With the recent growth of the Internet, the volume of data has also increased. In particular, the increase in the amount of unstructured data makes it difficult to manage data. Classification is also needed in order to be able to use the data for various purposes. Since it is difficult to manually classify the ever-increasing volume data for the purpose of various types of analysis and evaluation, automatic classification methods are needed. In addition, the performance of imbalanced and multi-class classification is a challenging task. As the number of classes increases, so does the number of decision boundaries a learning algorithm has to solve. Therefore, in this paper, an improvement model is proposed using WordNet lexical ontology and BERT to perform deeper learning on the features of text, thereby improving the classification effect of the model. It was observed that classification success increased when using WordNet 11 general lexicographer files based on synthesis sets, syntactic categories, and logical groupings. WordNet was used for feature dimension reduction. In experimental studies, word embedding methods were used without dimension reduction. Afterwards, Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms were employed to perform classification. These studies were then repeated with dimension reduction performed by WordNet. In addition to the machine learning model, experiments were also conducted with the pretrained BERT model with and without WordNet. The experimental results showed that, on an unstructured, seven-class, imbalanced dataset, the highest accuracy value of 93.77% was obtained when using our proposed model.

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Deterioration of natural resources such as vegetation, due to urbanization and increasing population density is evident in many areas of the world. For land use planning, it is vital to assess the plant density and forecast its future changes in light of vegetation-climate interactions given the current trend of global climate change. The purpose of this article is to show how we can detect the variation of vegetation density and forecast its future values with the Artificial Intelligence (AI) methods. As case studies, we selected 2 districts namely Alanya in Antalya province and Iznik in Bursa province of Turkey, that showed the highest and lowest land cover change between 2006 and 2018 respectively, according to the CORINE land cover classification. In the analysis, we have used satellite data (Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) data from MODIS/Terra satellite) and atmospheric data (archive precipitation and temperature measurements at meteorological stations) to define vegetation changes up to 2030.We have used ANN with original data and with the data obtained by the wavelet transform application (W-ANN). The average EVI value for 2030 was calculated as 0.22 with a 5.4% error probability for Iznik, and 0.28 with a 2% error probability for Alanya. By comparing the predicted values of W-ANN for 2030 with respect those of 2018, vegetation biomass density will decrease by 21.4% in Iznik, and 6.6% in Alanya. The results were also compared with the Landsat Normalized Difference Built-up Index (NDBI).