Sürdürülebilir Kalkınma Amaçları

• 1
• 1
• 1

• MakaleAçık Erişim2025/r/munzur/6146
  Enhancing water quality prediction with artificial intelligence: A hybrid convlstm model for spatio-temporal analysis

  Utku, Anıl; Kutlu, Banu; Utku, Esen Damla Balo

  Water quality plays a vital role in public health, environmental sustainability and ecosystem balance. However, industrialization, urbanization, and agricultural activities cause water pollution to increase, threatening both human health and aquatic ecosystems. Traditional water quality monitoring methods are usually time-consuming, costly, and require manual intervention. Therefore, developing automatic, data-driven, and high-accuracy prediction models is crucial for sustainable water management. This study created a hybrid ConvLSTM model to increase prediction accuracy. The created model was comparatively analyzed with RF, SVR, XGBoost, MLP, CNN, and LSTM. The dataset used includes historical measurement values of chemical pollutants. Input data includes geographical coordinates of sample points, chemical parameter type, and compliance with regulatory standards. Experimental results show that ConvLSTM provides the lowest prediction errors by learning spatial and temporal dependencies and reaches the highest accuracy rate with 0.994 R2 compared to other models.

  İncele
• MakaleAçık Erişim2023/r/munzur/6391
  Determination and monitoring of tetracycline and degradation products in livestock slaughterhouse wastewater treatment plant effluent

  Topal, Murat; Topal, E.Işıl Arslan; Öbek, Erdal

  Aim of our study was the determination and monitoring of tetracycline (TC) and degradation products (DEP) in livestock slaughterhouse wastewater treatment plant (SWWTP) effluent. For this purpose, TC and DEP values in SWWTP were investigated. The concentrations of TC and DEP were monitored for 12 months. TC, 4-epitetracycline (ETC), 4-epianhydrotetracycline (EATC), anhydrotetracycline (ATC), and physicochemical parameters of pH, suspended solids (SS), BOD5, COD, and TP were calculated. The maximum TC concentration was determined as 1.68±0.08 µg/L in March and the minimum TC was 1.08±0.05 µg/L in January. The maximum ETC was 2.93±0.14 µg/L in March and April. The minimum ETC was 1.98±0.1 µg/L in January. EATC was 10.82±0.5 µg/L in September, and minimum EATC value was determined as 9.14±0.4 µg/L in March. The maximum ATC value was 8.62±0.4 µg/L in June and the lowest ATC value was 6.61±0.3 µg/L in September. Concentrations of TC and DEP detected in SWWTP effluent were listed in descending order as EATC> ATC> ETC> TC.

  • 
  • 
  • 
  AtıfOpenAlex1

  İncele
• MakaleAçık Erişim2021/r/munzur/5877
  Detection of circuit components on hand-drawn circuit images by using fasterR-CNN method

  Köseoğlu, Murat; Günay, Mıhrıban

  In this study, one of deep learning methods, which has been very popular in recent years, is employed for the detection and classification of circuit components in hand-drawn circuit images. Each circuit component located in different positions on the scanned images of hand-drawn circuits, which are frequently used in electrical and electronics engineering, is considered as a separate object. In order to detect the components on the circuit image, Faster Region Based Convolutional Neural Network (R-CNN) method is used instead of conventional methods. With the Faster R-CNN method, which has been developed in recent years to detect and classify objects, preprocessing on image data is minimized, and the feature extraction phase is done automatically. In the study, it is aimed to detect and classify four different circuit components in the scanned images of hand-drawn circuits with high accuracy by using the Python programming language on the Google Colab platform. The circuit components to be detected on the hand-drawn circuits are specified as resistor, inductor, capacitor, and voltage source. For the training of the model used, a data set was created by collecting 800 circuit images consisting of hand drawings of different people. For the detection of the components, the pretrained Faster R-CNN Inception V2 model was used after fine tuning and arrangements depending on the process requirements. The model was trained in 50000 epochs, and the success of the trained model has been tested on the circuits drawn in different styles on the paper. The trained model was able to detect circuit components quickly and with a high rate of performance. In addition, the loss graphics of the model were examined. The proposed method shows its efficiency by quickly detecting each of the 4 different circuit components on the image and classifying them with high performance.

  AtıfOpenAlex8·TR Dizin1

  İncele