PM10, SO2 hava kirleticilerinin çoklu doğrusal regresyon ve yapay sinir ağları ile sezonsal tahmini

KOTAN, Burak; Erener, Arzu

doi:10.29128/geomatik.1158565

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Konuya ilişkin yapılan benzer çalışmalarda elde edilen sonuca göre yapay sinir ağlarının reel olana daha yakın sonuçlar ürettiği görülmektedir (Şenol ve Denizhan, 2023;Kotan ve Erener, 2023).…”

Section: Tartişma Ve Sonuçunclassified

Fındık Fiyatlarının Yapay Sinir Ağları ile Tahminlenmesi: Türkiye Örneği

Kara

2024

Batman Üniversitesi Yaşam Bilimleri Dergisi

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Fındık hiç kuşkusuz Türkiye için önemli bir tarım ürünüdür. Fındık fiyatlarının tahminlenmesi fiyatın belirlenmesi, ürünün pazarlanması vs. açıdan çok önemlidir. Fiyat tahminlemede bir çok yöntem kullanılmaktadır. Bu yöntemlerden birisi de yapay zekanın bir alt türü ola yapay sinir ağlarıdır. Bu çalışmada yapay sinir ağları ile fındık fiyatının tahminlenmesi yapılmıştır. Çalışmanın amacı fiyat tahminlenmesinde yapay sinir ağları kullanılarak bir model ortaya konulması ve uygulamasının gerçekleştirilmesidir. Alan yazındaki çalışmalardan farkı da bu yöntem ile fındık fiyatı tahminlenmesi üzerine uygulanmasıdır. Çalışmada, Giresun Ticaret Borsası’ndan elde edilen 2000-2023 yıllarına ait aylık fındık fiyatlarından oluşan 214 veriden oluşan zaman serisi kullanılmıştır. Çalışmanın analizi için MATLAB Neural Network Toolbox araç paketi kullanılmıştır. Çalışmada kullanılan veri seti tek değişkenli zaman serisi olduğu için Nonlinear Autoregressive (NAR) ile analiz gerçekleştirilmiştir. Çalışma neticesinde fındık fiyatlarının tahminine ilişki yapay sinir ağı modeli kurulmuş ve %33 hata payı ile tahminleme yapılmıştır.

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Section: Tartişma Ve Sonuçunclassified

Fındık Fiyatlarının Yapay Sinir Ağları ile Tahminlenmesi: Türkiye Örneği

Kara

2024

Batman Üniversitesi Yaşam Bilimleri Dergisi

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“…Although deterministic methods are a method that models physical and chemical processes such as discharge, accumulation, or transfer processes of pollutants using meteorological, emission, and chemistry models, the accuracy of these methods varies depending on the quality and scale of the data used and it takes a long time to apply these methods with big data. The importance of statistical methods, which compensate for the shortcomings and weaknesses of deterministic methods, is increasing in studies [17]. Statistical methods have also been used in the literature, especially in studies involving the PM10 pollutant.…”

Section: Introductionmentioning

confidence: 99%

Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation

Güler,

Kandemir

2024

Preprint

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Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes financial damage to the environment. In the case of air pollution, harmful and foreign substances in the air reach an amount and density above normal values. Air pollution also causes climate change, which affects global warming. Particulate matter is a harmful substance that harms the health of humans and other living things, causes air pollution, increases radiation, and negatively affects cloud formation. Particulate matter is extremely small solid particles and liquid droplets suspended in the air. PM10, which is less than 10 microns in diameter, is defined as small particles that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. Many criteria affect PM10 density and changes in density. The main criteria are vehicle traffic and fuel consumption, industrialization, coal, fuel oil and wood-burning heaters, greenhouse gas impact, and unregulated urbanization. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data between 2019 and 2023 obtained from the Air Monitoring Station in Kocaeli, Türkiye are used. After determining the criteria affecting PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking expert decision-maker opinions. The proposed decision-making model aims to guide obtaining the important causes of PM10 emission in terms of danger and focusing on these causes. Additionally, PM10 data is analyzed in the context of Covid-19 and a statistical analysis is made. One-way ANOVA is used to evaluate whether there is a significant difference in the average monthly data over the years. Games-Howell test, one of the post-hoc tests, is used for differences between groups (years). The study is important in that it provides a focus on the criteria affecting PM10 with an intuitive fuzzy perspective, along with statistical analysis.

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Prediction of elevation points using three different heuristic regression techniques

Demir

DOĞU

2024

Turkish Journal of Engineering

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The aim of this study is to estimate the elevation points used in the creation of the digital elevation model, which is the most important data of the projects and required in the engineering project, using horizontal and vertical location informations and three different heuristic regression techniques. As the study area, an area with mid-level elevations, located in the Marmara region, and covering a part of the intersection of Edirne, Kırklareli and Tekirdağ provinces was chosen. In the study, the estimations were investigated for three different sized areas, and these areas are square areas with the dimensions of 1x1 km, 10x10 km and 100x100 km, respectively. A total of 3500 elevation points were used in the study, and this number is constant in all areas, and 60% of these points were used in the testing phase and 40% in the training phase. The models used in the study are M5 model tree (M5-tree), multivariate adaptive regression curves (MARS) and Least Square Support Vector Regression (LSSVR). The results of the models were evaluated according to three different comparison criteria. These, coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used. When the modeling results are examined; M5-Tree regression method gave the best results (1), LSSVR method was better than MARS methods (2), The most successful input data was found in datasets using X and Y coordinates information, and the worst results were found in datasets using X coordinates (3). As the study area increased, the model performance did not improve (4). The least error was obtained in the modeling of 1x1 km area, and the highest R² was obtained from the modeling of 10x10 km area (5). It was concluded that the M5-tree method is a very successful method in height modeling.

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PM10, SO2 hava kirleticilerinin çoklu doğrusal regresyon ve yapay sinir ağları ile sezonsal tahmini

Cited by 3 publications

References 37 publications

Fındık Fiyatlarının Yapay Sinir Ağları ile Tahminlenmesi: Türkiye Örneği

Fındık Fiyatlarının Yapay Sinir Ağları ile Tahminlenmesi: Türkiye Örneği

Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation

Prediction of elevation points using three different heuristic regression techniques

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