Pipeline defect prediction method based on Random Forests algorithm

In this paper, the research status quo of pipeline defect prediction model and Random Forests model at home and abroad was investigated, and their advantages and problems were analyzed. Then, based on the data of some pipeline section monitored and recorded by GIS system of Shaanxi-Beijing Gas Pipeline, the Random Forests model was established and optimized, and the defect level of the pipeline section was predicted. It is shown that the Random Forests model has the function of index importance evaluation and it can be used to analyze the contribution degree of each index to the pipeline defect. This model is characterized by high evaluation accuracy, accurate ranking result and strong data mining capacity. By combining the Random Forests model with the GIS technology, pipeline defects can be predicted better and the control measures can be taken correspondingly.