Sürdürülebilir Kalkınma Amaçları
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- ArticleYalnız künye2023Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows
Katipoğlu, Okan Mert; Yeşilyurt, Sefa Nur; Dalkılıç, Hüseyin Yıldırım; Akar, Funda
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42 years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values were selected as inputs according to partial autocorrelation values in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Modeling of meteorological, agricultural, and hydrological droughts in semi-arid environments with various machine learning and discrete wavelet transform
Achite, Mohammed; Katipoglu, Okan Mert; Şenocak, Serkan; Elshaboury, Nehal; Bazrafshan, Ommolbanin; Dalkılıç, Hüseyin Yıldırım
Recent meteorological, hydrological, and agricultural droughts in the Mediterranean regions have raised concerns about the impact of climate change. In this study, the meteorological, agricultural, and hydrological droughts were modeled in the Wadi Ouahrane Basin using various machine learning (ML) models and standardized indices of rainfall, evapotranspiration, and runoff (SPI, SPEI, and SRI) at different times scales (1, 3, 6, 9, 12, and 24 months). The applied ML models were the linear support vector machine (SVM), quadratic SVM, cubic SVM, fine gaussian SVM, medium gaussian SVM, coarse Gaussian SVM, rational quadratic Gaussian process regression (GPR), squared exponential GPR, Matern 5/2 GPR, exponential GPR, bagged tree, and boosted tree. Moreover, the hybrid models acquired by combining these ML models with wavelet transform were evaluated. The performance of the models was analyzed using statistical criteria such as root mean square error, determination coefficient, and mean absolute error. As a result, wavelet-GPR models showed the most promising results in estimating SPI, SPEI, and SRI values. The values for SPI (R 2 of train: 0.393; and test: 0.351), SPEI (R 2 of train: 0.809; test: 0.746), and SRI (R 2 of train: 0.999; test: 0.808) indicate monthly time scale. Additionally, for the time periods of 3, 6, 9, 12, and 24 months, the predictions for SPI, SPEI, and SRI generally obtain R 2 of train 0.99 and test 0.95 values. Moreover, it was determined that wavelet-based ML models, established with inputs divided into three subcomponents with Daubechies mother wavelet, showed superior results than standalone ML models. The study results could guide decision-makers and planners in developing drought risk management and mitigation strategies.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Yeşilırmak havzasındaki hidrolojik kuraklıkların Mann-Kendall ve Yenilikçi Şen yöntemi ile trend analizi
Dalkılıç, Hüseyin Yıldırım; Katipoğlu, Okan Mert; Yeşilyurt, Sefa Nur
Bu çalışmada Yeşilırmak havzasında bulunan 7 adet akım gözlem istasyonuna ait aylık ve yıllık zaman periyodundaki akım kuraklık indekslerinin (AKİ) trend analizi yapılmıştır. Kuraklık trendlerinin ortaya çıkarılmasında Mann-Kendall (MK), Yenilikçi Şen Yöntemi (YŞY) ve Thiel Şen Yaklaşımı (TŞY) kullanılmıştır. İçsel bağımlılığı bulunan AKİ serilerine MK testi uygulanmadan önce trend-free pre-whitening (ön arındırma) işlemi ile serisel bağımlılığın etkisi kaldırılmıştır. Trend analizleri istatistiksel açıdan %1 ve %5 anlamlılık seviyelerine göre değerlendirilmiştir. Sonuç olarak hidrolojik kuraklık trendlerinin değişimini istenilen aralıkta grafiksel olarak göstermesi ve daha hassas olarak trendleri ifade edebilmesi açısından YŞY’nin MK testine göre üstün olduğu belirlenmiştir. Ayrıca çalışma alanında aylık ve yıllık zaman periyotlarında artan hidrolojik kuraklık trendlerinin hâkim olduğu tespit edilmiştir. Bu nedenle kuraklık risklerinin yönetilmesi, kuraklığa karşı alınabilecek önlemler ve erken uyarı sistemleri gibi aksiyonların bütünleşik bir şekilde uygulanması gerekmektedir.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Daily Flow Modeling With Random Forest and K-Nearest Neighbor Methods
Dalkılıç, Hüseyin Yıldırım; Yeşilyurt, Sefa Nur; Samui, Pijush
Water is an indispensable natural resource for life. Therefore, protection and control of water resources are of great importance. Since river flow estimation and modeling are very important in cases such as the management of water resources, irrigation, it is included in the literature as an issue that needs constant research and development. A large number of techniques are being used for estimation and modeling; thus, the estimation results are gradually improving with the development of the studies carried out, the comparison of techniques, and the determination and removal of the shortcomings. In this study, Random Forest and K-Nearest Neighbors nonlinear regression models, which are two of the machine learning methods, were used to evaluating the estimation results, to find the better estimation method, and to determine the advantages and disadvantages of these methods. In addition, Random Search and Grid Search methods were used to make the hyperparameter selection and comparison for the Random Forest model. In this study, in which daily flow data of 1981-2011 of the two stations in the Euphrates were used, and, when compared to other models, it was observed that better results were obtained when Random Search was applied to determine the hyperparameters of the Random Forest model.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2019Yağışların Trend Analizi
Dalkılıç, Hüseyin Yıldırım
Bu çalışmada Gümüşhane, Erzincan, Bayburt gibi iklimsel olarak birbirine yakın olan bölgeler çalışma alanıolarak seçilmiştir. Son zamanlarda yoğun su kullanımının artması ve bunun beraberinde su kaynaklarının sınırlıolması, kaynakların optimum şekilde kullanılması gerektiğini ortaya koymuştur. Yağış yapısının birbirineparalel olduğu bu şehirlerde 1978-2018 yılları arasındaki 3 istasyondan alınmış veriler ile bölgedeki yağıştrendlerinin tespiti amaçlanmıştır. Trend varlığını belirlemede Şen eğilim testi, Spearman Rho, Mann-Kendallve Mevsimsel Mann-Kendall testleri kullanılmıştır. Bu testler sonucunda yaz döneminde belirli aylardayağışlarda azalış, kış döneminde belirli aylarda ise yağışlarda artış olduğu görülmüştür.
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