Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Utku, Anıl(2)
- Acar, Ramazan(1)
- Akyol, Sinem(1)
- Artuğer, Fırat(1)
- Barut, Abdulkadir(1)
- Can, Ümit(1)
- Celik, Erkan(1)
- Cifuentes-Faura, Javier(1)
- Das, Narasingha(1)
- Drobne, S.(1)
- Gul, Muhammet(1)
- Kamal, Mustafa(1)
- Kljajic Borstar, M.(1)
- Kouider, Bendine(1)
- Pekel, Engin(1)
- Polat, Alper(1)
- Sapliogu, Kemal(1)
- Utku, Anil(1)
- Zadnik Stirn, L.(1)
- Zerovnik, J.(1)
- Özkaynak, Fatih(1)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ2Data-Driven Prediction of Climate Variables in Agricultural Cities of India With Hybrid GA-TCN-LSTM Model
Utku, Anil
Accurate prediction of climate variables is important in reducing the effects of natural disasters and taking preventive measures for agriculture and food security, energy sector, public health, and water resources management. For agricultural production, it is essential for producers to determine strategies for their agricultural activities and to increase agricultural productivity. Issues such as determining planting and harvest times, determining pesticides to be used against agricultural pests, selecting products to be planted, irrigation, and sustainable agriculture are directly related to climate variables. In this study, a GA-TCN-LSTM hybrid prediction model was created to determine climate variables such as relative humidity, temperature, wind speed, and dew point in Bhopal, Indore, Kanpur, Ludhiana, and Patna, which are among the prominent cities of India in terms of agricultural production. The hyper-parameters of the developed model using the temporal convolutional networks (TCN) and long short-term memory (LSTM) were optimized with the genetic algorithm (GA), and the GA-TCN-LSTM hybrid model was created. GA-TCN-LSTM was extensively compared with the base TCN-LSTM, convolutional neural network (CNN), LSTM, TCN, and CNN-LSTM. The compared models were tested using approximately 15 years of hourly, up-to-date, and real-time data of the cities. Experiments showed that GA-TCN-LSTM outperformed the compared models and had above 0.9 R-Squared (R2) for the majority of cities and climate variables.
AtıfScopus2WoS1OpenAlex2
İncele - MakaleAçık Erişim2025Scopus SJRQ1Hybrid GA-ConvLSTM for data-driven prediction of climate variables: a case study of the most biodiverse cities in India
Utku, Anıl; Akyol, Sinem
Climate variables affect ecosystems’ structure, sustainability, and biodiversity. Monitoring and analysis of climate variables are essential for the management of ecosystems and the conservation of biodiversity. Temperature affects the distributions, habitats, and phonological events of living things in ecosystems. Humidity is essential for meeting plants’ water needs and the habitat’s quality and health. Dew point is essential for water quality, balance, plant health, and microclimate. Wind speed is essential for pollinating plants, microhabitats, and the health of living things. For such reasons, accurate prediction of climate variables using artificial intelligence methods can effectively predict the future of ecosystems and biodiversity and develop future strategic plans. This study aimed to predict the climate variables of temperature, humidity, dew point, and wind speed of Bhubaneshwar, Dehradun, Guwahati, Mysore, and Nagpur, among the cities with the highest biodiversity in India. It aimed to determine the long-term impacts of climate change on managing ecosystems and biodiversity protection. The hyperparameters of the ConvLSTM model, developed using CNN and LSTM, were optimized with a genetic algorithm, and the GA-ConvLSTM hybrid model was created. GA-ConvLSTM was tested with XGBoost, SVM, ANFIS, CNN, LSTM, and base ConvLSTM using approximately 15 years of hourly, up-to-date, and real-time climate variable data of cities. This work is one of the pioneering studies using GA to optimize deep learning hybrid models for multi-city climate prediction through extensive long-term high-resolution datasets. The experimental results reveal that GA-ConvLSTM outperforms conventional models, producing an R2 score exceeding 0.9 for each city across all climate variables. GA-ConvLSTM produced temperature predictions with 0.833 RMSE and 0.995 R2 for Bhubaneswar, humidity predictions with 3.461 RMSE and 0.990 R2, dew point predictions with 0.632 RMSE and 0.995 R2, and wind speed predictions with 2.278 RMSE and 0.993 R2. The experimental findings confirm the improved performance over conventional models due to SVM and XGBoost failing to achieve these R2 values and high RMSE scores. The research meets three principal obstacles because of the combination of stochastic climate datasets and complex nonlinear patterns, which also encounter limitations in conventional methods for managing scale-dependency relationships and manual parameter adjustments. This approach delivers three main strengths because it combines CNN and LSTM networks through genetic programming while tracking temporal and spatial relationships and processing noisy data automatically, leading to improved results compared to other methods. Unlike other published models in the field, the hybrid model GA-ConvLSTM combines optimized CNN and LSTM elements with genetic algorithm elements. The proposed model received its first application in this research for multi-city climate prediction through training with 15 years of hourly data. Standard methods encounter challenges when detecting long-term dependencies, but GA-ConvLSTM successfully addresses this issue.
