Search results for: Sinan Nacar

Items from 1 to 7 out of 7 results

article

Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

Banu Yilmaz, Egemen Aras, Murat Kankal, Sinan Nacar

Acta Geophysica > 2019 > 67 > 6 > 1693-1705

The main aim of the research is to use the artificial neural network (ANN) model with the artificial bee colony (ABC) and teaching–learning-based optimization (TLBO) algorithms for estimating suspended sediment loading. The stream flow per month and SSL data obtained from two stations, İnanlı and Altınsu, in Çoruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were...

article

Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

Banu Yilmaz, Egemen Aras, Murat Kankal, Sinan Nacar

Acta Geophysica > 2019 > Vol. 67, no. 6 > 1693--1705

article

Performance evaluation of multiple adaptive regression splines, teaching–learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood

Sebahattin Tiryaki, Hüseyin Tan, Selahattin Bardak, Murat Kankal, more

European Journal of Wood and Wood Products > 2019 > 77 > 4 > 645-659

Understanding the mechanical behaviour of impregnated wood is crucial in making a preliminary decision on the usability of such woods for structural purposes. In this paper, by considering concentration (1, 3 and 5%), pressure (1, 1.5 and 2 atm.), and time (30, 60, 90 and 120 min), an experimental study was performed, and the mechanical behaviour of impregnated wood was determined as a result of the...

article

Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms

Sinan Nacar, M. Ali Hınıs, Murat Kankal

KSCE Journal of Civil Engineering > 2018 > 22 > 9 > 3676-3685

Streamflow forecasting based on past records is an important issue in both hydrologic engineering and hydropower reservoir management. In the study, three artificial Neural Network (NN) models, namely NN with well-known multi-layer perceptron (MLPNN), NN with principal component analyses (PCA-NN), and NN with time lagged recurrent (TLR-NN), were used to 1, 3, 5, 7, and 14 ahead of daily streamflow...

article

Predicting temporal rate coefficient of bar volume using hybrid artificial intelligence approaches

Murat Kankal, Ergun Uzlu, Sinan Nacar, Ömer Yüksek

Journal of Marine Science and Technology > 2018 > 23 > 3 > 596-604

To project the structures to be built in the coastal zone and to make the best use of the coastal area, the mechanism of sediment transport, including both longshore and cross-shore transport, in this region should be well known. Within this context, temporal change rate of cross-shore sediment transport is of vital importance, especially to predict the erosion quantitatively. In this study, hybrid...

article

Status of hydropower and water resources in the Southeastern Anatolia Project (GAP) of Turkey

Murat Kankal, Sinan Nacar, Ergun Uzlu

Energy Reports > 2016 > 2 > Complete > 123-128

This study focuses on the water resources development of the Southeastern Anatolia Project (GAP) in Turkey. The Euphrates and Tigris Rivers, located in the GAP, have the largest flow volume of 16.9% and 11.4% of Turkey’s average annually runoff (186 billion m³), respectively. While the Euphrates represents over 19.4% of the national gross hydropower potential (433 GWh/year), the Tigris correspond...

article

Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

Ergun Uzlu, Adem Akpınar, Hasan Tahsin Özturk, Sinan Nacar, more

Energy > 2014 > 69 > Complete > 638-647

The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC...

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Search results for: Sinan Nacar

Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

Performance evaluation of multiple adaptive regression splines, teaching–learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood

Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms

Predicting temporal rate coefficient of bar volume using hybrid artificial intelligence approaches

Status of hydropower and water resources in the Southeastern Anatolia Project (GAP) of Turkey

Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

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