Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025Artificial Intelligence Applications in the Aquaculture Industry: Sustainability, Efficiency and Innovative Solutions
Kutlu, Banu; Utku, Esen Damla Balo
Artificial Intelligence (AI) is becoming increasingly important to increase efficiency and sustainability by organizing processes and providing significant improvements in different sectors. AI is reshaping the aquaculture sector. AI is being used to help solve optimization problems in aquaculture, reduce costs, and find sustainable solutions. These scenarios guided the evaluation of the future role of AI in aquaculture and the benefits that AI brings to the aquaculture sector in this study. AI is improving the way fish stocks are monitored, live stocks are assessed, diseases among plants and animals are recognized, water quality is tested, and production lines are controlled. Aquaculture uses AI technologies to help detect diseases early, monitor environmental conditions affecting production, and improve both efficiency and sustainability. In addition, AI is examining and providing solutions to environmental issues such as global warming and water resource management.
AtıfOpenAlex5
İncele - MakaleAçık Erişim2025Enhancing 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.