Predictive approaches play an important role in detecting events, controlling risks and reducing maintenance and repair costs.The purpose is to provide a model for predicting critical and prioritized risks based on data 521 mining algorithms.Data mining method was planned based on the CRISP methodology.Data modeling has been done in two parts: "descriptive" and "predictive" data mining and the use of "clustering" and "classification" algorithms.
"Sillhouette index" is considered for clustering and the K-Means, Kohnen, Two Step algorithm is used; the best value is based on the K-Means algorithm.Silhouette is equal to 0.6446 with the number of clusters 5.Next, Neural Network Algorithms, C.
5 tree, Nearest Neighbor and Support Vector have been used for classification.These techniques recognizing data classification patterns and their integration increases the amount of data learning.The results showed learning in 97.56% Drive Belt of the agreed data and the accuracy and validity of the combined model for data classification was estimated at 92.
86%.Based on the results, 13 critical risks have been identified; "release of polluting gases and chemicals" and "lack of training and justification of contractors regarding the pipeline" have the highest and lowest priority, respectively.