عنوان مقاله [English]
To estimation of heavy metals pollution in Gorganroud river sediments using data Mining, sediment was collected in two seasons (spring and summer) at 10 stations with three replications. After analyzing the samples, heavy metal data were extracted. Then proposed method was included the steps for starting and collecting data, pre-processing data, constructing the model as well as evaluation and output. construction of model was performed using 3 Naive bayes algorithms, decision tree, and k- nn, and then the evaluation was carried out to evaluate the accuracy, precision, recall, and error.In the output of the proposed method, all three algorithms have positive results for our data. The values of the accuracy, precision, recall, and error for Naive bayes algorithm were 92%, 44.49%, 88.88%, 8%, respectively; the values of the Naive bayes algorithm were greater than the decision tree k-nn algorithm. Also, the K-nn algorithm was better than the decision tree and the accuracy and accuracy of this algorithm were more than the decision tree algorithm. Thus, in this thesis, the Naive bayes algorithm showed better results with this data.