Spatial Data Prediction Model Integrated with K-Nearest Neighbor Mechanism in Neural Networks

Traditional methods of spatial interpolation, such as inverse distance weighting (IDW) and ordinary Kriging (OK), utilize geographic distance and specific assumptions to simplify the computation of geospatial data complexity. Nevertheless, these conventional approaches are not as practical in obtaining high-precision estimation because of the intricate nonlinear relationship between geographic distance and correlation weights. In this study, a novel spatial interpolation technique, named WFNNKM, is introduced, which integrates the K-nearest neighbor (KNN) mechanism with a neural network to address this challenge. Firstly, the Lebesgue integral is used for clustering, and KNN tuples are obtained by clustering. Secondly, the KNN training task is constructed for interpolation points, and the bias parameters of each point are obtained through training. Finally, the pre-training parameters of a neural network are modified through bias parameters of nearest neighbors to obtain accurate prediction attribute values. In comparison with two conventional methods and three neural network approaches across three soil sample datasets, the results demonstrate a notably superior performance of the suggested approach compared to the five interpolation methods.