@article{ShabaniSamadianfardSattarietal.,
  author    = {Shabani, Sevda and Samadianfard, Saeed and Sattari, Mohammad Taghi and Mosavi, Amir and Shamshirband, Shahaboddin and Kmet, Tibor and V{\´a}rkonyi-K{\´o}czy, Annam{\´a}ria R.},
  title     = {Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis},
  series = {Atmosphere},
  volume    = {2020},
  journal   = {Atmosphere},
  number    = {Volume 11, Issue 1, 66},
  doi       = {10.3390/atmos11010066},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200110-40561},
  pages     = {17},
  abstract  = {Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{AbbaspourGilandehMolaeeSabzietal.,
  author    = {Abbaspour-Gilandeh, Yousef and Molaee, Amir and Sabzi, Sajad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir},
  title     = {A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars},
  series = {agronomy},
  volume    = {2020},
  journal   = {agronomy},
  number    = {Volume 10, Issue 1, 117},
  publisher = {MDPI},
  doi       = {10.3390/agronomy10010117},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200123-40695},
  pages     = {21},
  abstract  = {Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2\%, 87.7\%, and 83.1\%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100\% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}
@article{NabipourMosaviBaghbanetal.,
  author    = {Nabipour, Narjes and Mosavi, Amir and Baghban, Alireza and Shamshirband, Shahaboddin and Felde, Imre},
  title     = {Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions},
  series = {Processes},
  volume    = {2020},
  journal   = {Processes},
  number    = {Volume 8, Issue 1, 92},
  publisher = {MDPI},
  doi       = {10.3390/pr8010092},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200113-40624},
  pages     = {12},
  abstract  = {Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}