In this study, general knowledge and some details of the floods in Eastern Black Sea Basin of Turkey are presented. Brief hydro-meteorological analysis of selected nine floods and detailed analysis of the greatest flood are given. In the studied area, 51 big floods have taken place between 1955-2005 years, causing 258 deaths and nearly US $500,000,000 of damage. Most of the floods have occurred in June, July and August. It is concluded that especially for the rainstorms that have caused significantly damages, the return periods of the rainfall heights and resultant flood discharges have gone up to 250 and 500 years, respectively. A general agreement is observed between the return periods of rains and resultant floods. It is concluded that there has been no significant climate change to cause increases in flood harms. The most important human factors to increase the damage are determined as wrong and illegal land use, deforestation and wrong urbanization and settlement, psychological and technical factors. Some structural and non-structural measures to mitigate flood damages are also included in the paper. Structural measures include dykes and flood levees. Main non-structural measures include flood warning system, modification of land use, watershed management and improvement, flood insurance, organization of flood management studies, coordination between related institutions and education of the people and informing of the stakeholders.
This paper studies the performance of an artificial neural network (ANN) with teaching-learning-based optimization (TLBO) for modeling electric energy demand (EED) in Turkey. The ANN with TLBO (ANN-TLBO) was compared to the ANN with backpropagation (ANN-BP) and the ANN with artificial bee colony algorithm (ANN-ABC) models. Gross domestic product, population, import, and export were selected as independent variables in the models. The results reveal that the ANN-TLBO models perform better than the ANN-BP and ANN-ABC models in EED estimation. The average root-mean-square error of the ANN-BP and ANN-ABC models was decreased by 42.3 and 39.3 % using the ANN-TLBO model, respectively. Different scenarios have been studied over a projected 6-year period, from 2013 to 2018, to forecast Turkey's EED. The results of the proposed model give excellent clues with regards to its use in future energy studies.
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