KNN-Based Modeling and Its Application in Aftershock Prediction

Abstract

For the problem that the prediction accuracy of real-valued attribute data is not high, a modeling method named PR-KNN (Polynomial Regression and K Nearest Neighbor) is proposed, which is based on combination of KNN (K Nearest Neighbor) algorithm and Polynomial Regression model. Firstly, K nearest decision attribute values in training samples are selected by using KNN algorithm. Secondly, these K nearest decision attribute values are modeled by using Polynomial Regression method. And this method is applied to aftershock prediction. Experimental data are the sequence data of aftershocks with magnitude greater than or equal to 4.0 from Wenchuan earthquake. Comparing with traditional KNN regression algorithm and Distance-Weighted KNN regression algorithm, experimental results show that the maximum relative error predicted by PR-KNN reduces by 6.012% and 7.751% respectively, and maximum absolute error reduces by 0.367 and 0.473 respectively.