Intelligent Analysis Strategy for the Key Factor of Soil Nitrogen and Phosphorus Loss via Runoff under Simulated Karst Conditions

Abstract

Given the complex influence of various factors on soil nitrogen (N) and phosphorus (P) loss through runoff in a karst environment, analyzing the importance of different factors to determine the most efficient method for soil nutrient conservation remains a key challenge. Herein, we proposed a novel intelligent analysis strategy based on the Random Forest (RF) regression algorithm to identify the main features and discover the fundamental mechanisms among them under a rock-exposed karst slope with synchronous existence of surface runoff and subsurface leakage. Typically, the results indicated that the rock–soil angle (β) was the main factor influencing soil N and P loss, which was further confirmed based on the RF regression-multifactor analysis. The proposed strategy was used to characterize the relationships of inflow rate, soil bed–ground angle, and rock–soil angle with soil N and P concentrations in soil surface runoff, subsurface runoff, and fissure runoff to study the potential application of soil N and P loss under karst conditions. Our results provide a new approach and promising potential for soil nutrient conservation and related soil and plant research.