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Öğe DEVELOPMENT OF MULTISITE STREAMFLOW GENERATION MODELS(PARLAR SCIENTIFIC PUBLICATIONS (P S P), 2016) Arslan, Chelang A.; Buyukyildiz, MeralForecasting of streamflow can have a significant economic impact, as this can help in water management and can be helpful tool to provide protection from water shortages and possible flood damage. In recent work the artificial neural networks different models with different training algorithms were examined to simulate Tigris River using the cross correlation between the flow of different sites or gauge stations. The challenging task in this work was to improve the forecasting models to generate a future series by ANNs models by using input parameters from nearby sites. Therefore the best conventional method to compare and judge the results was selected to be the 1st order autoregressive moving average Matalas which deals with multi variables as input parameters and generate future series for these variables at the same time. The traditional architecture of ANNs models were also a good comparison tools to decide the new multisite ANN models success. It was concluded from this study that consisting nearby sites monthly flow series as input parameters in ANN architecture after investigating the cross correlation between the series's may led to more successful forecasting models. This can also provide a good promise to predict ungauged flow values in some sites which are suffering from missed data by using the flow values from nearby stations.Öğe Estimation of the Change in Lake Water Level by Artificial Intelligence Methods(SPRINGER, 2014) Buyukyildiz, Meral; Tezel, Gulay; Yilmaz, VolkanIn this study, five different artificial intelligence methods, including Artificial Neural Networks based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks (MLP), Radial Basis Neural Networks (RBNN) and Adaptive Network Based Fuzzy Inference System (ANFIS), were used to estimate monthly water level change in Lake Beysehir. By using different input combinations consisting of monthly Inflow - Lost flow (I), Precipitation (P), Evaporation (E) and Outflow (O), efforts were made to estimate the change in water level (L). Performance of models established was evaluated using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R-2). According to the results of models, epsilon-SVR model was obtained as the most successful model to estimate monthly water level of Lake Beysehir.Öğe An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models(SPRINGER, 2017) Buyukyildiz, Meral; Kumcu, Serife YurdagulSediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world"s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R-2).Öğe FACTOR ANALYSIS OF SURFACE WATER QUALITY PARAMETERS FOR THE RIVERS OF TURKEY(PARLAR SCIENTIFIC PUBLICATIONS (P S P), 2015) Yilmaz, Volkan; Buyukyildiz, MeralIn this study, the Factor Analysis, a multivariate statistical analysis, was applied on twelve different parameters [streamflow, temperature, pH, electrical conductivity (EC), Na+, K, (Ca+Mg)(2+), CO32-, Cl-, SO42-, sodium adsorption ratio (SAR) and boron (B) concentrations] measured at 67 streamflow gauging stations of Turkey's 19 water basins in terms of annual averages for a long period of time (1992-2008). First of all, the appropriateness of the data was confirmed using the Kaiser-Meyer-Olkin (KMO) Criteria and Bartlett's Test of Sphericity. As a result of the study, it was observed that 80% of the total variance was described by three factors, i.e. the first, second and third factors described the 50.25%, 19.04% and 10.52% of the total variance, respectively. Moreover, the characters described by each factor were determined and named as environmental effects (in the frame of high ion relation), alkaline and climatic effects. Finally, the score values of the stations were calculated, and the relationships between the stations and the factors were investigated. In this context, the stations giving extreme score values under each factor were considered as they should be carefully examined by the decision-makers.Öğe GLOBAL CLIMATE CHANGE WITH ITS REFLECTIONS ON TURKEY(INT SCIENTIFIC CONFERENCE SGEM, 2009) Buyukyildiz, Meral; Marti, Ali Ihsan; Yilmaz, VolkanThe climate change due to global warming is accepted among the greatest environmental problems resulted from the human effects of increasing energy consumption, urbanization and deforestation events threatening the healthy life of people and environment. Many results of the climate change due to global warming can be estimated as the melting of snow covers, terrestrial and sea icebergs, replacement of climate zones, frequent formation of severe weather events, floods that gain strength in time, severe droughts, formation of deserts, increasing epidemic cases and harmful agricultural insects, etc. all of which are very important events directly or indirectly affecting human life, socioeconomic sectors and ecological systems. Global warming is an effective change perceived by people from equator to poles, and from oceans to the highest lands. Since Turkey has a complex climatic structure, it is among the countries that will be mostly affected from the climate change especially due to global warming. Therefore, determining the dimensions of the effects of the global warming on Turkey's climate will provide to estimate and make projections about the climatic changes in the country, since it is necessary for the future plans of water supply, agriculture, flood control, ecology, etc. activities.Öğe THE HYDRO-ECOLOGIC EFFECTS OF GLOBAL CLIMATE CHANGES ON THE WORLD(INT SCIENTIFIC CONFERENCE SGEM, 2009) Marti, Ali Ihsan; Buyukyildiz, MeralPast to present, climate changes take place on every sections of the world during the history. This is an ordinary fact when these changes occur naturally, i.e. the natural climate changes are the necessity of our world's natural behavior, and then the beings on the world are not affected artificially. However, in recent years, some human activities that increase the concentrations of greenhouse gases (GHGs) in the atmosphere and cause global warming are the most effective factors playing great roles on changing the climates of the world. The objective of this study is to present the hydro-ecologic effects of climate changes due to global warming on the hydrologic characteristics of the world in terms of precipitation, streamflow and evaporation those of which are closely related to each other and considerably affect the ecological balance.Öğe Monthly evaporation forecasting using artificial neural networks and support vector machines(SPRINGER WIEN, 2016) Tezel, Gulay; Buyukyildiz, MeralEvaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and epsilon-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, epsilon-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R (2)). According to the performance criteria, the ANN algorithms and epsilon-SVR had similar results. The ANNs and epsilon-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R (2) = 0.905.Öğe Utilization of PSO algorithm in estimation of water level change of Lake Beysehir(SPRINGER WIEN, 2017) Buyukyildiz, Meral; Tezel, GulayIn this study, unlike backpropagation algorithm which gets local best solutions, the usefulness of particle swarm optimization (PSO) algorithm, a population-based optimization technique with a global search feature, inspired by the behavior of bird flocks, in determination of parameters of support vector machines (SVM) and adaptive network-based fuzzy inference system (ANFIS) methods was investigated. For this purpose, the performances of hybrid PSO-epsilon support vector regression (PSO-epsilon SVR) and PSO-ANFIS models were studied to estimate water level change of Lake Beysehir in Turkey. The change in water level was also estimated using generalized regression neural network (GRNN) method, an iterative training procedure. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R (2)) were used to compare the obtained results. Efforts were made to estimate water level change (L) using different input combinations of monthly inflow-lost flow (I), precipitation (P), evaporation (E), and outflow (O). According to the obtained results, the other methods except PSO-ANN generally showed significantly similar performances to each other. PSO-epsilon SVR method with the values of minMAE = 0.0052 m, maxMAE = 0.04 m, and medianMAE = 0.0198 m; minRMSE = 0.0070 m, maxRMSE = 0.0518 m, and medianRMSE = 0.0241 m; minR (2) = 0.9169, maxR (2) = 0.9995, medianR (2) = 0.9909 for the I-P-E-O combination in testing period became superior in forecasting water level change of Lake Beysehir than the other methods. PSO-ANN models were the least successful models in all combinations.
