multiple linear regression · Konu · Gümüşhane Üniversitesi

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MakaleAçık Erişim2023Scopus SJRQ1/r/gumushane/12786
Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey
Yener, Ismet; Guvendi, Engin
The dominant height of forest stands (SDH) is an essential indicator of site productivity in operational forest management. It refers to the capacity of a particular site to support stand growth. Sites with taller dominant trees are typically more productive and may be more suitable for certain management practices. The present study investigated the relationship between the dominant height of oriental beech stands and numerous environmental variables, including physiographic, climatic, and edaphic attributes. We developed models and generated maps of SDH using multilinear regression (MLR) and regression tree (RT) techniques based on environmental variables. With this aim, the total height, diameter at breast height, and age of sample trees were measured on 222 sample plots. Additionally, topsoil samples (0–20 cm) were collected from each plot to analyze the physical and chemical soil properties. The statistical results showed that latitude, elevation, mean annual maximum temperature, and several soil attributes (i.e., bulk density, field capacity, organic carbon, and pH) were significantly correlated with the SDH. The RT model outperformed the MLR model, explaining 57% of the variation in the SDH with an RMSE of 2.37 m. The maps generated by both models clearly indicated an increasing trend in the SDH from north to south, suggesting that elevation above sea level is a driving factor shaping forest canopy height. The assessments, models, and maps provided by this study can be used by forest planners and land managers, as there is no reliable data on site productivity in the studied region.
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MakaleAçık Erişim2023Scopus SJRQ1/r/gumushane/12631
Modelling some stand parameters using Landsat 8 OLI and Sentinel-2 satellite images by machine learning techniques: a case study in Türkiye
Bulut, Sinan; Günlü, Alkan; Çakır, Günay
Remote sensing technologies have been extensively used in forest management in predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 satellite images to estimate stand volume, basal area, number of trees, mean diameter, and top height. 180 temporary sample plots were taken in pure Crimean pine stands with varied structure. Reflectance, vegetation indices, and eight texture values were generated from Landsat 8 and Sentinel-2 satellite images. The stand parameters were modelled with the remotely sensed data using multiple linear regression, support vector machine, and deep learning techniques. The results showed that the support vector machine technique provided the highest level of model performance with 45° orientation for number of trees (R2 = 0.98, RMSE%=5.97) and 90° orientation for basal area (R2=0.91, RMSE%=15.22). The results indicated that the texture values presented better results than the reflectance and the vegetation indices in estimating the stand parameters.
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MakaleYalnız künye2018Scopus SJRQ3/r/gumushane/15214
Prediction of the antimicrobial activity of walnut (Juglans regia L.) kernel aqueous extracts using artificial neural network and multiple linear regression
Kavuncuoglu, Hatice; Kavuncuoglu, Erhan; Karatas, Seyda Merve; Benli, Büsra; Sagdic, Osman; Yalcin, Hasan
The mathematical model was established to determine the diameter of inhibition zone of the walnut extract on the twelve bacterial species. Type of extraction, concentration, and pathogens were taken as input variables. Two models were used with the aim of designing this system. One of them was developed with artificial neural networks (ANN), and the other was formed with multiple linear regression (MLR). Four common training algorithms were used. Levenberg–Marquardt (LM), Bayesian regulation (BR), scaled conjugate gradient (SCG) and resilient back propagation (RP) were investigated, and the algorithms were compared. Root mean squared error and correlation coefficient were evaluated as performance criteria. When these criteria were analyzed, ANN showed high prediction performance, while MLR showed low prediction performance. As a result, it is seen that when the different input values are provided to the system developed with ANN, the most accurate inhibition zone (IZ) estimates were obtained. The results of this study could offer new perspectives, particularly in the field of microbiology, because these could be applied to other type of extraction, concentrations, and pathogens, without resorting to experiments.
AtıfScopus39WoS33OpenAlex46
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MakaleAçık Erişim2015Scopus SJRQ3/r/gumushane/13210
Prediction of pedal cyclists and pedestrian fatalities from total monthly accidents and registered private car numbers
Ghasemlou, Kiarash; Aydin, Metin Mutlu; Yildirim, Mehmet Sinan
Accident prevention is relatively a complex issue considering the effectiveness of the injury prevention technologies as well as more detailed assessment of the complex interactions between the road condition, vehicle and human factor. For many years, highway agencies and vehicle manufacturers showed great efforts to reduce the injuries resulting from the vehicle crashes. Many researchers used a broad range of methods to evaluate the impact of several factors on traffic accidents and injuries. Recent developments lead up to capable for determining the effects of these factors. According to World Health Organization (WHO), cyclists and pedestrians comprise respectively 1.6% and 16.3% in traffic crash fatalities in 2013. Also in Turkey crash fatalities for pedestrian and cyclists are respectively 20.6% and 3% according to Turkish Statistical Institute data in 2013. The relationship between cycling and pedestrian rates and injury rates over time is also unknown. This paper aims to predict the crash severity with the traffic injury data of the Konya City in Turkey by implementing the Artificial Neural Networks (ANN), Regression Trees (RT) and Multiple Linear Regression modelling (MLRM) method.
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MakaleYalnız künye2012Scopus SJRQ2/r/gumushane/16201
Comparison of Multiple Linear Regression and Artificial Neural Network Models Goodness of Fit to Lactation Milk Yields
Takma, Cigdem; Atil, Hulya; Aksakal, Vecihi
In this study, effects of lactation length, calving year and service period on lactation milk yield of Holstein Friesians were modeled with multiple regression and artificial neural networks (ANN) and compared goodness of fit of models. Analyses were carried on five lactations milk yields of 305 Holsteins calved at 2006, 2007 and 2008 years. After several experiments, hidden layer number was taken one and hidden nodes number were found three for the chosen architecture. Moreover, convergence criteria, maximum iteration number and epoch number were taken as 1.10(-6), 50 and 20, respectively. Adjusted coefficient of determination (R-2),root mean square error (RMSE), mean absolute deviation (MAD), mean absolute percentage error performance criteria (MAPE) were used for comparison of artificial neural network and multiple linear regression models goodness of fit. After analysis R-2 values were found among 0.62-0.85 for the five lactations with neural networks model. RMSE, MAD and MAPE criteria also were found among 480.9-1682.8, 325.2-1381.7 and 6.1-20.2, respectively. These criteria were found for R-2, RMSE, MAD and MAPE among 0.30-0.75, 1964.8-30008.7, 1576.6-2458.3 and 24.7-35.6, respectively for multiple linear regression. When the models were compared, artificial neural networks model gave better fit than multiple linear regression models. Consequently, artificial neural networks was determined an alternative method to multiple regression analysis.
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