Yazar "Yilmaz, Gokcen Bayrak" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    ESTIMATION OF NUTRIENT LOADS IN ERGENE BASIN THROUGH GIS
    (Parlar Scientific Publications (P S P), 2014) Yilmaz, Gokcen Bayrak; Sivri, Nuket
    This study aims to determine the nutrient (total nitrogen (TN), total phosphorus (TP)) loads from point and diffuse sources of the river basin, and the effects of the nutrient loads on Ergene River, being one of the most polluted rivers in Turkey. To this end, Geographic Information System (GIS), was used to digitize land-use of the basin; the spatial values of land-use patterns were used to estimate the load coming from diffuse sources, Exponential Function Method was used to make population predictions and calculate the load change based on population, and Export Coefficient Model was used to estimate nutrient loads coming from diffuse sources of contamination. In the estimation of TN (24000 tons/year) and TP (2700 tons/year) loads, it was found that diffuse sources have a large share (about 86%). The large portions of nutrient load coming from point sources (74% TN, 93% TP) are associated with domestic wastewater, and chemical and natural (animal) fertilizers generate almost the total load (83% TN, 93% TP) coming from diffuse sources. When the effects of nutrient loads on surface waters in Ergene Basin are considered, both in ecological and socio-economical terms, it was identified that intended use of water sources changed; agricultural and aquatic production was negatively affected by deteriorated ecological balance at surface water. It was concluded that ecological sanctions at the basin should have the precedence over the socio-economic sanctions.
  • Küçük Resim Yok
    Öğe
    THE PREDICTION OF FLOW-RATE AND NUTRIENT LOAD IN ERGENE RIVER BASIN THROUGH ARTIFICIAL NEURAL NETWORKS
    (Parlar Scientific Publications (P S P), 2014) Yilmaz, Gokcen Bayrak; Sivri, Nuket; Akgundogdu, Abdurrahim; Seker, Dursun Zafer
    This study aims to predict the highest rate of monthly average flow and load change in Ergene River, one of the most contaminated rivers of Turkey and having a high flood frequency. For this purpose, the Flow Observation Station (FOS) of Luleburgaz district was chosen for modelling as it is located at a point in the middle of the basin, where domestic and industrial wastes of the region with the population density of basin reach and seasonal floods are observed. An artificial neural networks method, the Feed-Forward Back Propagation Neural Networks (FFBPNN), method was used to evaluate the relation among hydro-meteorological data of Luleburgaz FOS recorded for 168 months between 1997 and 2010, and the flow-rate of Ergene River Luleburgaz Station was predicted monthly for the year of 2011. The load change in the river was observed with direct calculation method on the basis of the acquired flow-rate values and long-term nutrient concentration averages.