addition, the proposed approach is useful to mitigate flood risk along with allocating a fair budget to the local municipalities for flood risk mitigation measures. The findings of this research could also provide useful procedures for professionals of the water resources and local authorities.
Ömer Ekmekcioğlu; Kerim Koc; Mehmet Özger. District based flood risk assessment in Istanbul using fuzzy analytical hierarchy process. Stochastic Environmental Research and Risk Assessment 2020, 35, 617 -637.
AMA StyleÖmer Ekmekcioğlu, Kerim Koc, Mehmet Özger. District based flood risk assessment in Istanbul using fuzzy analytical hierarchy process. Stochastic Environmental Research and Risk Assessment. 2020; 35 (3):617-637.
Chicago/Turabian StyleÖmer Ekmekcioğlu; Kerim Koc; Mehmet Özger. 2020. "District based flood risk assessment in Istanbul using fuzzy analytical hierarchy process." Stochastic Environmental Research and Risk Assessment 35, no. 3: 617-637.
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.
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 StyleEyyup 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 StyleEyyup 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.
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.
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 StyleEyyup 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 StyleEyyup 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.
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.
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 StyleEyyup 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 StyleEyyup 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.
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.
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 StyleMehmet 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 StyleMehmet 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.