Sürdürülebilir Kalkınma Amaçları

• 1
• 1
• 1

• MakaleAçık Erişim2024Scopus SJRQ1/r/bayburt/11911
  Assessment of Different Methods for Estimation of Missing Rainfall Data

  Hırca, Tuğçe; Eryılmaz Türkkan, Gökçen

  Missing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.

  AtıfScopus22·WoS23·OpenAlex27

  İncele
• MakaleAçık Erişim2023Scopus SJRQ1/r/bayburt/11830
  Evaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System

  Bacaksız, Efsun; Opan, Mücahit; Kara Dilek, Zuhal Elif; Karadeniz, Murat

  In a multi-reservoir system, the stochastic nature of basin data resulting from rainfall introduces risk into water management operations. Effective management that accounts for these risks can obtain maximum benefits from the system. This study presents a description of a multi-reservoir water resources system with hydroelectric power plants, utilizing the energy optimization model developed by OPAN in 2007. The model was applied to reservoirs located successively on the Lower Kızılırmak River in the Kızılırmak Basin, with the objective function being the maximization of firm power by using drought period inflows and total energy by using monthly average inflows. The study considered three scenarios: deterministic, probabilistic, and risky (critical cases), with probabilities of inflows from the basin being determined for the latter. Monthly inflows with determined probabilities were used to obtain data for the risky case. Optimum operating levels were determined based on this data to maximize firm power and total energy. According to the operating levels, the reservoir with the largest useful volume manages the operation. The values obtained from the optimization were then used in multivariate regression analysis using the Statistical Package for Social Scientists (SPSS), a statistical analysis program. The analysis explored the effects of monthly operating levels in the reservoirs, the amount of inflow released from the spillway, the amount of inflow released for energy production, the monthly average inflow to the reservoirs, and firm power values on energy production.

  • 
  • 
  • 
  AtıfScopus7·WoS6·OpenAlex7

  İncele