AtıfScopus1OpenAlex1İncele - MakaleYalnız künye2025Scopus SJRQ2Using Meta-Heuristic Optimization Algorithms to Determine Baseflow and Comparing Their Temporal Performance
Acar, Ramazan; Sapliogu, Kemal
Accurate and simple determination of baseflow is an extremely important issue. In this study, the baseflow levels of Göksu station number 1805 in the Seyhan basin were determined by calibrating the Lyne and Hollick, Chapman, Chapman and Maxwell methods available in the literature using meta-heuristic optimization methods. The meta-heuristic algorithms used for calibration in the study were run thirty times each. Thus the reliability, the reliability of the algorithms was tested with the standard deviations obtained. The study also measured the temporal performance of the algorithms. In addition, the baseflows of Göksu station were determined and their percentage rates were found separately according to all three methods and intuitive methods, and the results obtained were compared. By examining the results obtained in this section, the average baseflow rate of the basin was also determined. Furthermore, each phase of the study was repeated over a five-year period, with the objective of measuring its sustainability. Consequently, it has been demonstrated that the method can be employed over extended periods.
AtıfOpenAlex1İncele - MakaleYalnız künye2023Scopus SJRQ1A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction
Utku, Anıl; Can, Ümit; Kamal, Mustafa; Das, Narasingha; Cifuentes-Faura, Javier; Barut, Abdulkadir
Beijing, Shanghai, Singapore, and London are regions with high population density and industrial activities. In this sense, accurate prediction of the rate of particulate matter 2.5 (PM_2.5), one of the most important air pollutants, is very important for creating a healthy ecological system. This study proposes a hybrid method to predict PM_2.5 with high success. This method aims to optimize the Long Short-Term Memory (LSTM) deep learning model, which successfully solves time series problems with a Genetic Algorithm (GA). Here, the hyperparameters of the LSTM model, such as the number of layers, number of neurons, number of epochs, and kernel size, are optimized to achieve the highest prediction rate. Against this backdrop, the present study uses for the first time a hybrid GA-LSTM model to predict PM_2.5 concentrations in Beijing, Shanghai, Singapore, and London. The Beijing dataset consists of 13 attributes, the Shanghai dataset consists of 10 attributes, the London dataset consists of 5 attributes, and the Singapore dataset consists of 7 attributes. In this study, input variables such as PM_2.5, DEWP, TEMP, PRESS, WS, WD, PRE, Snowfall hours, Rain hours, and Season were used. To assess the effectiveness of this hybrid method, the developed GA-LSTM model was compared with k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), LSTM, and Gated Recurrent Units (GRU). Experimental results showed that GA-LSTM outperformed the compared models. In addition, one of the best results was obtained compared to other models in the literature.
AtıfScopus17OpenAlex18İncele - MakaleAçık Erişim2022Scopus SJRQ1SBOX-CGA: substitution box generator based on chaos and genetic algorithm
Artuğer, Fırat; Özkaynak, Fatih
What makes artificial intelligence techniques so remarkable in the field of computer science is undoubtedly their success in producing effective solutions to difficult computational problems. In particular, metaheuristic optimization algorithms are a unique example of using artificial intelligence techniques to generate approximate solutions to problems that cannot be solved in polynomial time, called NP. Obtaining a substitution box (s-box) structure that will satisfy the desired requirements in cryptography is an example of these NP problems. In the literature, it is a hot topic to optimize the s-box structures obtained from chaotic entropy sources with heuristic algorithms to improve their cryptographic properties. The study with the highest nonlinearity value (110.25) based on optimization algorithms to date has been published in 2020. In this study, a method with a higher nonlinearity value than the algorithms previously proposed in the literature is developed. It has been shown that the nonlinearity value can be increased to 111.75. These results will be a basis for new research on the chaos-based s-box literature and will motivate new studies to develop alternative optimization algorithms in the future to obtain s-box structures based on the random selection equivalent to the AES s-box.
AtıfScopus47WoS35OpenAlex49İncele - MakaleYayıncıda açık erişim2020Optimal position of piezoelectric actuators for active vibration reduction of beams
Kouider, Bendine; Polat, Alper
This paper investigates the optimal placement of piezoelectric actuators for the active vibration attenuation of beams. The governing equation of the beam is achieved by coupled first order shear deformation theory with two node element. The velocity feedback controller is designed and used to calculate the feedback gain and then apply to the beam. In order to search for the optimal placement of the piezoelectric actuators, a new optimization criterion is considered based on the use of genetic algorithm to reduce the displacement output of the beam. The proposed optimization technique has been tested for two boundary conditions configurations; clamped -free and clamped-clamped beam. Numerical examples have been provided to analyze the effectiveness of the proposed technic.
AtıfScopus22WoS19OpenAlex21İncele - BildiriYalnız künye2017Forecasting daily patient visits in an emergency department by ga-ann hybrid approach
Pekel, Engin; Gul, Muhammet; Celik, Erkan; Drobne, S.; Zerovnik, J.; Zadnik Stirn, L.; Kljajic Borstar, M.
An Emergency Department (ED) plays a crucial role in the health system by providing acute care for patients who attend hospital without prior appointment. An accurate forecasting of patient visits in EDs contributes to health care decision makers to better allocate ED human resources and medical equipment. In this paper, a hybrid genetic algorithm–artificial neural network (GA–ANN) approach is developed. The forecasting performance of the hybrid approach is obtained using the real-world data set collected from a public hospital in Istanbul, Turkey. The hybrid GA–ANN model is shown to perform well in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying GA-ANN to model ED patient arrivals, and the results can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations.
AtıfScopus4İncele