An Adaptive K-Nearest-Neighbor Approach for Predicting Chemical Composition Content in Soil

Land quality is evaluated according to the chemical composition content in soil. The geochemical evaluation of land quality can help users to determine how to use the land, e.g., dynamically manage land resources and adjust the pattern of farming. However, some chemical composition contents are missing in practice. It is necessary to predict the missing chemical composition content for the geochemical evaluation. This paper proposes an adaptive k-nearest-neighbor approach for predicting the chemical composition content in soil. The approach can adaptively determine the similarity between soil samples based on the characteristics of geological background, soil type, land use type and geographical position. According to the similarity, the proposed approach selects the k nearest neighbors of a sample and predicts the missing chemical composition content. The experimental results show that the proposed approach has better accuracy and stability than its competitors.