EVALUATION OF ADVANCED DATA MINING ALGORITHMS IN LAND USE/LAND COVER MAPPING
Keywords: Land use Land cover, LULC, machine learning, data mining, image classification
Abstract. For environmental monitoring, land-cover mapping, and urban planning, remote sensing is an effective method. In this paper, firstly, for land use land cover mapping, Landsat 8 OLI image classification based on six advanced mathematical algorithms of machine learning including Random Forest, Decision Table, DTNB, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge) and Simple Logistic is used. Then, results are compared in the terms of Overall Accuracy (OA), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for land use land cover (LULC) mapping. Based on the training and test datasets, Simple Logistic had the best performance in terms of OA, MAE and RMSE values of 99.9293, 0.0006 and 0.016 for training dataset and values of 99.9467, 0.0005 and 0.0153 for the test dataset.