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    A CMIP6-ensemble-based evaluation of precipitation and temperature projections
    (Springer Wien, 2024) Yilmaz, Banu; Aras, Egemen; Nacar, Sinan
    Understanding climate change's effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Alt & imath;nkaya Dam Basin, which holds significant potential for hydroelectric power generation in T & uuml;rkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 - 4.5 and SSP5 - 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980-2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025-2054), mid-far (2055-2084), and far (2085-2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 degrees C and 5.64 degrees C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.
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    Biochemical oxygen demand prediction in wastewater treatment plant by using different regression analysis models
    (Desalination Publ, 2019) Baki, Osman Tugrul; Aras, Egemen; Akdemir, Ummukulsum Ozel; Yilmaz, Banu
    The management and operation of the wastewater treatment plants (WWTP) have an important role in the controlling and monitoring of the plants' operations. Various performance data are taken into account in the controlling of the WWTP. The irregularities between operating parameters often lead to management problems that cannot be overcome. The aim of this study is to provide a simple and reliable prediction model to estimate the biochemical oxygen demand (BOD) with specific water quality parameters like wastewater temperature, pH, chemical oxygen demand, suspended sediment, total nitrogen, total phosphorus, electrical conductivity, and input discharge. The data records in this study were measured between June 2015 and May 2016 and obtained from the laboratory of Antalya Hurma WWTP. In the creation of the model, classical regression analysis, multivariate adaptive regression splines (MARS), artificial bee colony, and teaching-learning based optimization were used. The root mean square error and the mean absolute error were used to evaluate performance criteria for each model. When the results of the analyses were compared with each other, it was observed that the MARS method gave better estimation results than the other methods used in the study. As a result, it was evinced that the MARS method produces acceptable results in the BOD estimation.
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    Prediction of hydroelectric power generation with machine learning and innovative combined deep learning techniques
    (Springer, 2026) Yilmaz, Banu; Aras, Egemen; Samadianfard, Saeed
    Dams provide energy production by the accumulation and storage of water. Therefore, changes in weather conditions directly affect production capacity and energy efficiency. While the amount of precipitation determines the circulation capacity of water resources, temperature affects the evaporation rate of water and thus water levels. Flow is one of the critical parameters required to determine the amount of water needed for energy production and to ensure efficient energy production. Within the scope of this study, energy production forecasting models have been established for the Alt & imath;nkaya Dam Basin, which has significant potential for hydroelectric energy production in Turkey. In addition to long-short-term memory (LSTM) and feed-forward neural network (FFNN) methods, TPAFFNN-LSTM, which combines these methods with an innovative temporal pattern attention (TPA) mechanism, was also used. Random forest (RF) and extreme gradient boosting (XGB) are also used to evaluate the efficiency and accuracy of the proposed models. As a feature selection method, LASSO regression was applied before the analyses. Shapley Additive Explanations (SHAP) and Regression Receiver Operating Characteristic (RROC) analyses were used in the evaluation phase of all models. According to the results obtained, the nRMSE and NSE criteria of the TPAFFNN-LSTM method were obtained as 0.16 and 0.69, respectively. These results were found to be 18% and 19% more successful than the other methods. The proposed method represents a significant advancement in hydropower energy generation forecasting, providing a robust framework that combines depth of analysis with clarity of insights.
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    Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches
    (Springer International Publishing Ag, 2019) Yilmaz, Banu; Aras, Egemen; Kankal, Murat; Nacar, Sinan
    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, Inanli and Altinsu, in Coruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were used as input parameters, only SSL data were used as output parameters for all models. The successes of the ANN-ABC and ANN-TLBO models that were developed in the research were contrasted with performance of conventional ANN model trained by BP (back-propagation). In addition to these algorithms, linear regression method was applied and compared with others. Root-mean-square and mean absolute error were used as success assessing criteria for model accuracy. When the overall situation is evaluated according to errors of the testing datasets, it was found that ANN-ABC and ANN-TLBO algorithms are more outstanding than conventional ANN model trained by BP.